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GPU Knowledge, AI servers & Bare Metal Insights

The Real AI Play: Infrastructure&nbsp; The next big opportunity in AI isn’t another model; it’s the supercomputers fueling them. While most headlines fixate on software-level breakthroughs, superior returns exist in the backbone of those innovations: GPU infrastructure.&nbsp;&nbsp; In the Substack below, Kai Golden, Managing Director of Hydra Capital, breaks down why bare metal GPU clusters are emerging as a powerful alternative asset class. With 30%+ IRRs, fast paybacks, and the depreciation benefits of direct ownership, they offer a compelling response to the rising costs and opaque pricing of public cloud providers.&nbsp; Hydra Host is making this opportunity more accessible than ever. Our <a href="https://hydrahost.com/financing/" rel="noopener noreferrer" target="_blank">financing solutions</a> and <a href="https://hydrahost.com/calculator/" rel="noopener noreferrer" target="_blank">GPU calculator</a> are designed to help investors and operators underwrite and scale infrastructure with speed and confidence. If you're serious about supporting the AI boom and capturing yield through high-performing hardware, this is where the real play begins.&nbsp; <a href="https://kaigolden.substack.com/p/how-to-invest-in-gpus" rel="noopener noreferrer" target="_blank">Read the full analysis</a>&nbsp;&nbsp;

PU infrastructure powering AI data centers with trading-style dashboards – The Real AI Play: Infrastructure by Hydra Host

Discover why bare metal GPU clusters are emerging as a top-performing asset class in the AI era. Hydra Host's Director of Capital breaks down ROI, utilization, and infrastructure economics for long-term investors.

The Real AI Play: Infrastructure 

Lenovo x Hydra Host: Delivering the Future of AI Infrastructure When AI infrastructure needs to move from idea to reality, the right procurement partner makes all the difference. Hydra Host teamed up with Lenovo to deploy one of the first production-ready NVIDIA H200 clusters in the world. The full story is now available in <a href="https://www.lenovo.com/us/en/case-studies-customer-success-stories/hydra-host/" rel="noopener noreferrer" target="_blank">Lenovo's official case study.</a> This project was about more than just speed. It was about getting the details right. Hydra’s procurement team collaborated closely with Lenovo to source and deliver high-performance ThinkSystem SR680a V3 servers under strict deadlines. This successful execution highlights how Hydra helps customers navigate a complex supply chain and secure top-tier AI hardware quickly and confidently. Beyond the hardware, this deployment is a cornerstone of <a href="https://hydrahost.com/ai-factory/" rel="noopener noreferrer" target="_blank">Hydra Host’s AI Factory Accelerator</a>, a full-stack program designed to help teams build sovereign AI infrastructure from the ground up. Lenovo’s modular systems are a key part of this effort, providing the performance and scalability needed for real-world AI production environments. Hydra Host’s strength lies in its ability to act as <a href="https://hydrahost.com/procurement/" rel="noopener noreferrer" target="_blank">a true procurement partner, managing everything from sourcing and shipping to deployment and monetization</a>. Together with partners like Lenovo, Hydra is redefining what’s possible for companies to build the next generation of AI factories.

Hydra Host partnered with Lenovo to deliver a production-ready H200 cluster, powering sovereign AI infrastructure through expert GPU procurement.

Lenovo x Hydra Host: Delivering the Future of AI Infrastructure

FOR IMMEDIATE RELEASE Hydra Host Powers World’s First Sovereign Deployment of NVIDIA B300 Chips in El SalvadorAI sovereignty starts with infrastructure—Hydra delivers it. San Salvador, El Salvador – June 26, 2025&nbsp;– In a historic move set to redefine the future of AI,&nbsp;Hydra Host&nbsp;has provisioned the world’s&nbsp;first sovereign acquisition of NVIDIA B300 chips&nbsp;for&nbsp;El Salvador’s National Artificial Intelligence Laboratory, marking a bold leap toward full-stack technological independence. Following a meeting between President Nayib Bukele and Hydra Host CEO &amp; Co-founder&nbsp;Aaron Ginn, El Salvador secured access to the most advanced AI processors on the market, enabling the country to train, run, and secure AI models on infrastructure it fully controls. “This isn’t just a chip order, it’s a sovereignty play,” said Ginn. “Hydra exists to give governments and enterprises control over their compute future.” Hydra Host, an&nbsp;<a href="https://hydrahost.com/post/hydra-host-named-nvidia-dgx-cloud-partner/" rel="noopener noreferrer" target="_blank">NVIDIA Cloud Partner (NCP)</a>&nbsp;focused entirely on&nbsp;bare metal deployments, is building the infrastructure behind El Salvador’s Sovereign AI—a national strategy for strategic resilience, economic competitiveness, and digital independence. This marks a new chapter in Hydra’s mission to empower sovereign AI capabilities. The company has deployed GPU infrastructure in over&nbsp;40&nbsp;global data centers, launched turnkey&nbsp;<a href="https://hydrahost.com/ai-factory/" rel="noopener noreferrer" target="_blank">AI Factories</a>&nbsp;for enterprises and nation-states, and is now expanding its presence in Central America by hiring its&nbsp;first full-time team member in El Salvador. Hydra will also play a hands-on role in&nbsp;talent development, as Aaron Ginn leads workshops with students in the country’s&nbsp;<a href="https://x.com/cubo_ai" rel="noopener noreferrer" target="_blank">Cubo AI program,</a>&nbsp;preparing a new generation of AI engineers equipped to build, scale, and govern their own models locally. This announcement follows El Salvador’s broader push for technological sovereignty through its&nbsp;National Artificial Intelligence Agency (ANIA), with partners including Google AI, NVIDIA, ARK Invest, and Nike. By placing B300 chips directly into the hands of a nation, not a hyperscaler, Hydra Host is setting the gold standard for sovereign AI infrastructure. In a world where compute is power,&nbsp;true sovereignty starts with bare metal. Media ContactAndrea HoltMarketing Manager, Hydra Host<a href="mailto:andrea.holt@hydrahost.com" rel="noopener noreferrer" target="_blank">andrea.holt@hydrahost.com</a>

President Bukele and Aaron Ginn announce B300 deployment for El Salvador’s National AI Lab.

Hydra Host powers El Salvador’s historic deployment of NVIDIA B300 chips, delivering the world’s first sovereign AI cluster.

Hydra Host Powers World’s First Sovereign Deployment of NVIDIA B300 Chips in El Salvador

Announcing Hydra as an NVIDIA Cloud Partner supporting global AI infrastructure and AI Factory deployments.

<h1>FOR IMMEDIATE RELEASE:</h1> <h1>Hydra Host Joins NVIDIA DGX Cloud Lepton, Expanding Access to High Performance AI Infrastructure</h1> June 11, 2025 | Miami, Florida - Hydra Host, a global AI infrastructure provider specializing in bare metal GPU deployment and cluster monetization, has been named an NVIDIA Cloud Partner (NCP) and is contributing compute resources to <a href="https://nvidianews.nvidia.com/news/nvidia-dgx-cloud-lepton-connects-europes-developers-to-global-nvidia-compute-ecosystem" rel="noopener noreferrer" target="_blank">DGX Cloud Lepton</a>, an AI platform and marketplace connecting developers to a global network of GPU compute. As one of the NCPs contributing to DGX Cloud Lepton , Hydra is uniquely positioned to help organizations build scalable AI factories - high-performance compute environments purpose built for large model training, inference, and monetization. Hydra currently manages over 20,000 bare metal GPUs across 40+ global data center partners, giving customers flexible deployment options across the U.S., Europe, and APAC. This milestone aligns with <a href="https://hydrahost.com/ai-factory/" rel="noopener noreferrer" target="_blank">Hydra’s AI Factory Accelerator</a>, a full-stack program that supports customers building sovereign AI factories for procurement and financing to deployment, compliance, and monetization. “Our collaboration with NVIDIA reflects a shared belief in the future of AI factories - sovereign, high-performance, and fully monetizable,” said Aaron Ginn, CEO of Hydra Host. “DGX Cloud Lepton is a strategic resource for AI developers, and we’re proud to deliver compute resources through a platform that enables customers to innovate and scale rapidly." As AI demand increases globally, organizations need infrastructure that combines performance, ownership, and cost efficiency. With DGX Cloud Lepton and the AI Factory Accelerator, Hydra enables customers to turn high-performance GPU clusters into sustainable infrastructure assets. For more information, visit <a href="https://www.hydrahost.com/" rel="noopener noreferrer" target="_blank">hydrahost.com</a> or explore the <a href="https://www.nvidia.com/en-us/data-center/dgx-cloud-lepton/" rel="noopener noreferrer" target="_blank">DGX Cloud Lepton initiative</a> at <a href="https://www.nvidia.com/en-us/data-center/dgx-cloud-lepton/" rel="noopener noreferrer" target="_blank">nvidia.com</a>. You can also apply for DGX Cloud Lepton early access here.

Hydra Host joins NVIDIA DGX Cloud Partner, accelerating AI Factory deployments with bare metal GPU infrastructure worldwide.

Hydra Host Joins NVIDIA DGX Cloud Lepton, Expanding Access to High Performance AI Infrastructure

<h2>Introduction to Artificial Neural Networks in the AI Factory</h2> Artificial neural networks (ANNs) are the foundational machinery behind modern AI development—functioning as the engines that drive the AI factory. As AI factories evolve to operate at industrial scale, ANNs have become essential for automating tasks like perception, decision-making, prediction, and language generation. When paired with GPU-accelerated infrastructure, they enable organizations to train, refine, and deploy AI models with unprecedented speed and efficiency. Hydra Host supports this pipeline by delivering the scalable multi-GPU compute power needed to fuel every phase of neural network development.<h2> </h2><h2>What is an Artificial Neural Network?</h2> An artificial neural network is a layered computing system inspired by the human brain. It uses interconnected nodes—called neurons—that process data in stages to identify patterns, classify inputs, and generate outputs. In an AI factory setting, ANNs are the core components that convert raw data into learned intelligence, enabling everything from speech recognition to fraud detection.<h2> </h2><h2>How Neural Networks Mirror the Brain—and Power AI Pipelines</h2> Each neuron in an ANN functions similarly to a biological neuron, applying weights and thresholds to input data and passing it along if activated. As data flows through multiple layers, the network adjusts these parameters using algorithms like backpropagation to optimize accuracy. Within the AI factory, this process forms the "model training" station—where raw training data is transformed into intelligent output through iterative learning cycles.<h2> </h2><h2>Inside the ANN Assembly Line: Layers and Nodes</h2> Neural networks consist of: <ul><li>Input layers that receive data</li><li>Hidden layers that transform and abstract features</li><li>Output layers that produce predictions or classifications</li></ul> Each layer in the factory pipeline adds complexity and refinement, shaping models that can perform tasks like image segmentation or natural language processing. Multi-layer architectures are central to deep learning, a subset of AI that excels at unstructured data. <h2>Training Neural Networks in the AI Factory</h2> Neural networks are trained using large labeled datasets. This supervised learning process teaches models to adjust weights and thresholds to minimize error, making them increasingly accurate over time. High-quality training requires intensive compute power—especially for large-scale models. Hydra Host enables this by offering access to GPU-rich environments optimized for deep learning workloads. <h2>GPU Acceleration and Industrial-Scale Model Training</h2> In modern AI factories, GPU acceleration is essential. Unlike CPUs, GPUs are optimized for parallel computation, making them ideal for training massive neural networks efficiently. Hydra Host provides access to top-tier GPUs such as the NVIDIA L40S, A100, and H100, supported by scalable infrastructure that allows AI teams to train, tune, and deploy at industrial scale. <h2>Specialized Neural Network Architectures for AI Factories</h2> <ul><li>Convolutional Neural Networks (CNNs): Used for image processing tasks</li><li>Recurrent Neural Networks (RNNs): Ideal for time-series and sequence modeling</li><li>Transformer Networks: Core to modern LLMs and AI-driven language generation</li></ul> Hydra Host supports all of these use cases with flexible, high-throughput infrastructure tailored to the demands of advanced ANN models. <h2>Technical Elements That Power ANN Training</h2> Key processes in neural network training include: <ul><li>Activation Functions (e.g., Sigmoid, ReLU): Enable non-linear learning</li><li>Cost Functions (e.g., Mean Squared Error): Measure model accuracy</li><li>Gradient Descent and Back-propagation: Optimize model parameters</li></ul> <h2>Neural Networks and the AI Factory Lifecycle</h2> The AI factory lifecycle includes: <ol><li>Data ingestion and preprocessing</li><li>Model training (with ANNs)</li><li>Hyperparameter tuning</li><li>Validation and testing</li><li>Deployment and inference at scale</li></ol> ANNs are central to steps 2 through 5. Hydra Host helps accelerate this lifecycle by providing bare metal and virtualized GPU environments that scale as your needs evolve—from prototyping to deployment. <h2>Hydra Host: The Compute Backbone of Neural Network Infrastructure</h2> At Hydra Host, we don’t just provide servers—we power the AI factory. Whether you're training vision models with CNNs, fine-tuning LLMs with Transformers, or deploying real-time inference applications, our infrastructure is built to scale with your ambition. Our high-density GPU servers, fast storage, and hybrid cloud capabilities ensure that neural network training is never limited by compute. <h2>Conclusion: Why ANNs Need Scalable Infrastructure</h2> Artificial neural networks represent the intelligent core of the AI factory. To train, deploy, and scale them effectively, organizations need robust, GPU-accelerated infrastructure. Hydra Host provides that foundation—delivering the flexibility, performance, and scalability that AI teams require to turn data into intelligence and intelligence into results.

Learn how artificial neural networks power the AI factory and how Hydra Host supports scalable, GPU-accelerated neural network training.

Artificial Neural Networks in the AI Factory

<h1>Introduction to AI Factories</h1> AI factories represent the next evolution in artificial intelligence development. These digital infrastructures operate similarly to traditional manufacturing plants—but instead of physical goods, they produce and refine AI models and intelligent services. Their role in modern data ecosystems is to streamline how AI is trained, optimized, and deployed. At a time when scalable, automated intelligence is essential for innovation, AI factories have become a foundation for enterprises looking to embed AI deeply into their workflows. <h1>What Is an AI Factory?</h1> An AI factory is a purpose-built software and hardware infrastructure designed to generate, improve, and deliver AI systems at scale. It combines high-performance computing—especially GPU clusters—with software workflows that support machine learning pipelines. Like a physical factory, it runs on continuous data input, processing pipelines, and iterative refinement, resulting in deployable intelligence. To meet these requirements, an AI factory must be supported by robust networking, extensive storage capacity, and low-latency computation. It facilitates tasks such as training large language models, optimizing inference runtimes, and deploying real-time AI across hybrid or multicloud environments. <h1>Why AI Factories Matter in the Era of Scalable AI</h1> AI factories have shifted the paradigm from ad hoc model training to industrial-grade AI production. As models grow in complexity and data volumes explode, companies can no longer afford manual or disjointed AI workflows. AI factories enable rapid iteration, repeatability, and real-time model updates—critical advantages for use cases like autonomous driving, language generation, fraud detection, and personalized recommendations. For companies that operate across markets and continents, AI factories make it possible to scale intelligence consistently while maintaining governance, compliance, and performance standards. <h1>Core Components of a Modern AI Factory</h1> At the heart of an AI factory are seven key components—each one optimized to manage different phases of the AI model lifecycle. Together, they enable the ingestion of data, model training, fine-tuning, and eventual deployment in production environments. <ul><li>Training: This is the central task in any AI factory. Raw datasets are used to teach models patterns and relationships. GPUs or specialized accelerators are required to handle the parallel compute demands.</li><li>Fine-Tuning: Once a base model is trained, fine-tuning helps adapt it to specific business domains or use cases. This process allows for personalization and domain-specific intelligence.</li><li>Inference: Inference is where models generate real-time output from user prompts or data inputs. The inference engine must be optimized for latency, throughput, and efficiency, particularly in customer-facing applications.</li><li>Retrieval-Augmented Generation (RAG): This technique enhances language models by retrieving relevant context from external knowledge bases before generating responses, making outputs more grounded and accurate.</li><li>Corpus Management: Managing datasets—cleaning, storing, labeling, and organizing them—is critical. Without a strong data pipeline, model performance and generalization are severely limited.</li><li>External Service Integration: AI factories often connect to APIs, telemetry systems, and human feedback loops. This interconnectivity ensures AI models evolve based on external signals and live usage patterns.</li><li>Development and Experimentation: Before models go live, they must be rigorously tested in sandbox environments. An experimentation layer supports A/B testing, regression analysis, and reproducibility of results.</li></ul> <h1>Specialized Infrastructure: GPUs, DPUs &amp; High-Performance Hardware</h1> AI factories are only as strong as their underlying compute. GPU clusters—like those available from providers such as Hydra Host—are essential for training and deploying large-scale models. Data Processing Units (DPUs) offload and accelerate networking and security tasks, freeing up GPUs for core workloads. These specialized hardware resources support parallel processing, memory pooling, and low-latency communication. They are foundational for AI factories aiming to support large language models (LLMs), reinforcement learning systems, or multimodal AI that spans text, image, and speech. Hydra Host offers access to multi-GPU instances—including NVIDIA H100, A100, and L40S GPUs—allowing AI builders to scale training and inference while maintaining high availability across hybrid infrastructure. <h1>Deployment Models: From AI-SaaS to Edge Hosting</h1> AI factories can be deployed in different ways, depending on business needs, data sovereignty, and performance targets: <ul><li>AI-SaaS: Turnkey services provided by vendors. Ideal for small teams or rapid prototyping.</li><li>Cloud-hosted: Hosted in a public or private cloud, offering scalability and integration flexibility.</li><li>Self-hosted: For full control, especially in regulated industries where compliance and customization are paramount.</li><li>Edge-hosted: AI runs closer to where data is generated (e.g., IoT devices or remote sites), reducing latency and bandwidth requirements.</li></ul> Choosing the right deployment model impacts everything from model responsiveness to infrastructure cost and data privacy. <h1>AI Workflow Optimization: Building for Speed and Scale</h1> Optimizing an AI factory is not just about hardware—it’s also about designing workflows that minimize bottlenecks. Techniques include automating data labeling pipelines, adopting containerized model orchestration (e.g., via Kubernetes), and implementing reference architectures that standardize performance baselines. Experimentation platforms support agile development cycles, letting teams evaluate model variants in parallel. Real-time observability ensures that failures can be debugged quickly, and performance metrics can guide continuous improvements. <h1>Real-World Examples of AI Factories</h1> AI factories are already in use by some of the world’s most advanced organizations: <ul><li>Google uses its AI factory to support billions of daily ad auctions with optimized real-time bidding.</li><li>Uber leverages AI factories to dynamically adjust ride availability, routing, and pricing.</li><li>Amazon updates product pricing and recommendation engines through automated pipelines powered by AI factories.</li><li>Walmart runs AI-driven cleaning robots and operational analytics using data pipelines and model deployments that function like a digital factory.</li></ul> These examples show how AI factories drive automation, personalization, and competitive advantage. <h1>The Role of Hydra Host in the AI Factory Ecosystem</h1> Hydra Host provides the compute infrastructure needed to power enterprise AI factories. Their bare metal GPU hosting offers dedicated access to NVIDIA H100, A100, and RTX 4090 cards—allowing AI teams to scale securely, without the unpredictability of shared cloud environments. By partnering with Hydra Host, companies can deploy their AI factories with greater performance consistency, predictable costs, and full hardware control—critical for continuous training, fine-tuning, and inference at scale. <h1>Future Outlook: AI Factories as the Backbone of Intelligence</h1> AI factories are no longer a luxury—they are becoming a necessity for any business building AI-driven products. As demand for AI models grows, the need for industrialized AI development workflows and optimized compute environments will only increase. Emerging trends—such as multi-agent systems, real-time LLM fine-tuning, and edge AI deployment—will further push the boundaries of what AI factories can do. Businesses investing now in infrastructure, experimentation, and workflow standardization will be best positioned to lead in this AI-first economy.

Discover how AI factories streamline model creation and deployment, and how GPU infrastructure powers them at scale.

What Is an AI Factory? Infrastructure for Scalable AI Production

Hydra Host, a leader in high-performance compute infrastructure, has officially launched the industry’s first interruptible GPU instances for bare metal deployments, delivering flexible, cost-efficient compute options tailored for AI and machine learning teams operating at scale.&nbsp;&nbsp;Unlike traditional interruptible instances limited to virtualized cloud environments, Hydra’s solution offers the same raw performance and control of bare metal, with the added benefit of up to 50% cost savings for workloads that can tolerate preemption.&nbsp;&nbsp;“We built interruptible bare metal to achieve maximum utilization for our <a href="https://hydrahost.com/ai-factory/" rel="noopener noreferrer" target="_blank">AI Factory</a> partners, while giving AI Platforms more options and flexibility in available plans and pricing,” said Ariel Deschapell, CTO of Hydra Host. “This is the first time interruptible logic has been applied to the GPU server layer itself, not just abstracted through cloud virtualization.”&nbsp;&nbsp;Hydra Interruptible is ideal for teams running:&nbsp;<ul><li>Cost-sensitive inference workloads&nbsp;</li><li>Kubernetes container orchestration or serverless&nbsp;</li><li>Distributed training runs&nbsp;</li><li>Benchmarking or burst experimentation&nbsp;</li><li>Low urgency but price sensitive compute workloads, like batch or Bitcoin seed phrase recovery&nbsp;</li></ul>&nbsp;This feature adds to Hydra’s growing suite of infrastructure tools aimed at modernizing GPU deployment for AI builders and operators. With global deployments across 40+ partner data centers and over 20,000 GPUs under management, Hydra is building the foundation for the next generation of AI infrastructure—flexible, distributed, and designed for <a href="https://hydrahost.com/brokkr-for-data-centers/" rel="noopener noreferrer" target="_blank">monetization</a>.&nbsp;&nbsp;Hydra customers interested in being one of the first to access interruptible reservations can contact <a href="mailto:sales@hydrahost.com" rel="noopener noreferrer" target="_blank">sales@hydrahost.com</a>.&nbsp;

Hydra Host launches the first interruptible bare metal GPU instances, offering cost-efficient compute for AI workloads, inference, and short-term training jobs. 

Hydra Host Launches First Interruptible Bare Metal GPU Servers, in Beta for select partners 

The Cloud’s TSMC Moment: Why The Largest Neoclouds Won't Own Hardware&nbsp; In 1987, the founding of Taiwan Semiconductor Manufacturing Company (TSMC) began a fundamental shift in the semiconductor industry by<a href="https://www.tsmc.com/english/aboutTSMC/dc_infographics_foundry#:~:text=computers%2C%20automotive%20electronics%2C%20the%20Internet,and%20have%20significantly%20improved%20our" rel="noopener noreferrer" target="_blank"> pioneering a “pure-play” foundry model</a>. Prior to this, industry heavyweights like Intel, Texas Instruments, and NEC Semiconductor were vertically integrated device manufacturers (IDMs), handling both integrated circuit (IC) design and manufacturing. TSMC changed that by specializing exclusively in manufacturing, enabling a new generation of fabless startups like NVIDIA to outsource this capability and specialize solely in IC design.&nbsp; The decoupling of the semiconductor business has had enormously disruptive consequences. Today, NVIDIA is the largest semiconductor firm by market capitalization and stands at 30x the capitalization of Intel. TSMC itself is 7x the size of Intel, and has become one of the most valuable and critical companies in the world, responsible for supplying virtually all of the cutting edge chips for giants such as NVIDIA and Apple.&nbsp; This specialization has facilitated enormous progress on both sides of the industry, which has directly led to the advancements which have facilitated the current AI boom. Now because of that same boom, similar economics are facilitating another decoupling and disruption further up the value chain.&nbsp;Today, cloud computing is experiencing the same critical threshold of complexity and increasingly divergent capital requirements that drove the decoupling of IC design and manufacturing in the late 80’s and 90’s. In the future most clouds, and eventually even the largest, won’t own any hardware at all.&nbsp; Understanding the Decoupling: Moore’s First and Second Laws&nbsp; Most are familiar with Moore’s Law: the number of transistors on integrated circuits doubles roughly every two years. However, Moore’s second law, less widely known but equally important, states that the cost of building state-of-the-art semiconductor foundries doubles approximately every four years.&nbsp;&nbsp;This exponential growth in capital requirements, and the parallel growth of design sophistication, naturally set the stage for decoupling and specialization. Companies found it increasingly difficult to master both ever more sophisticated chip design and the extraordinary complexity needed to manufacture them at scale cost effectively.&nbsp; Today, an almost identical dynamic is emerging in cloud computing. Cloud giants like AWS, Google Cloud, and Azure are vertically integrated, managing both massive server infrastructure and developing sophisticated software stacks on top of their own hardware. These big three are all doubling down on this strategy and attempting to take it even a step further by also designing their own in-house AI chips (<a href="https://www.wired.com/2016/05/google-tpu-custom-chips/#:~:text=This%20morning%20at%20Google%20I%2FO%2C,transforming%20the%20Google%20search%20engine" rel="noopener noreferrer" target="_blank">such as TPUs</a>) to further verticalize their stack.&nbsp; The first generation of AI-centric clouds (or “neoclouds”), like Lambda Labs, roughly follow this same vertically integrated playbook by building their own hardware capacity while pursuing their own abstracted software platforms in tandem. But because of increasingly similar economic pressures, this model stands to be disrupted, and efforts to vertically integrate even further stand to have massive blow back. The first to realize and execute on this effectively will be the ultimate winners just as TSMC and NVIDIA are today.&nbsp; The Emergence of “Pure-Play” AI Factories&nbsp; The accelerating sophistication and cost of GPU-powered data centers (referred to as<a href="https://blogs.nvidia.com/blog/dell-technologies-world-2024/#:~:text=Huang%20discussed%20the%20concept%20of,valuable%20data%20tokens%20distributed%20globally" rel="noopener noreferrer" target="_blank"> AI Factories by Jensen</a>) parallel the early history of semiconductor manufacturing facilities. Take the A100 GPU from NVIDIA which had an initial starting price of $20,000. Roughly 5 years later, the price of the soon to be released B200 GPU is close to $40,000. The power draw, meanwhile, has roughly quadrupled from 250W to 1000W, accelerating challenges in supporting infrastructure and operations. This rate of growing capex and complexity closely mirrors the dynamics of foundries in Moore’s second law.&nbsp;This isn’t the only pressure driving decoupling. As the cost and complexity of HPC infrastructure and AI Factories continue to escalate, so too does the pace of change in the world of software applications and tooling.&nbsp; As AI-driven software development accelerates, software iteration cycles will shorten dramatically, demanding frequent updates, adaptations, and specialized features to support its continued improvement and growth. If the rate of increasing costs and complexity of AI Factories seems to parallel that of foundries, then the speed of software iteration cycles seem poised to mirror the rapid progress of integrated circuit capacity in Moore’s first law.&nbsp; Though this is innately harder to measure, given recent advancements it doesn’t seem outrageous to suggest that software iteration speeds are on pace to roughly double every two years. Compare the state of the art in early 2025 to early 2023: Tools such as Cursor and Replit are now able to leverage current frontier models to reduce the barrier of entry for software development to a level never seen before. They enable the completion and deployment of working app POCs with a handful of prompts, while also turbocharging the abilities of experienced software developers. An<a href="https://github.blog/news-insights/research/the-economic-impact-of-the-ai-powered-developer-lifecycle-and-lessons-from-github-copilot/#:~:text=quantitative%20research%20study%20that%20developers,for%20productivity%E2%80%99s%20sake%20means%20nothing%E2%80%94we" rel="noopener noreferrer" target="_blank"> analysis by Github in May 2024</a> showed a 50% increase in developer productivity from using Copilot. These tools and capabilities are the worst they will ever be today.&nbsp; For the software side of the cloud business, this means demand for tooling and infrastructure to launch, manage, and produce this software will only continue to accelerate just as their own ability to iterate quickly on their own platforms accelerate as well. If they don’t, then their competition surely will instead.&nbsp; Just as AMD eventually<a href="https://arstechnica.com/gadgets/2008/10/and-then-there-was-one-amd-spins-off-foundry/" rel="noopener noreferrer" target="_blank"> spun out their foundry business</a> to go fabless despite fierce initial resistance ("<a href="https://clausaasholm.substack.com/p/real-men-have-fabs" rel="noopener noreferrer" target="_blank">real men have fabs</a>"), cloud service providers will begin to realize that AI hardware investment and cloud software development require fundamentally different expertise, investment profiles, and intense areas of focus.&nbsp; Unlocking a New Generation of Neoclouds&nbsp; Future neoclouds will be forced to specialize exclusively in software and user experience to beat and stay ahead of the competition, by offloading hardware investment and management to dedicated AI Factory providers. These AI Factories will function similarly to TSMC, by taking on the burdens of intense capex and reducing costs by sheer economies of scale. They will provide access to state of the art infrastructure through flexible bare metal APIs, freeing neoclouds to invest their resources entirely into differentiated user experiences and software innovation at higher levels of abstraction up the cloud stack.&nbsp; Such AI Factory businesses will face early challenges similar to those TSMC encountered, which lacked large obvious early customers as the major semiconductor firms already possessed in house foundries. But this didn’t particularly concern founder and CEO Morris Chang. Chang had previously served as an executive at Texas Instruments, where he worked with numerous engineers who dreamed of one day launching their own chip companies. Raising the capital to do this was extremely prohibitive however, with the cost of building an in house foundry quickly escalating into the hundreds of millions.&nbsp; By creating an independent foundry that they could outsource to, Chang reduced the barrier of entry substantially, enabling a generation of fabless chip startups to enter the market without prohibitive infrastructure investment needs. In a rare interview on the Acquired podcast, Morris Chang recollected the first time a then nearly bankrupt NVIDIA had reached out to him in the 90s: “I had always told our sales people we should never be negligent in talking to future customers, even if the customer seemed to be a very small one”.&nbsp; This approach paid off. NVIDIA, Qualcomm, Broadcom, Mediatek, P.A. Semi (acquired by Apple to form their in house chip division) and countless others flourished and reshaped the industry precisely because they were able to outsource to TSMC and other foundries adopting the pure-play foundry model.&nbsp; Reducing Barriers to Entry&nbsp; The same economic headwinds are now pushing upstream full force for cloud platforms. The capital and expertise required to operate GPU clusters and AI Factories are quickly becoming prohibitive. In parallel, the demands for the software side of the cloud business are only beginning their dramatic acceleration as well.&nbsp; Neoclouds will have to keep up with the demand for ever better and more expansive tooling as the barriers for producing software continue to decrease. Those that are able to focus purely on software and customer-facing innovation are poised to be far more successful, given they no longer need to shell out capital, time, and focus on an underlying hardware business.&nbsp; The emergence of dedicated AI Factories that can enable this will dramatically lower barriers to entry just as TSMC did, spawning new specialized cloud companies, each innovating faster and more creatively than today’s vertically integrated providers. Among them, several companies will inevitably scale into formidable players, reshaping the competitive landscape and potentially disrupting established hyperscalers just as Intel is nearly an afterthought today.&nbsp; TSMC's choice to specialize transformed the semiconductor industry and, with it, the world. The cloud industry’s TSMC moment is now happening as we speak, as a direct consequence of the previous specialization in the semiconductor industry. Vast AI, Runpod, Salad, and SF Compute are just a handful of startups attempting to build software only cloud businesses on top of bare metal providers, while my company Hydra Host is taking the approach of enabling independent data-centers to transform into specialized AI Factories with our turnkey bare metal solution and bundled design, procurement, and financing services.&nbsp; The decoupling is underway. The only questions are how quickly will it play out, and will the hyperscalers be able to adapt? Or will they become the new generation of IDMs like Intel and NEC Semiconductor before them?

TSMC's choice to specialize transformed the semiconductor industry and, consequently, the world. The cloud industry's TSMC moment is occurring now, driven by semiconductor specialization. Companies like Vast AI, Runpod, Salad, SF Compute, and Hydra Host, which transforms independent data centers into specialized AI Factories, exemplify this shift. 

The Cloud’s TSMC Moment: Why Neoclouds Won't Own Hardware

Hydra Host recently closed its latest fundraising round with top VCs and operators in the AI and data centers space. The round, led by Sun Microsystems co-founder Scott McNealy’s <a href="https://flume.ventures/" rel="noopener noreferrer" target="_blank">Flume Ventures</a>, included participation from <a href="http://nea.com/" rel="noopener noreferrer" target="_blank">NEA</a> and Max Altman’s <a href="https://www.sagavc.com/" rel="noopener noreferrer" target="_blank">Saga Ventures</a>, as well as Hydra Host’s customers Chase Lochmiller (co-founder and CEO of <a href="https://crusoe.ai/" rel="noopener noreferrer" target="_blank">Crusoe</a>) and Youssef El Manssouri (co-founder and CEO of <a href="https://www.sesterce.com/" rel="noopener noreferrer" target="_blank">Sesterce</a>).&nbsp;&nbsp; According to Scott McNealy:&nbsp;&nbsp;&nbsp; <blockquote>"Hydra Host abstracts away the pain of running a data center with a much overdue end-to-end solution, delivering chips, financing, monetization, and underlying technical infrastructure across one consolidated platform. Speaking to any data center, one gets a sense that Hydra Host is inevitable."</blockquote> Hydra Host is growing fast. Hydra Host grew its compute brokerage business 9x in 2024 and achieved 3,706 total nodes and 29,648 GPUs under management. Across its cloud distribution and hardware procurement business lines, Hydra Host grew total GMV 331x and revenue 18x. Hydra Host nearly doubled its headcount from 13 to 23 over this period to support this growth.&nbsp;&nbsp; Hydra Host looks forward to operating alongside the pro-data center, pro-AI innovation Trump administration to make America’s data centers more secure, agile, and affordable. Hydra Host’s founding team co-founded the Foundation for American Innovation and bears deep policy expertise in the space, poising the firm to excel in a rapidly accelerating data center environment. Co-founder Aaron Ginn’s national security thought leadership was recently published in <a href="https://www.foxnews.com/opinion/its-time-build-americas-cyber-nuke-we-desperately-need-deterrent-stop-wars-before-start" rel="noopener noreferrer" target="_blank">Fox Opinion</a> regarding the hardware required to guarantee America’s continued industrial leadership and national security.&nbsp;&nbsp; What are customers saying?&nbsp;&nbsp;&nbsp;“Hydra Host has been our secret weapon in the world of GPU scaling, offering an incredible partnership and industry best solutions to scale quickly through an intuitive global marketplace of compute.”&nbsp;&nbsp; -Marc Seal, CEO of Sortium&nbsp;&nbsp; “As a hands-on operator passionate about tinkering and pushing the boundaries of CPU and GPU performance, Brokkr has allowed me to focus on engineering design, IP creation, sales and consultancy. Brokkr is a game-changer for any data center operator looking to simplify operations while maximizing return on effort and time efficiency.”&nbsp;&nbsp;-James Walsh, Managing Director of Sytronix

Hydra Host recently closed its latest fundraising round with top VCs and operators in the AI and data centers space. The round, led by Sun Microsystems co-founder Scott McNealy’s Flume Ventures, included participation from NEA and Max Altman’s Saga Ventures, as well as Hydra Host’s customers Chase Lochmiller (co-founder and CEO of Crusoe) and Youssef El Manssouri (co-founder and CEO of Sesterce). 

Hydra Host Raises Seed Plus led by Flume Ventures

Recent chip export restrictions designed to deny advanced hardware to the Chinese Communist Party have swept up several other countries in the process. Most notable are the Gulf States of the Middle East- such as the UAE, Saudi Arabia, and Kuwait.With the rapid rise in popularity of Deepseek (almost certainly trained on restricted H100s), these export rules are facing renewed scrutiny. Enforcing direct prohibitions on sales to China is simple, but preventing subsequent resale through other jurisdictions is far more complex. To address this, the heavy-handed approach used by the previous Biden administration severely limited exports to many partner and neutral countries- a policy that remains leaky and ultimately harms U.S. interests. To understand why requires asking a simple question. Do we want the rest of the world to buy American or Chinese? Our restrictive export measures effectively subsidize the Chinese chip industry by knocking American competition out of key markets and pushing neutral or friendly nations deeper into China’s technological and economic orbit. It’s a case of cutting off our nose to spite our face.Fortunately, the choice is not between accepting the current policy or abandoning all restrictions on critical hardware altogether. We can apply better technological solutions to enable effective export controls without inflicting so much collateral damage—and we must do so swiftly. <h2>The Gulf States: Key to the Compute Race</h2> No place illustrates this policy dilemma more clearly than the Arabian Gulf. Historically renowned for its strategic value- from ancient trade routes to its modern status as the nucleus of global oil production- the Gulf region is on the verge of becoming a central player in AI infrastructure. Countries like Saudi Arabia, the UAE, Kuwait, Oman, and Qatar have invested heavily in power generation and internet infrastructure. As a result, they not only surpass the baseline for AI development but could become the most consequential partners worldwide in the emerging “compute race.” Like the transition from early gasoline engines to mass-market automobiles where every technological leap fueled even higher demand for oil- each new AI breakthrough (à la Deepseek) will require exponentially more compute power and thus more energy to run advanced workloads at scale. All this means that raw energy output is quickly becoming the limiting factor for developing high-end compute. The United States has already tapped most “low-hanging fruit” sites for data centers, forcing some hyperscalers like AWS and Azure to even invest in <a href="https://www.datacenterdynamics.com/en/opinions/hyperscaler-nuclear-power-ambitions-heat-up/" rel="noopener noreferrer" target="_blank">restarting nuclear plants.</a> In this context, the Gulf States’ energy capacity stands out dramatically. As of 2023, Saudi Arabia, the UAE, and Kuwait collectively produce around 16 million barrels of oil per day- nearly 17% of global output. Their proven reserves are even higher with plenty of headroom to ramp up production. In addition to fossil fuels, these nations are pouring resources into renewable energy projects such as solar further expanding their net energy surplus. As a whole, the Gulf region is the largest net exporter of energy in the world, making them ideal partners for large-scale AI endeavors that demand vast, stable power supplies.In the near term, it will become increasingly apparent the Gulf region will rapidly become the most consequential provider of AI infrastructure capacity just as it is the most important producer of oil today. The only question is with whom will it partner with to develop this capacity: American businesses or the rapidly growing Chinese tech sector. Currently, the U.S. leads in advanced chip capabilities, but if we continue to withhold those capabilities from a region with such vast potential, China will not hesitate to seize the initiative to boost its own capabilities. <h2>A Technology Problem</h2> So how can we enable key international partners effective access to American chips and hardware, while ensuring they do not ultimately make their way to China once delivered? Simple. By bundling deployments of restricted hardware with tailored DC management software like Hydra Host's Brokkr platform, we can ensure that delivered GPUs are actually installed and remain on site in the intended destination country. The Brokkr platform provides a live source of truth for all these deployments to OEMs and other stakeholders, providing ongoing and automated compliance monitoring. If GPUs are disconnected from the authorized data center site in which the software is installed, it triggers an immediate alert providing the assurance necessary to relax export concerns. By implementing smarter policies and leveraging targeted technology to allow Gulf States to purchase American hardware without the risk of resale leaks, we can strengthen U.S. influence, bolster domestic chipmakers, and maintain our technological edge—all while countering China’s growing presence in a region critical to the future of AI. This approach doesn’t mean abandoning security or ignoring export controls; it means using more nuanced tools to deny our most advanced technology to adversaries without undermining partnerships and ceding these markets to China.

The Middle East is set to become a global AI hub, but US chip restrictions could push it toward China. Learn how smarter policies & Brokkr tech can secure US interests.

Advancing American Interests with GPU Exports to the Middle East

Hydra Host Powers the Future of Decentralized Compute&nbsp;Hydra Host provides miners the diverse GPUs needed to dominate their subnets alongside best-in-class support and quality assurance.&nbsp;&nbsp;Decentralized Physical Infrastructure Networks (DPINs) use cryptocurrency mining mechanics to connect compute buyers with networks of compute contributors.&nbsp;&nbsp; Compute buyers like generative AI services, GPU marketplaces, and even medical research companies create <a href="https://taostats.io/subnets" rel="noopener noreferrer" target="_blank">subnets</a> where compute providers or “miners” do computational work or provide infrastructure in exchange for tokens and incentives.&nbsp; These subnets typically charge per seat registration and non-linearly rank and reward based on factors like compute contributed, calculations completed, and other measures of performance.&nbsp;&nbsp; DPIN whales source their compute through Hydra Host.&nbsp;Bare metal compute is the natural solution for DPIN. Using Hydra Host to source cheap, readily available compute from independent data centers around the world allows DPIN whales to quickly scale up and down their resources, craft contracts on their terms, and preserve margin that would be lost to the unnecessary layers of abstraction typical of large cloud providers and virtualization layers.&nbsp;When a miner needed a jaw-dropping quantity of 4090 Threadrippers in a European colo, they came to Hydra Host. They sent an 11PM text and had a utilization-ready quote five minutes later. In DPIN, access is nothing without reliability. The economics of subnets penalize poor performance and downtime while exponentially rewarding performance and scale. This means that DPIN miners cannot risk downtime and need rapid, white glove service at a moment’s notice. This is why Hydra Host provides miners a round-the-clock team of engineers and puts its dollars behind its compute.&nbsp; When a miner was de-registered due to a data center’s internal server error, Hydra Host deployed its team to handle re-registration and credited all re-registration costs.&nbsp;&nbsp; Hydra Host has the GPUs you need, where you need them, and for however long you need them. <a href="mailto:sales@hydrahost.com" rel="noopener noreferrer" target="_blank">Just let us know how many.</a>&nbsp;&nbsp;

Bare metal compute is the natural solution for DPIN. Using Hydra Host to source cheap, readily available compute from independent data centers around the world allows DPIN whales to quickly scale up and down their resources, craft contracts on their terms, and preserve margin that would be lost to the unnecessary layers of abstraction typical of large cloud providers and virtualization layers. 

This is why, when a miner needed a jaw-dropping quantity of 4090 Threadrippers in a European colo, they came to Hydra Host. They sent an 11PM text. Five minutes later, they had a quote ready for immediate utilization. 

Hydra Host Powers the Future of Decentralized Compute 

In the 1950s, McDonald’s revolutionized the fast-food industry, expanding from a single restaurant to over 200 locations in just five years. This kind of growth was unprecedented, and the secret lay in Ray Kroc’s now ubiquitous innovation: franchising. Kroc created a system to empower capable entrepreneurs with their own capital to scale what worked and piggyback on the parent company's brand credibility, expertise, resources, and distribution. The franchise model enabled rapid expansion while maintaining standardized operations, procurement, and delivery. Across industries ranging from dining to automotive care to shipping and logistics, the franchise model has since devoured many smaller competitors who simply lacked the scale and flexibility to compete. Today, a similar transformation is playing out in real time for AI factories—high-performance computing (HPC) data centers optimized for artificial intelligence workloads. These GPU-focused facilities, the backbone of AI development, face challenges that echo those of early McDonald’s: high capital needs, ever-changing operational complexity, and scalability bottlenecks. Here's how franchising can offer the same breakthrough for AI factories as it has in many other industries. <h3>The Challenge of Monetizing AI Factories</h3>Building an AI factory is no small feat. These data centers require significant capital outlay for real estate, infrastructure, and high-performance servers. And that's only the beginning. Once the facility is operational, the real challenge emerges: distribution and monetization.For most data center operators, setting up hardware is the easy part. Creating and deploying an entire cloud platform so customers can actually utilize that hardware, and acquiring those customers, present an entirely new set of challenges that's wholly alien to the traditional data center industry. As a result, even well-funded data centers often struggle to attract sustained demand or stand out in an increasingly crowded market. A recent example highlights this reality: data center Equinix acquired Packet, a promising bare metal cloud company, for $335 million in 2020. Despite high expectations, Equinix recently announced the sun-setting of the renamed Equinix Metal Cloud offering to refocus on its core colocation business.The Packet acquisition and subsequent scale up investment by Equinix failed, resulting in a massive write-off. The example of Equinix Metal underscores the complexities of running a cloud business alongside a traditional data center business. Each has vastly different operational and customer needs, and even a company with Equinix's vast resources couldn't effectively bridge the gap.This presents a critical issue. The global AI ecosystem benefits from decentralization, where a diverse range of independent data centers can ensure competition, innovation, and resilience. However, without a scalable and replicable growth model, the industry risks GPU power becoming concentrated in the hands of a few dominant players, stifling competition and creating chokepoints for innovation. That is of course, much like the traditional cloud. <h3>Franchising as a Solution</h3>Franchising offers entrepreneurs the opportunity to be at an industry's cutting edge day one. Just as McDonald’s created a template for entrepreneurs to replicate its operations, AI factory franchising will provide a standardized blueprint for data center expansion and operations using near-turnkey software and ongoing full stack support. Through a franchise model, individual operators and investors can deploy their expertise and resources in developing their own AI Factory projects, while benefiting from shared procurement operations, financing relationships, software infrastructure, distribution channels, and of course an existing customer base. Contrast this with today's non-standardized approach, wherein aspiring data center operators must reinvent the wheel and spend precious time and resources rediscovering existing best practices to finance, procure, and monetize their chips. Here's how Hydra Host is pioneering data center franchising:<ol><li>Faster, Cheaper Infra Procurement: Small, independent data centers often struggle to acquire the latest GPU models as quickly as their large cloud company competitors. When they do get a quote, they often lack the leverage needed to access fair pricing. Hydra takes care of pricing leverage, providing small data centers the benefits of scale instantly. Through existing relationships with companies like NVIDIA and various infrastructure and OEM partners, Hydra Host aggregate orders and passes those benefits directly to our DC partners.</li><li>Easy-to-Use, Standardized Infrastructure: Hydra Host's Brokkr platform features a bare metal management and provisioning system, providing a source of truth for server management and status, as well as a way to monetize bare metal servers in a secure and automated way. Bare metal skips the need for a hypervisor or containerized abstraction layer, reducing complexity and increasing stability and performance, making it a premium choice for AI model and platform companies. It also means servers on Brokkr are compatible and can be pushed to third party marketplaces, such as Runpod and <a href="http://salad.com/" rel="noopener noreferrer" target="_blank">salad.com</a>, to supplement Hydra driven demand when needed.</li><li>Centralized Distribution: Creating a competitive platform is one of the biggest hurdles independent data centers face. Brokkr takes care of this and acts as a unified user interface and API for all of our DC partners, delivering end customers a reliable, centralized source of supply with a predictable and standardized purchasing experience.</li></ol> Hydra Host solves provisioning for its clients, handling utilization and passing along the benefits of a its standard and ecosystem so data centers only have to concern themselves with on-premise day to day operations and maintenance. <ol><li>Outsourced Customer Acquisition and Sales: Just as McDonald’s centralized its branding and marketing, Hydra is constantly sourcing paying customers and funneling clients to data centers integrated with Brokkr. Individually, independent operators can't compete with industry giants long term. But as part of the Brokkr umbrella and ecosystem, data centers can combine to quickly scale to more locations and provide more diverse offerings and configurations than any centralized cloud provider can hope to match.</li><li>Financing: Hydra Host exploits its visibility into compute and hardware pricing, as well as its procurement channels, to provide its clients advantageous and rapid access to equipment financing. Hydra's finance partners can underwrite with greater precision, confidence, and speed than any other financiers in the space. This means Hydra's data center clients enjoy better and faster financing than they could access anywhere else.</li></ol> <h3>Why It Matters</h3>Franchising AI factories wouldn’t just enable faster growth; it could democratize the entire AI infrastructure market. A distributed network of AI factories, each independently owned but operating under a standardized system, could help ensure that GPU power isn’t concentrated in a handful of companies or geographies, and enable much more experimentation and innovation. Franchising allows investors and operators to scale what works instead of reinventing the wheel. Data centers are overdue for a turnkey, just-add-capital approach allowing them to focus on scaling instead of fighting for relationships and rediscovering what their competitors already discovered years before. <h3>The Road Ahead</h3>Franchising isn’t a silver bullet, and there are significant challenges to overcome. The balance between standardization and local flexibility can be tricky to manage. However, the potential benefits are significant. Franchising has historically demonstrated its power to improve the odds of success in competitive industries. For example, while only ~20% of independent restaurants survive beyond five years, top franchises like McDonald’s boast survival rates of over 90% in the same timeframe. AI factories will follow a similar dynamic. Standalone data centers often face steep challenges, including building platforms, acquiring customers, and navigating competitive pressures. A franchising model for AI factories delivers the same advantages franchises provide in other industries: a proven playbook, operational support, and the strength of a unified brand. By empowering local entrepreneurs with these tools, franchised AI factories unlock a path to greater success, ensuring more resilient and distributed AI infrastructure worldwide.

Franchising AI factories wouldn’t just enable faster growth; it could democratize the entire AI infrastructure market. A distributed network of AI factories, each independently owned but operating under a standardized system, could help ensure that GPU power isn’t concentrated in a handful of companies or geographies, and enable much more experimentation and innovation. 

AI Factories In A Box: Why Franchising is the Future of Data Centers

Running a data center is a relentless, round-the-clock commitment, as James Walsh, founder of Sytronix and CTO of Daylight Compute, knows all too well. For two grueling years, James worked seven days a week, overseeing every detail of Daylight Compute's data center operations. Whether it was provisioning GPUs or troubleshooting technical hiccups, his dedication kept customers happy but left no time for himself.&nbsp;Then came Brokkr by Hydra Host a game-changer that transformed not just his workflow but his entire approach to managing server rentals.&nbsp;&nbsp;The Struggle: Complexity at the Cost of Balance&nbsp;Managing a bare metal data center is no small feat. While platforms like Vast, Fluidstack, and Cudo Compute offered partial solutions, none fully addressed the intricate needs of Daylight Compute. Every server required hands-on management, keeping James perpetually tethered to data center operations.&nbsp;This constant demand on his time not only drained his energy but also stole his focus from what he truly needed to send time on, the research and development of server technology.&nbsp;&nbsp;The Turning Point: Discovering Brokkr&nbsp;Determined to regain control of his time, James began searching for a smarter, more streamlined solution. Enter Brokkr: a comprehensive platform designed to optimize GPU resources, automate server management, and connect data center operators with a global customer base.&nbsp;Brokkr wasn’t just another marketplace it was the system James had been actively searching for.&nbsp;&nbsp;The Solution: Simplicity Meets Sophistication&nbsp;With Brokkr, James turned Daylight Compute into a finely tuned operation. The platform delivered:&nbsp;<ul><li>Automated Efficiency: Features like auto-reboot and reprovisioning minimized downtime and manual interventions.&nbsp;</li><li>Seamless Monetization: Brokkr linked James’s GPUs to a global network, maximizing utilization and revenue.&nbsp;</li><li>Centralized Control: A single dashboard gave James the power to manage everything from anywhere.&nbsp;</li><li>Scalable Stability: Brokkr’s infrastructure adapted to customer needs, enabling growth without overextension.&nbsp;</li></ul>&nbsp;The Results: Freedom to Innovate and Thrive&nbsp;For the first time in over 2 years, James could step away from the grind and even take a vacation. "With Brokkr, you don’t work yourself to death," he shared.&nbsp;Freed from the constant demands of data center management, James redirected his energy to his passion for engineering high-performance servers. Sytronix, now specializing in CPU and GPU servers for AI, CGI, and HPC, has become a leader in the industry.&nbsp;James’s expertise in tuning AMD EPYC CPUs and NVIDIA GPUs like the RTX 4090 has shattered CGI rendering benchmark records. His precision engineering delivers unprecedented speed and efficiency, making <a href="https://www.sytronix.co.uk/" rel="noopener noreferrer" target="_blank">Sytronix</a> the go-to for record-breaking server performance.&nbsp;&nbsp;The Brokkr Advantage: A Platform Built for Success&nbsp;<a href="https://docs.hydrahost.com/docs/brokkr-overview" rel="noopener noreferrer" target="_blank">Brokkr</a> isn’t just a tool it’s a transformative ecosystem for data center operators. Key features include:&nbsp;<ul><li>Reliability Through Automation: Auto-reboots and reprovisioning minimize human error and keep systems running smoothly.&nbsp;</li><li>Scalability Without Boundaries: A global network ensures compliance, low-latency solutions, and seamless expansion.&nbsp;</li><li>Effortless Monetization: GPU resources are easily listed and connected to customers worldwide, maximizing earnings.&nbsp;</li><li>Enhanced Security: Robust encryption and access controls safeguard operations.&nbsp;</li><li>Peak Efficiency: Dynamic dashboards optimize resource allocation and energy usage, aligning with sustainability goals.&nbsp;</li></ul>&nbsp;A New Era for Data Center Management&nbsp;James Walsh’s journey with Brokkr is proof of what’s possible when innovation meets execution. By removing the burdens of day-to-day management, Brokkr empowered James to focus on growth, creativity, and setting new performance standards in the data center industry.&nbsp;Are you ready to optimize your operations and reclaim your time? Learn more about Brokkr and how it can transform your data center. Visit <a href="https://hydrahost.com/datacenters/" rel="noopener noreferrer" target="_blank">Hydra Host</a> or reach out to <a href="mailto:sales@hydrahost.com" rel="noopener noreferrer" target="_blank">sales@hydrahost.com</a> and start your journey today.&nbsp;&nbsp;With Brokkr, James Walsh didn’t just optimize his business he revolutionized it. Now, it’s your turn.&nbsp;&nbsp; &nbsp;

Running a data center is a relentless, round-the-clock commitment, as James Walsh, founder of Sytronix and CTO of Daylight Compute, knows all too well. For two grueling years, James worked seven days a week, overseeing every detail of Daylight Compute's data center operations. Whether it was provisioning GPUs or troubleshooting technical hiccups, his dedication kept customers happy but left no time for himself. 

From Overworked to Optimized: How James Walsh Revolutionized Server Rentals with Brokkr

<h3>Interconnect Technologies: An Overview</h3> I'll start by laying out the technology used in AI, HPC and other server workloads to give you an understanding of how these technologies are used and later in this blog I will breakdown how this tech affects your bottom line. Explaining why old tech should not be used and why some new tech can be risky if you haven't enough knowledge to operate these technologies. <h4>1. PCIe (Peripheral Component Interconnect Express)</h4>PCIe is the most widely used interconnect for CPUs, GPUs, and peripherals in servers. Generational improvements in PCIe versions 3.0, 4.0, and 5.0 have brought higher bandwidth and lower latency:<ul><li>PCIe 3.0: Suitable for none demanding workloads which do not require high performance. Release date 2010 very dated.</li><li>PCIe 4.0: Addressing mid-level performance requirements. Release date 2017 still in wide use but now become dated technology.</li><li>PCIe 5.0: Enables better communication for high-performance systems like AI/ML training or HPC. Release date 2019, now in wide spread use with modern up to date servers.</li></ul> PCIe 6 standards have already been set in 2022, but are not in widespread use yet and supported by very few devices - no CPU's currently sold by Intel or AMD to the open market. The same is also true of PCIe 7, which won't be released until 2025 and so far to date I have never seen or heard of a PCIe 7 device in the field. Most next gen GPU's and network adapters will support PCIe 5. <h4>2. NVidia\Mellanox Interconnects Networks and Fabrics.</h4>InfiniBand is a high-speed networking interconnect designed for low-latency, high-throughput communication across clustered systems. With speeds up to 800 Gbps (HDR)&nbsp;and direct RDMA support, it is widely used in HPC and enterprise environments. Features include:<ul><li>Scalability: Connects thousands of nodes in a supercomputing environment.</li><li>GPUDirect RDMA: Facilitates GPU-to-GPU communication across nodes without CPU intervention.</li><li>Seamless CPU-GPU Communication: Enables large-scale hybrid compute clusters.</li><li>NVLink and NVSwitch </li></ul> Developed by NVIDIA, NVLink&nbsp;is a GPU-to-GPU interconnect that provides ultra-fast communication with bandwidths of up to 1800 GB/s&nbsp;in its latest version. It creates unified memory pools for AI/ML workloads and is essential in systems like NVIDIA DGX for:<ul><li>Single-Node Performance: Enhances GPU communication within one server.</li><li>Large GPU Clusters: Uses NVSwitch&nbsp;to connect multiple GPUs in a full-mesh topology for large-scale AI/ML or HPC tasks.</li><li><a href="https://www.nvidia.com/en-gb/data-center/nvlink/" rel="noopener noreferrer" target="_blank">https://www.nvidia.com/en-gb/data-center/nvlink/</a></li></ul> <h4>4. XGMI (AMD Infinity Fabric)</h4>XGMI is AMD’s proprietary interconnect for efficient CPU-to-GPU and GPU-to-GPU communication. Features include:<ul><li>Tight CPU-GPU Integration: Leverages AMD EPYC CPUs with Instinct GPUs for AI and HPC workloads.</li><li>GPU to GPU communication: 896GB\s Bidirectional</li><li>Scalable Clusters: Facilitates distributed workloads using Infinity Fabric.</li><li><a href="https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/data-sheets/amd-instinct-mi300x-platform-data-sheet.pdf" rel="noopener noreferrer" target="_blank">https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/data-sheets/amd-instinct-mi300x-platform-data-sheet.pdf</a></li></ul> <h4>5. RDMA and RoCE</h4>Remote Direct Memory Access (RDMA)&nbsp;allows memory access across nodes without CPU intervention, significantly reducing latency. RDMA can be implemented over InfiniBand or Ethernet-based technologies like RoCE (RDMA over Converged Ethernet): <ul><li>RoCE: Brings RDMA's low-latency benefits to Ethernet networks, enabling compatibility with existing data center infrastructure.</li><li>InfiniBand RDMA: Offers the lowest latency and highest throughput, ideal for HPC.</li><li><a href="https://www.juniper.net/content/dam/www/assets/white-papers/us/en/2024/juniper-artificial-intelligence-data-center-comparison-of-infiniband-and-rdma-over-converged-ethernet.pdf" rel="noopener noreferrer" target="_blank">https://www.juniper.net/content/dam/www/assets/white-papers/us/en/2024/juniper-artificial-intelligence-data-center-comparison-of-infiniband-and-rdma-over-converged-ethernet.pdf</a></li></ul> <h3>Performance Benefits of Advanced Interconnects</h3> <h4>1. Clustered GPU and CPU Systems For AI and HPC</h4>In clustered systems, interconnect technologies like InfiniBand, NVLink and RDMA are critical for ensuring efficient communication between compute elements:<ul><li>GPU Clusters: NVlink and xGMI create unified memory pools, while InfiniBand or RoCE connects GPUs across nodes with minimal overhead, enabling fast AI/ML training or large-scale simulations.</li><li>Hybrid Clusters: InfiniBand and xGMI\Infinity Fabric - provide low-latency connections between CPUs and GPUs, optimising workloads that rely on both compute types.</li><li> </li></ul><h4>2. AI ML Workloads</h4>AI and ML workloads benefit significantly from advanced interconnects:<ul><li>NVLink&nbsp;accelerates GPU to GPU communication, reducing training times for AI models.</li><li>InfiniBand RDMA&nbsp;allows GPUs across nodes to exchange data efficiently enabling distributed AI training.</li><li>RoCE (RDMA Over Converged Ethernet) used as a Ethernet networking alternative to NVidia\Mellanox's Infiniband.</li><li> </li></ul>3. HPC WorkloadsHigh-performance computing (HPC) demands high-speed, low-latency networking interconnects:<ul><li>InfiniBand: Essential for large-scale simulations, weather modeling and complex molecular dynamics.</li><li>RDMA: Eliminates CPU bottlenecks in distributed systems, ensuring smooth data flow across nodes.</li></ul> <h3>Profitability of AI Server Rentals: How Interconnects Make a Difference</h3> <h4>1. Increased Performance and Utilisation</h4>Faster interconnects reduce workload execution times, allowing servers to handle more tasks within a given period. Features like InfiniBand scalability and NVLink’s unified memory pools enable:<ul><li>Higher Throughput: More tasks completed per server.</li><li>Improved Resource Utilisation: Minimising idle hardware increases revenue efficiency.</li></ul> <h4>2. Differentiated Offerings in Data Centre Tiers</h4>Advanced interconnects, storage solutions and memory technologies allow service providers to create premium pricing tiers, ensuring they can cater to a wide range of customer needs while maximising revenue opportunities. Here’s a breakdown of the key tiers and their potential for profitability: <h4>Lowest Tier: PCIe 3 Systems</h4>Servers using PCIe 3&nbsp;interconnects are rapidly becoming obsolete. While they can still handle basic workloads, their earnings potential is extremely limited. Achieving ROI is highly unlikely unless:<ul><li>The equipment is purchased at a very low price.</li><li>Operating costs, particularly electricity, are minimal (less than $0.10 USD per kWh).</li></ul>Most rental platforms avoid offering multi-GPU servers with PCIe 3, even if the GPUs themselves are decent, due to these constraints. <h4>Standard Tier: PCIe 4 Systems</h4>PCIe 4-based servers are the current standard for general-purpose workloads and some AI tasks. However, they are gradually being replaced by PCIe 5 systems.<ul><li>Profitability: While still profitable in the short term, their earnings potential is diminishing over time.</li><li>Use Cases: Best suited for rendering or AI inference workloads that run on single GPUs or multi-GPU setups without clustering.</li></ul>Performance Range: PCIe 4 servers have a broad range of configurations, from entry-level systems with 8-core CPUs to high-performance dual AMD EPYC&nbsp;setups with up to 128 cores. <h4>Medium to High-Performance Tier: PCIe 5 Systems</h4>PCIe 5-based servers represent the next step in performance for bare-metal machines. These systems:<ul><li>Deliver strong earnings when paired with powerful GPUs.</li><li>Are ideal for connecting devices on server nodes.</li></ul>Storage Innovations: Advanced storage solutions, such as NVMe drives&nbsp;with PCIe 4 or 5 interfaces and storage-class memory (SCM), are often integrated at this tier to support demanding workloads. These provide:<ul><li>Low-latency performance, critical for real-time processing in AI, HPC or some high-frequency trading applications.</li><li>High throughput, ensuring that even large datasets can be handled efficiently without bottlenecks.</li></ul> OpenZFS File System:<ul><li>OpenZFS is increasingly adopted in high-performance environments at this tier for its advanced features, including data integrity checks, deduplication, and efficient snapshots.</li><li>It allows seamless integration with NVMe and scalable storage pools, making it an attractive choice for maintaining data reliability and performance.</li></ul> CXL (Compute Express Link) Interconnects:<ul><li>CXL 2.0 and 3.1&nbsp;interconnects, built on PCIe 5, allow systems to share memory and expand their capacity seamlessly.</li><li>These technologies enable the pooling of high-speed memory, which can be dynamically allocated across CPUs and GPUs.</li><li>For memory-intensive workloads like AI training, CXL dramatically increases system flexibility and scalability, reducing bottlenecks.</li></ul>This tier also serves as the foundation for servers in the higher-performance tiers, which incorporate additional advanced technologies. <h4>High-Performance Tier: InfiniBand and RoCE Systems</h4>Servers equipped with InfiniBand&nbsp;or RoCE&nbsp;(RDMA over Converged Ethernet) excel in distributed system setups. They offer:<ul><li>Earnings Potential: Good to very good returns when utilised effectively.</li><li>Requirements: These systems demand specialised knowledge, infrastructure, and moderate to significant investment.</li></ul>Storage Innovations: Distributed storage systems, such as Ceph&nbsp;or BeeGFS, are commonly deployed in these configurations. These enable:<ul><li>Scalable, high-speed storage, critical for workloads like machine learning and HPC.</li><li>Data redundancy and fault tolerance, ensuring system reliability.</li></ul> OpenZFS\ZFS File System:<ul><li>OpenZFS excels in this tier due to its scalability and advanced features such as multi-level caching&nbsp;(ARC and L2ARC) and compression, which enhance performance while optimising storage usage.</li><li>Its ability to handle petabyte-scale datasets makes it ideal for AI, HPC, and large-scale data analytics.</li></ul> CXL and Memory Expansion:<ul><li>Systems in this tier benefit significantly from CXL-enabled memory expansion, which allows for larger memory pools that can be shared between nodes.</li><li>Memory pooling and tiering&nbsp;using CXL reduces overall system costs while enhancing performance for distributed AI and HPC tasks.</li></ul> <h4>Premium GPU Clusters: NVLink and xGMI with InfiniBand or RoCE</h4>The highest-performance tier involves NVLink&nbsp;or xGMI GPU clusters, paired with InfiniBand&nbsp;or RoCE, making them ideal for AI/ML&nbsp;or HPC&nbsp;applications. These systems:<ul><li>Require large-scale investment&nbsp;in premium GPUs, CPUs, and infrastructure.</li><li>Demand skilled staff for setup, maintenance, and operation.</li><li>Need a strong sales pipeline to secure renters or contracts to ensure high utilisation.</li></ul>Storage Innovations: Premium GPU clusters incorporate cutting-edge storage solutions to complement their high-performance computing capabilities:<ul><li>All-flash arrays (AFAs)&nbsp;provide ultra-low latency and high IOPS, essential for the massive data throughput demands of HPC and AI training.</li><li>Parallel file systems&nbsp;such as Lustre&nbsp;or IBM Spectrum Scale (GPFS)&nbsp;enable rapid data access and processing across distributed clusters.</li><li>Tiered storage architectures, integrating SCM or NVMe alongside more cost-effective storage, optimise both performance and cost.</li></ul> OpenZFS, ZFS Or other Distributed Files Systems:<ul><li>OpenZFS is a popular choice for managing complex storage needs in premium clusters. Its ability to handle data compression&nbsp;and adaptive caching&nbsp;enhances both performance and efficiency.</li><li>With support for replication&nbsp;and snapshots, OpenZFS ensures data security and facilitates system recovery in high-stakes environments.</li></ul>CXL for High-Performance Memory:<ul><li>CXL enables the creation of memory fabrics, allowing GPUs, CPUs, and accelerators to access shared memory pools without traditional bottlenecks.</li><li>This capability enhances scalability for AI/ML training workloads and complex simulations, making it a critical component in premium clusters.</li></ul>Risk and Profitability:<ul><li>Highly profitable when fully utilised, but operational costs are significant.</li><li>Risky for inexperienced investors unless a skilled team and customer contracts are in place before deployment.</li></ul> <h3>Maximising Revenue Across Tiers</h3>Each tier caters to specific customer needs, from budget-conscious workloads to cutting-edge applications requiring premium hardware, storage, and memory. The inclusion of advanced interconnects like CXL, alongside robust file systems like OpenZFS, further enhances performance and scalability, making these solutions even more attractive to diverse markets. By offering comprehensive server configurations, service providers can ensure increased revenue as they scale up the performance and complexity of their offerings. <h4>3. Enhanced Customer Satisfaction and Retention</h4>Customers benefit from faster workload completion and reliable performance:<ul><li>Reduced Latency: Technologies like NVLink\Infinity Fabric and RDMA improve user experience for latency-sensitive tasks.</li><li>Seamless Scalability: InfiniBand and RoCE ensures smooth expansion for growing workloads.</li></ul>Satisfied customers are more likely to renew contracts and recommend services, increasing lifetime value (LTV). <h4>4. Operational Cost Savings</h4>Efficient interconnects reduce costs associated with energy and infrastructure:<ul><li>Lower Power Consumption: Faster job execution reduces energy costs per workload.</li><li>Optimized Hardware Requirements: High-speed interconnects minimise the number of nodes required for large-scale jobs.</li></ul> <h4>5. Market Competitiveness</h4>Providers offering advanced interconnect technologies stay ahead in the competitive server rental market:<ul><li>AI/ML and HPC Attractiveness: InfiniBand and NVLink cater to the growing demand for these workloads.</li><li>Branding as a Premium Provider: High-performance interconnects elevate the perceived value of services.</li><li> </li></ul><h3>Challenges and Considerations</h3><ul><li>Higher Initial Costs: Advanced interconnects like InfiniBand and NVLink require significant investment.</li><li>Customer Education: Providers must educate customers on the value of interconnect technologies to justify premium pricing.</li></ul> <h3>Conclusion</h3> Interconnect technologies like PCIe 5, InfiniBand, RoCE, NVLink, xGMI Infinity Fabric CXL, and RDMA&nbsp;are transforming server infrastructure, particularly in clustered CPU-GPU systems. Their ability to enhance performance, optimise resource usage, and reduce costs makes them indispensable for modern server rentals. By offering advanced interconnect-equipped servers, rental providers can: <ol><li>Accelerate workload execution for AI, ML, and HPC tasks.</li><li>Maximise resource utilisation and server profitability.</li><li>Differentiate their offerings and charge premium prices.</li><li>Build customer loyalty and strengthen market positioning.</li></ol> Incorporating these interconnect technologies into server rental strategies ensures long-term profitability and competitiveness in a rapidly evolving market. This blog is for anyone interested in earning passive income by renting out servers they've purchased as part of a new and exciting asset class. If you're considering entering this market but need guidance, feel free to reach out to me, <a href="https://www.linkedin.com/in/james-walsh-15b40591/" rel="noopener noreferrer" target="_blank">James Walsh (Sytronix)</a> or <a href="https://www.linkedin.com/in/alex-moody-6b2502170/" rel="noopener noreferrer" target="_blank">Alex Moody (Sytronix)</a> via LinkedIn. I’ll also share this blog there for easy access.Don’t worry if you're unfamiliar with the technologies discussed here just ask! Whether you connect with me, one of my team members, or our trusted partners Panchaea&nbsp;in Europe and Hydra Host&nbsp;in the USA we’ll be happy to explain the tech and its potential in simple, straightforward terms. For those looking to explore the investment opportunities in this space further, our data center partner, <a href="https://www.daylightcompute.co.uk/" rel="noopener noreferrer" target="_blank">Daylight Compute</a>&nbsp;(based in the UK), can provide additional insights. Let’s get started!

Learn how interconnects like PCIe, NVLink, and InfiniBand impact the profitability of AI server rentals. Optimize for speed and revenue.

Profitability Of Renting AI servers with different interconnects

<h1>From Refuge to Revolution: Patmos' Journey into GPU Compute with Hydra Host&nbsp;</h1><h2> </h2><h2>Discover how Patmos transformed its data center monetization strategy through a dynamic partnership with Hydra Host.&nbsp;</h2> <h3>Introduction and Background&nbsp;</h3> Inspired by the small Greek island known for its historic exile, <a href="http://patmos.tech/" rel="noopener noreferrer" target="_blank">Patmos</a> began its journey as a boutique hosting company with a bold mission: to provide a safe haven for free speech.&nbsp;Recognizing the urgent need for a secure and resilient digital refuge, Patmos acquired its own data center to ensure complete independence from external infrastructure.&nbsp;This strategic move ​allows​ them to guarantee their clients' security and autonomy, crucial for defending the right to be heard for all ideas.&nbsp; Hydra Host, sharing a similar philosophy, was a natural ally for Patmos. Together, we formed an alliance to offer a robust and reliable GPU infrastructure for AI/ML workloads.&nbsp; <h3>Goals and Objectives&nbsp;</h3> When Patmos ventured into the GPU infrastructure market, their goal was to penetrate the competitive space and establish a customer base.&nbsp;Partnering with Hydra Host provided the contacts, customers, and relationships necessary for this endeavor, leveraging our network and​ our GPU Marketplace, <a href="https://brokkr.hydrahost.com/" rel="noopener noreferrer" target="_blank">Brokkr</a>,​ to gain market traction.&nbsp; <h3>Challenges Faced&nbsp;</h3> Before collaborating with Hydra Host, Patmos faced challenges monetizing its GPU infrastructure and gaining market visibility in the competitive GPU hosting market.&nbsp;Despite robust infrastructure, they faced difficulties in customer acquisition and market visibility.&nbsp; <h3>Solutions and Implementation&nbsp;</h3> Hydra Host introduced the Brokkr GPU Marketplace, a turnkey solution that streamlined the GPU rental process, allowing customers to easily rent GPUs for AI workloads and high-performance computing (HPC) tasks.​​ This turnkey solution ​allows​ customers to easily log in and rent GPUs, relieving Patmos of the burden of customer acquisition and enabling them to focus on service excellence.&nbsp;With Brokkr, Patmos ​can now ​effortlessly offer HPC solutions to diverse clients, from single GPU users to large-scale cluster rentals.&nbsp; <h3>Results and Benefits&nbsp;</h3> The collaboration with Hydra Host transformed Patmos's business by enabling the company to tap into the growing AI market and provide scalable GPU solutions for customers ranging from individual developers to enterprise-level HPC needs.&nbsp;&nbsp; <h3>Future Plans&nbsp;</h3> Looking ahead, Patmos plans to expand its data center infrastructure with a new flagship location in the Midwest.&nbsp;With over 20 megawatts dedicated to HPC and higher-than-standard rack densities, this facility aims to meet the growing demand for GPU resources and fortify Patmos's market position.&nbsp; <h3>Conclusion&nbsp;</h3> The partnership between Patmos and Hydra Host has been transformative, providing Patmos with the tools and support needed to thrive in the competitive GPU hosting market.&nbsp;Through Hydra Host's innovative Brokkr GPU Marketplace and extensive marketing efforts, Patmos ​has successfully overcome​ the challenges of customer acquisition and market penetration.&nbsp;This collaboration enabled Patmos to offer a wide range of high-performance computing solutions and ​has reinforced​ their commitment to providing a digital refuge for free speech.&nbsp; As Patmos continues to expand, our alliance remains a cornerstone of their growth strategy.&nbsp;The future looks bright for Patmos as they continue to build on the solid foundation established through their partnership with Hydra Host, ensuring that voices from all corners of the digital world can be heard​,​ unimpeded and unafraid.&nbsp; &nbsp;

Learn how Patmos partnered with Hydra Host to revolutionize GPU compute with Brokkr. Discover strategies for AI and HPC market success.

From Refuge to Revolution: Patmos' Journey into GPU Compute with Hydra Host

<h1>AI Glossary: 50+ Essential Terms to Understand Artificial Intelligence</h1> <h2>A&nbsp;</h2> AI (Artificial Intelligence): The simulation of human intelligence processes by machines, including learning, reasoning, and self-correction.&nbsp; AI Accelerator: Specialized hardware or systems designed to speed up AI tasks, with NVIDIA GPUs being a leader in this space.&nbsp; Algorithm: A set of rules or instructions given to an AI system to help it learn, solve problems, and make decisions.&nbsp; Ampere Architecture: NVIDIA's GPU architecture designed to accelerate AI, data analytics, and HPC workloads.&nbsp; &nbsp; <h2>B&nbsp;</h2> Bare Metal: A physical server dedicated to a single tenant, offering full control over its resources without virtualization.&nbsp; Benchmarking: The process of measuring system or model performance against a standard to evaluate efficiency.&nbsp; Big Data: Large, complex datasets requiring advanced tools for processing, often used to uncover patterns.&nbsp; Black Box: An AI system where the decision-making process is opaque, even if inputs and outputs are visible.&nbsp; &nbsp; <h2>C&nbsp;</h2> Cluster: A group of interconnected computers (nodes) working as a unified system for complex computations.&nbsp; Colocation: Housing privately-owned servers in a third-party data center.&nbsp; Compute: The processing power available in cloud or data center environments for running applications.&nbsp; Containerization: A lightweight virtualization method that packages applications and dependencies into a container.&nbsp; Conversational AI: Technology that powers machines to engage in human-like conversations using natural language processing (NLP).&nbsp; CUDA (Compute Unified Device Architecture): NVIDIA’s parallel computing platform enabling general-purpose processing on GPUs.&nbsp; CUDA Core: The fundamental processing unit in an NVIDIA GPU, handling parallel computing operations.&nbsp; &nbsp; <h2>D&nbsp;</h2> Deep Learning: A subset of machine learning that uses neural networks with multiple layers to analyze complex data.&nbsp; DGX System: NVIDIA’s AI supercomputers designed for deep learning and AI workloads.&nbsp; &nbsp;<h2> </h2><h2>F&nbsp;</h2> Finetuning: The process of training a pre-trained AI model on a smaller, task-specific dataset to enhance performance.&nbsp; FP16 (Half Precision Floating Point): A 16-bit floating point format that improves memory efficiency in AI training.&nbsp; FP32 (Single Precision Floating Point): A 32-bit format used in AI training and gaming for representing real numbers.&nbsp; &nbsp;<h2> </h2><h2>G&nbsp;</h2> GDDR Memory: A type of memory in GPUs that provides high bandwidth for data access, critical in gaming and AI.&nbsp; Generative AI: AI that creates new content (text, images, video) based on patterns learned from existing data.&nbsp; GPU (Graphics Processing Unit): A processor designed for parallel processing, widely used in AI and machine learning.&nbsp; &nbsp;<h2> </h2><h2>H&nbsp;</h2> HPC (High-Performance Computing): The use of supercomputers for solving complex problems requiring large-scale processing.&nbsp; Hypervisor: Software that runs virtual machines by separating the physical hardware from the operating system.&nbsp; &nbsp;<h2> </h2><h2>I&nbsp;</h2> InfiniBand (IB): A high-speed networking standard used in HPC clusters for low-latency communication.&nbsp; Internet of Things (IoT): A network of connected devices that exchange data, often enhanced by AI for predictive tasks.&nbsp; IOPS (Input/Output Operations Per Second): A measurement of storage device performance.&nbsp; &nbsp; <h2>K&nbsp;</h2> Kubernetes: An open-source platform automating the deployment and management of containerized applications.&nbsp; &nbsp; <h2>L&nbsp;</h2> Latency: The time delay between data transmission and receipt, important for real-time applications.&nbsp; LLM (Large Language Model): AI models trained on vast amounts of text data to generate human-like language.&nbsp; Load Balancing: Distributing workloads across resources to optimize performance and avoid overloading any single system.&nbsp; &nbsp;<h2> </h2><h2>M&nbsp;</h2> Machine Learning (ML): A subset of AI where machines use algorithms and statistical models to improve performance over time.&nbsp; MIG (Multi-Instance GPU): A feature allowing an NVIDIA GPU to be partitioned into smaller, isolated instances.&nbsp; &nbsp;<h2> </h2><h2>N&nbsp;</h2> Natural Language Processing (NLP): AI that allows computers to understand, interpret, and generate human language.&nbsp; NIC (Network Interface Controller): A hardware component enabling a device to connect to a network.&nbsp; Node: A single machine or device in a network or cluster that processes and exchanges data.&nbsp; NVLink: NVIDIA’s high-speed interconnect for fast communication between GPUs and CPUs.&nbsp; NVSwitch: NVIDIA’s high-bandwidth switch allowing multiple GPUs to work together within a server.&nbsp; &nbsp;<h2> </h2><h2>O&nbsp;</h2> OOB (Out of Band): A management method allowing access to network devices via a separate channel, even if the main network is down.&nbsp; Optimization: The process of improving system or model performance by adjusting parameters.&nbsp; &nbsp;<h2> </h2><h2>P&nbsp;</h2> Pre-training: The initial training phase where AI models learn general patterns from large datasets.&nbsp; &nbsp;<h2> </h2><h2>R&nbsp;</h2> RAID (Redundant Array of Independent Disks): A data storage technology combining multiple disks for redundancy and improved performance.&nbsp; &nbsp; <h2>S&nbsp;</h2> SLA (Service Level Agreement): A contract specifying the level of service expected from a provider.&nbsp; SLM (Small Language Model): A smaller AI model used for tasks where large-scale models are unnecessary.&nbsp; &nbsp;<h2> </h2><h2>T&nbsp;</h2> Tensor: A mathematical object used in machine learning and AI, particularly in tensor processing units (TPUs).&nbsp; Tensor Core: Specialized cores in NVIDIA GPUs designed for matrix operations crucial to AI workloads.&nbsp; TensorRT: NVIDIA’s deep learning inference library optimized for real-time AI applications.&nbsp; Text-to-Image Generator: An AI tool that converts text descriptions into images.&nbsp; Text-to-Speech: AI technology that converts written text into spoken words.&nbsp; Throughput: The amount of data processed by a system in a given amount of time, often measured in bits per second (bps).&nbsp; &nbsp; <h2>V&nbsp;</h2> Virtualization: Creating virtual versions of resources like servers or storage to enable multiple OSs on one machine.&nbsp; Volta Architecture: NVIDIA’s GPU architecture that introduced Tensor Cores, widely used in AI tasks.&nbsp; &nbsp;

Master AI terminology with Hydra Host’s Essential AI Glossary. Over 50 terms explained, from GPUs and deep learning to NLP and Tensor Cores.

The Essential Glossary of AI: 50+ Key Terms to Know 

<h1>Introduction: The Anticipation of Blackwell&nbsp;</h1> In the fast-paced world of high-performance computing (HPC) and artificial intelligence (AI), NVIDIA’s upcoming <a href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/?ncid=pa-srch-goog-267462&amp;_bt=709953316733&amp;_bk=nvidia%20blackwell&amp;_bm=p&amp;_bn=g&amp;_bg=169122792928&amp;gad_source=1&amp;gclid=EAIaIQobChMI-vbrv9aGiAMV2zWtBh1nQipsEAAYASAAEgIT6vD_BwE" rel="noopener noreferrer" target="_blank">Blackwell architecture</a> is the next big leap everyone is eagerly awaiting. Blackwell is expected to deliver unprecedented breakthroughs in performance, energy efficiency, and scalability, set to redefine AI, HPC, and data center applications. But with Blackwell's release still on the horizon, businesses can't afford to wait. So, what are the best GPU alternatives to rent in the meantime?&nbsp; <h2>The Waiting Game: Why You Need GPU Solutions Now&nbsp;</h2> The anticipation around NVIDIA Blackwell is palpable, but waiting doesn't mean your AI and HPC projects should stall. In today’s competitive landscape, staying ahead means accessing powerful compute solutions right now. Fortunately, Hydra Host offers several GPU rental options that can seamlessly bridge the gap while you await Blackwell's official release.&nbsp; <h2>Current Alternatives to Blackwell: What to Rent Now&nbsp;</h2> <h3>1. <a href="https://hydrahost.com/nvidia/h200/" rel="noopener noreferrer" target="_blank">Commit to H200</a>&nbsp;</h3>The NVIDIA H200 GPU is the next evolution from the H100, delivering even more power and performance for demanding AI and HPC applications. While it comes at a slightly higher cost than the H100, the H200’s advanced architecture and enhanced capabilities make it ideal for businesses that need top-tier performance without compromise. This GPU ensures that your AI, machine learning (ML), and data processing tasks are completed with unmatched efficiency, keeping you ahead of the competition.&nbsp;&nbsp;<ul><li>Ideal for pushing the boundaries of AI and ML.&nbsp;</li><li>Suitable for running complex simulations.&nbsp;</li><li>Effective for training large-scale models.&nbsp;</li><li>Handles massive datasets with unmatched efficiency.&nbsp;</li><li>Ensures top-tier performance for businesses requiring no compromise.&nbsp;</li></ul> <h3>2. <a href="https://hydrahost.com/nvidia/grace-hopper/" rel="noopener noreferrer" target="_blank">On-Demand GH200s</a>&nbsp;</h3> Renting on-demand GH200 GPUs gives your business the flexibility to scale resources as needed, without the burden of long-term commitments. At just $4.99 per card on the <a href="http://brokkr.hydrahost.com/" rel="noopener noreferrer" target="_blank">Brokkr Marketplace</a>, this cost-effective option is ideal for businesses that need to stay agile and ready for Blackwell’s release.&nbsp; <ul><li>Perfect for prototyping AI/ML models.&nbsp;</li><li>Useful for testing new algorithms.&nbsp;</li><li>Prepares workloads for future transition to Blackwell.&nbsp;</li><li>Allows for nimble operations with flexible scaling&nbsp;</li><li>Cost-effective option at $4.99 per card on Brokkr Marketplace.&nbsp;</li></ul> <h3>3. <a href="https://hydrahost.com/nvidia/h100-pcie-80-gb/" rel="noopener noreferrer" target="_blank">H100 Tensor Core GPUs</a>&nbsp;</h3> The H100 Tensor Core GPUs remain a top choice for AI and deep learning tasks, renowned for their exceptional performance. With some H100 clusters coming off contract, businesses can seize the opportunity to access these powerful GPUs at competitive on-demand pricing or through attractive long-term deals, particularly suited for inference workloads. This flexibility allows you to efficiently scale your AI operations while optimizing costs.&nbsp;<ul><li>Excellent for training and inference in deep learning models.&nbsp;</li><li>Ideal for natural language processing (NLP) tasks.&nbsp;</li><li>Supports advanced scientific computing.&nbsp;</li><li>Suitable for businesses needing short-term rentals or long-term commitments.&nbsp;</li><li>Offers reliability and power to maintain momentum in AI projects.&nbsp;</li></ul> <h3>4. <a href="https://hydrahost.com/nvidia/a100-pcie-40-gb/" rel="noopener noreferrer" target="_blank">A100 GPUs</a>&nbsp;</h3> The A100 GPUs, part of NVIDIA’s Ampere architecture, continue to be a reliable workhorse for AI, data analytics, and HPC tasks. These GPUs offer an excellent balance of performance and cost, making them a savvy choice for businesses that want to maintain momentum without overspending while waiting for Blackwell.&nbsp;<ul><li>Well-suited for both training and inference workloads.&nbsp;</li><li>Ideal for AI, data analytics, and HPC tasks.&nbsp;</li><li>Provides robust power without a hefty price tag.&nbsp;</li><li>Cost-effective bridge to the future NVIDIA Blackwell architecture.&nbsp;</li><li>Maintains project momentum during the wait.&nbsp;</li></ul> <h3>GPU Comparison Table&nbsp;</h3> Here’s a detailed comparison of the H200, GH200, H100, and A100 GPUs to help you choose the best option based on your project needs:&nbsp;

<h1>Why Renting Makes Sense Now&nbsp;</h1> <ul><li>Avoiding Obsolescence: Why invest heavily in technology that may soon be outdated? Renting GPUs allows you to avoid large expenditures on hardware that could lose its edge once Blackwell is released.&nbsp;</li><li>Flexibility and Scalability: Renting GPU resources provides the agility your business needs. You can scale up or down based on project demands, without being tied down, paying only for what you use.&nbsp;</li><li>Preparing for the Future: Renting GPUs now allows your team to continue refining projects, so when Blackwell finally arrives, you’re not just catching up—you’re leading the pack in AI and HPC innovation.&nbsp;</li></ul> <h1>Conclusion: Stay Ahead of the Curve&nbsp;</h1> In the rapidly evolving AI/ML landscape, standing still is not an option. Renting GH200s, H100s, or A100s is a strategic move to keep your business moving forward during the wait for NVIDIA Blackwell. It’s about positioning yourself to leverage future advancements, not just survive them.&nbsp; Explore the comprehensive GPU rental options available in the <a href="http://brokkr.hydrahost.com/" rel="noopener noreferrer" target="_blank">Brokkr Marketplace</a> today. <a href="https://meetings.hubspot.com/ryan-horjus" rel="noopener noreferrer" target="_blank">Schedule a consultation</a> with our Lead Sales Engineer, Ryan, and find out which setup best meets your needs, ensuring you’re ready when Blackwell arrives.&nbsp;

Discover top GPU rental options like H100, GH200, and A100 while waiting for NVIDIA Blackwell. Optimize AI and HPC workloads today.

What to rent while waitign for NVIDIA Blackwell? 

What to Rent While Waiting for NVIDIA Blackwell: Top GPU Alternatives for AI and HPC 

<h1>Unlocking Breakthroughs in BioTech and Pharma with Cluster as a Service (CaaS)&nbsp;</h1>&nbsp;In the rapidly evolving world of biotechnology and pharmaceuticals, the ability to process vast amounts of data quickly and accurately is paramount. From drug discovery and genomics to genetic research, the need for high-performance computing in pharma has never been greater. Hydra Host's <a href="https://hydrahost.com/cluster/" rel="noopener noreferrer" target="_blank">Cluster as a Service (CaaS)</a> delivers a robust solution, leveraging GPU clusters to propel innovation in these critical fields.&nbsp;&nbsp;<h2>The Power of GPU Clusters in Chemical Modeling&nbsp;</h2>&nbsp;Chemical modeling and simulations are at the heart of modern pharmaceutical research. Using computational techniques to model the behavior of molecules, researchers can predict the interactions between drugs and their targets, optimize compounds, and reduce the time and cost associated with bringing new drugs to market. Hydra Host's GPU clusters are engineered to meet the immense computational demands of chemical modeling. Our CaaS platform provides access to the latest NVIDIA GPUs, tailored for complex calculations in molecular dynamics, quantum chemistry, and other advanced simulations.&nbsp;&nbsp;<h2>Why Choose Hydra Host's Cluster as a Service?&nbsp;</h2>&nbsp;<ol><li>Superior Performance for Complex Simulations: Our GPU clusters deliver unmatched performance, enabling researchers to run detailed simulations at unprecedented speeds. Whether you are modeling protein-ligand interactions or exploring new chemical compounds, our clusters can handle the most demanding computational tasks with ease.&nbsp;</li><li>Flexibility and Scalability: Hydra Host's CaaS offers flexibility, allowing you to scale your computing resources up or down based on your project's needs. This means you can quickly adapt to changing research requirements without the need for significant upfront investment in hardware.&nbsp;</li><li>Cost-Effective HPC Solutions: By partnering with financial firms, Hydra Hosts provides access to financing for powerful GPU clusters and helps you manage your budget more effectively. This model eliminates the need for costly infrastructure investments and reduces operational costs, making research more accessible. For more affordable options, check out our blog on <a href="https://hydrahost.com/post/top-affordable-gpus-for-deep-learning/" rel="noopener noreferrer" target="_blank">Top Affordable GPUs for Deep Learning</a>.&nbsp;</li><li>Expertise and Support: Our team is dedicated to ensuring you get the most out of our CaaS platform. From initial setup to ongoing support, we work closely with you to optimize your workflows and achieve your research goals.&nbsp;</li></ol>&nbsp;<h2>Applications in Biotechnology and Pharmaceuticals&nbsp;</h2>&nbsp;<ul><li>Drug Discovery and Development: GPU clusters are revolutionizing drug discovery by enabling faster, more accurate simulations of molecular interactions. This accelerates the identification of promising drug candidates and optimizes their properties before clinical trials. Learn more about the <a href="https://www.nature.com/articles/s41573-019-0017-4" rel="noopener noreferrer" target="_blank">role of high-performance computing in drug discovery</a>.&nbsp;</li><li>Genomics and Personalized Medicine: In genomics, rapid data processing and analysis are crucial. Our GPU clusters support advanced techniques like whole-genome sequencing and personalized medicine, offering the computational power needed to uncover genetic insights. Explore more about <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5868789/" rel="noopener noreferrer" target="_blank">high-performance computing in genomics</a>.&nbsp;</li><li>Deep Learning in Bioinformatics: Deep learning is transforming bioinformatics, providing powerful tools for analyzing complex biological data. GPU clusters are vital for training deep learning models that can identify patterns and make predictions from large datasets, applicable in areas like histopathology, protein structure prediction, and genetic variation analysis.&nbsp;&nbsp;</li><li>Molecular Dynamics: Understanding molecular behavior in various environments is essential for drug design. Our GPU clusters perform molecular dynamics simulations at previously unattainable scales and speeds, offering deeper insights into molecular behavior.&nbsp;&nbsp;</li><li>Quantum Chemistry: Quantum chemistry calculations are essential yet computationally intensive for accurately modeling chemical reactions and properties. Hydra Host's GPU clusters offer the computational power necessary for efficient quantum chemistry calculations.&nbsp;&nbsp;</li></ul>&nbsp;<h2>Join the Future of BioTech and Pharma with Hydra Host&nbsp;</h2>Hydra Host's Cluster as a Service is more than just a technological solution; it is a gateway to innovation and discovery. By leveraging our GPU clusters, biotech and pharma companies can accelerate their research, reduce costs, and ultimately bring new therapies and solutions to the market faster.&nbsp;&nbsp;Explore the possibilities with Hydra Host and see how our Cluster as a Service can transform your research capabilities. Visit our <a href="https://hydrahost.com/cluster/" rel="noopener noreferrer" target="_blank">Cluster as a Service</a> page to learn more and get started today.&nbsp;

Revolutionize biotech and pharma with Hydra Host's Cluster as a Service. Accelerate research with scalable GPU clusters for discovery.

GPU clusters for biotech and pharma research

Unlocking Breakthroughs in BioTech and Pharma with Cluster as a Service (CaaS)

<h1>Embracing Sovereign AI with Hydra Host's Bare Metal Compute&nbsp;</h1> AI is at odds with globalization. <a href="https://blogs.nvidia.com/blog/what-is-sovereign-ai/" rel="noopener noreferrer" target="_blank">Sovereign AI</a> is gaining momentum by empowering nations to control their own foundational models and safeguard their data, security, and sovereignty. At Hydra Host, we take a strong position at the spearhead of these movements by providing the infrastructure required to support these initiatives through our bare metal compute solutions.&nbsp; <h2>The Call for Sovereign AI&nbsp;</h2> Nvidia CEO <a href="https://blogs.nvidia.com/blog/world-governments-summit/" rel="noopener noreferrer" target="_blank">Jensen Huang</a> described the world transitioning into a second wave of AI, driven by government engagement and the need for nation-states to develop their sovereign AI. "It codifies your culture, your society's intelligence, your common sense, your history – you own your own data," Huang stated. "It's not that costly, it is also not that hard," he added, suggesting that the first step should be to codify the language and data of your culture into your own large language model.&nbsp; <h2>The Importance of Sovereign AI&nbsp;</h2> Sovereign AI is about asserting control, enhancing security, and building trust. It's imperative for nations and organizations to protect their data, maintain economic stability, and ensure autonomy. In a world where data is as valuable as oil, controlling this resource is crucial for national security and economic competitiveness.&nbsp; <h3>Why Sovereign AI Matters:&nbsp;</h3> <ul><li>Economic Security: Control over data and AI infrastructure guards' economic interests from external manipulation and influence.&nbsp;</li><li>Political Autonomy: Ensuring data sovereignty supports national sovereignty, preventing undue foreign influence over critical infrastructure.&nbsp;</li><li>Strategic Advantage: Nations and organizations that control their AI infrastructure can leverage these technologies for competitive advantages in global markets.&nbsp;</li><li>Cultural Preservation: Sovereign AI helps protect and codify a nation's culture, language, and societal values within its AI models.&nbsp;</li></ul> <h2>Four Dimensions of Sovereign AI&nbsp;</h2> <ol><li>Local Data Storage: Ensuring data is stored within the correct jurisdiction to comply with local laws.&nbsp;</li><li>Platform Ecosystem: Evaluating and securing the hosting platform to prevent unauthorized access and breaches.&nbsp;</li><li>Data Exchange and Usage: Controlling who accesses and shares your data, ensuring it aligns with national and organizational interests.&nbsp;</li><li>Operational Management: Managing your data and infrastructure securely to maintain integrity and trust.&nbsp;</li></ol>&nbsp;&nbsp;<h2>The Challenge of Cloud Data Sovereignty&nbsp;</h2> The <a href="https://www.bsa.org/files/policy-filings/09012021whatiscloudact.pdf" rel="noopener noreferrer" target="_blank">US Cloud Act of 2018</a> posed significant challenges to data sovereignty by requiring US-based cloud providers to comply with US laws, even for data stored overseas. While hyperscalers have attempted to address these concerns by expanding local data center offerings, the fundamental issue remains true data sovereignty cannot be fully realized without control over the infrastructure.&nbsp; <h2>Bare Metal: The Optimal Choice for Sovereign AI&nbsp;</h2> Bare metal servers offer a dedicated single-tenant environment with unparalleled security, performance, and control. Bare metal is the best choice for sensitive high performance workloads: it ensures that data and computing power are not exposed to unnecessary third parties, simplifying security and privacy posture. This makes it an ideal choice for highly adversarial nation-state use cases do not leave the country's control and cannot be affected by foreign interference.

<h2>Hydra Host's Comprehensive Solutions&nbsp;</h2> At Hydra Host, we are not just keeping pace with the evolving landscape—we're leading it. Our suite of services, including Cluster-as-a-Service (CaaS), the Brokkr SaaS platform, high-performance GPU offerings, and our API, empowers organizations to achieve true data sovereignty. Here is how:&nbsp; <h3><a href="https://hydrahost.com/cluster/" rel="noopener noreferrer" target="_blank">Cluster-as-a-Service (CaaS)</a></h3>Our CaaS platform provides direct access to advanced GPU clusters, ensuring efficient AI processing without the limitations of shared environments. Dedicated servers minimize the risks associated with shared environments, providing a secure foundation for sovereign AI initiatives. By partnering with top-tier local data centers like <a href="https://patmos.tech/" rel="noopener noreferrer" target="_blank">Patmos</a> and <a href="https://www.sesterce.com/blog/hydra-host-has-chosen-sesterce-as-its-cluster-solutions-provider" rel="noopener noreferrer" target="_blank">Sesterce</a>, we ensure your data remains within jurisdictional boundaries, untouched by foreign interference.&nbsp; <h3><a href="https://brokkr.hydrahost.com/" rel="noopener noreferrer" target="_blank">Brokkr SaaS Platform</a></h3>The Brokkr platform is the solution for provisioning and managing bare metal HPC hardware. The lack of virtualization allows the sidestepping of GPU and InfiniBand passthrough, simplifying optimization of complex AI training and inference workloads. This also makes it a much more stable solution for deploying higher level orchestration software such as container based workloads, radically simplifying the stack.&nbsp;&nbsp; <h3><a href="https://hydrahost.com/nvidia/a100-pcie-40-gb/" rel="noopener noreferrer" target="_blank">High-Performance GPU Offerings</a></h3>Hydra provides access to cutting-edge GPU clusters that cater to high-performance computing needs in data centers from Thailand, India, Sweden, and the United States. These GPUs are designed to handle intensive AI workloads, ensuring rapid processing and superior performance. By leveraging our GPU offerings, organizations can accelerate their AI initiatives and achieve significant advancements in their operations.&nbsp; <h3><a href="https://docs.hydrahost.com/brokkr-api/xtfWAGw7Eg2QdGXmTVgmTf/brokkr-api-documentation/onRPgMPir5L8skNV3wVKji" rel="noopener noreferrer" target="_blank">API for Sovereign AI</a></h3>Our newly launched API is crucial in supporting sovereign AI. It provides a standardized, secure way to manage, access, and control AI services and data across our entire rapidly growing data center network. This ensures control over AI models and data, facilitates data localization by keeping data within jurisdictional boundaries, and integrates seamlessly with existing infrastructure for improved interoperability. With robust security protocols, our API protects sensitive data and maintains compliance with data sovereignty requirements. This enables organizations to expand their AI capabilities while maintaining strict control over their infrastructure and data.&nbsp;&nbsp;<h2>Conclusion&nbsp;</h2> Hydra Host's suite of solutions, including our CaaS platform, Brokkr SaaS, high-performance GPU offerings, and API, is a game-changer in the quest for sovereign AI. By leveraging bare metal solutions, organizations can secure their data, optimize performance, and maintain control. These are key elements for effective autonomous governance and strategic decision-making in today's digital economy. As the demand for sovereign AI grows, Hydra Host is dedicated to providing the infrastructure and expertise necessary to support these critical initiatives.

Learn how to achieve Sovereign AI with Hydra Host’s bare metal compute. Ensure data sovereignty, AI security, and infrastructure autonomy.

Embracing Sovereign AI with Hydra Host's Bare Metal Compute | Data Sovereignty and AI Security

<a href="https://bitcoinmagazine.com/technical/compute-maximalism-the-symbiosis-between-bitcoin-mining-and-ai" rel="noopener noreferrer" target="_blank">Initially published at Bitcoin Magazine</a> The story of human progress can be simplified into the story of increasing energy utilization. We harness energy to create order, both in terms of biology and society. Energy surpluses allow for every form of wealth creation, which in turn produce new technologies to effectively harness yet more energy. This truth has inspired famous concepts such as the Kardashev scale, which measures civilizations by their ability to harness energy resources towards useful ends. Compute is a natural continuation of this endeavor. Modern digital technology transforms ever greater quantities of electricity into advanced value-creating processes. The most recent surge in demand for compute has come from two technologies in particular: bitcoin mining and, more recently, high performance compute (“HPC,”) in particular the use of Graphics Processing Units (“GPUs”) for artificial intelligence. The meteoric rise in energy consumption by these technologies have raised many questions: What impact will these power-hungry technologies have on our energy systems? Given their mutually voracious use of energy, what interplay will they have with each other? What do these developments mean for humanity? We explore the essential characteristics of these respective technologies, and how they offer alternative markets for excess power that can in fact improve the efficiency of energy systems. Based on this exploration, we also argue that bitcoin mining and HPC are complementary rather than competitive. As we’ll see, their respective trade offs offer a symbiotic ability to maximize the value created from energy resources, which in turn benefits society as a whole. In short, we argue for compute maximalism. <h2>Energy</h2> Modern technology depends on energy converted from a wide array of sources into electricity, and this comes with certain challenges and tradeoffs. The primary of which is limited portability. This is due to several simple realities. Electricity requires a grid, essentially a massive series of circuits which transports energy in real time. The grid must remain in balance, meaning the generation must roughly equal demand at any point in time. This is difficult for two reasons: First, energy resources are not always conveniently distributed, have long lead times to develop, and have varying degrees of dispatchability. Second, transmission as well as storage are both expensive, have similarly long lead times, and experience inherent inefficiencies. An estimated <a href="https://demo.openeclass.org/modules/document/file.php/DEMO-A1153/%CE%A3%CE%B7%CE%BC%CE%B5%CE%B9%CF%8E%CF%83%CE%B5%CE%B9%CF%82%20%CF%80%CF%81%CE%BF%CF%82%20%CE%BC%CE%B5%CE%BB%CE%AD%CF%84%CE%B7/EFFICIENT%20ELECTRICAL%20ENERGY%20TRANSMISSION%20AND%20DISTRIBUTION%20.pdf" rel="noopener noreferrer" target="_blank">8-15%</a> of electricity is lost to transmission and distribution losses by the time it reaches local consumers, and this figure is even higher for long term battery storage. The result is that it will always be cheaper and more efficient to consume generated electricity immediately at the source, than to transport it over time or space. As such, the most efficient solution isn’t to more widely and inefficiently transport electricity to where it can be used, but to move use cases to the electricity. Compute is an ideal use case for such excess electricity because it is power dense, largely portable, and scalable; we are yet to find a limit on our demand for compute.Meanwhile “meatspace” constraints are strong limiting factors for legacy forms of energy sinks like aluminum smelting and manufacturing. Bitcoin mining in particular has emerged as such an ideal use case for local surplus power, providing a dispatchable and revenue-generating load to balance the grid. More recently the demand for High Performance Compute, in particular GPUs, is also having unignorable impacts on the utilization of energy as well. Many are expecting these two technologies to compete over the same energy resources, but as we explore the characteristics of each, the potential symbiosis will become self-evident. <h2>Bitcoin Mining</h2> Bitcoin mining can be thought of as a permissionless energy sink. Bitcoin’s proof-of-work consensus mechanism amounts to proof of energy-intensive computation. Miners must perform this energy-intensive computation to create new blocks of transactions, thereby earning bitcoin as a reward. It is this proof-of-work that provides global settlement assurances in a decentralized and permissionless way. In practice, this looks like millions of computers (these days, application specific integrated circuits or “ASICs”) running in bare bones data centers around the world. One of the beautiful things about bitcoin mining is its permissionless nature; anyone anywhere in the world can plug in an ASIC. In effect, Bitcoin allows miners around the world to participate in a global energy market; whoever has the lowest cost of power has the highest margin. This global decentralized network is part of the reason why Bitcoin’s adoption has steadily continued, as people seek a new monetary and financial system that is active 24/7, lacks a single point of failure, and sidesteps the perverse incentives of politically captured central bank monopolies. Bitcoin mining is distinguished by the following characteristics relative to GPU/HPC infrastructure: <ul><li>No customers</li><li>No customer acquisition</li><li>No support</li><li>High interruptibility</li><li>Low operational complexity</li><li>Low connectivity requirements (Less than 100MB/s)</li><li>Low margin (generally)</li></ul> <h2>HPC</h2> Data center GPUs are the latest form of HPC, the demand for which has exploded in the previous 2 years due to quickly escalating interest in AI/ML breakthroughs which rely on it. These technologies have unlocked whole new categories of digital operations and functions which were not previously possible, with the resulting use cases only just beginning to be explored. The sudden explosion in interest in these technologies has quickly made NVIDIA, the leading manufacturer of the underlying GPUs, the most valuable company in the world. Initially this sudden spike in demand created an intense bottleneck in the production of sufficient units of GPU itself. This however was temporary and over time continues to be alleviated by increased production, with focus quickly switching to a new bottleneck: Data center rack space with cheap power. The result has been an explosion in new data center build outs, wherever a large amount of steady power can be sourced. This has brought GPU infrastructure into competition with Bitcoin mining in many areas with excess local power. Relative to Bitcoin mining, GPU/HPC has these is distinguished by these characteristics: <ul><li>Customers</li><li>Customer acquisition</li><li>Customer support</li><li>Low interruptibility</li><li>High operational complexity</li><li>High connectivity requirements (10 - 100GBs)</li><li>High margin (generally)</li></ul> <h2>Complimentary Competition</h2> The demand for both Bitcoin and AI/ML technologies has taken off in the last decade, a testament to their utility to society. This demand has led to the proliferation of their respective compute resources. To reduce operating costs, both markets seek excess power to utilize as it tends to be cheaper. This naturally resolves some of the grid inefficiencies discussed above, but it does mean that data center builders and operators will find themselves asking which form of compute to support and invest in for the same amount of available power. Both forms of compute are energy intensive and relatively location-agnostic (barring legal or jurisdictional considerations beyond the scope of this paper) bringing them into seeming competition, but they can in fact be highly complementary tools for maximizing utilization and profit from such excess or stranded electricity. GPU workloads have higher operational complexity, and low interruptibility, as well as higher upfront capital investment. That makes it a poor choice for taking advantage of transient surpluses of power, such as the peak window of energy production by solar panels for instance. Unlike bitcoin mining, GPUs have customers, who are typically sensitive to issues such as uptime and availability. There are exceptions, such as spot instances and frameworks which can failover from such instances, but generally speaking due to the existence of a customer the interruptibility tolerance of GPU infrastructure will never match that of bitcoin mining. Coupled with the higher capital costs and complexity, in these situations we can expect bitcoin mining to continue to grow and dominate as a highly flexible, dispatchable load to the grid. Consistent excesses in power on the other hand, such as a largely fixed delta between the base generation capacity of hydropower or nuclear sites and their surrounding consumption, are ideal opportunities for GPU infrastructure to close the gap and establish new baseline consumption and equilibrium. These situations favor the low interruptibility of GPU infrastructure, and justify the added expenditure and operational complexity in order to secure substantially higher revenues. So long as the supporting bandwidth is available to facilitate GPU workloads (at least 10GB/s, ideally 100GB/s), these sites will always provide more profit opportunity than if allocated exclusively for bitcoin mining. <h2>Hybrid data center Strategies</h2> There are also strategies which can utilize both technologies in tandem to maximize revenue and return on investment. First, bitcoin mining could be used as an initial load for energy resources before the site is suitable for high performance compute. Examples include: (1) using semi-portable modular bitcoin mining data centers to monetize power while the remaining infrastructure for an HPC data center (redundant power/internet lines, buildings, backup energy systems, etc.) is built; or (2) pioneering stranded energy resources with bitcoin mining, some of which may eventually be used for HPC. In fact, <a href="https://bitcoinmagazine.com/tags/core-scientific" rel="noopener noreferrer" target="_blank">Core Scientific</a>’s recently announced deal with CoreWeave could be viewed as an example of this occurring in the wild, as bitcoin mining led to the development of a large substation and data center shell that would eventually be used for HPC. A second, more advanced strategy is to co-mingle HPC and Bitcoin mining workloads in tandem, using Bitcoin mining as a counter weight to balance fluctuations in HPC workload power draws. While HPC loads require reliable power, “inferencing workloads” which host production AI/ML models can fluctuate based on levels of real time use by users, leading to typical cycles of high activity and power consumption and low activity and lower power consumption. To date, the value for such HPC has substantially outweighed any inefficiencies from fluctuating power use, but the highly flexible and interruptible nature of Bitcoin mining can be used to provide stable power draw and in turn lower effective power rates, in addition to providing additional revenue for the data center overall. Some are describing this strategy as a “mullet data center,” with AI in the front and bitcoin in the back. While it is still early, this approach promises to utilize the best of both HPC and bitcoin mining to offer the most value maximized data center deployments possible with current technology. <h2>Industry Implications</h2> Until recently, the data center industry has been dominated by colocation providers. These providers build the facilities used to host industrial servers, and lease out space, power, connectivity, and sometimes the servers themselves to tenants. Traditionally, the majority of these tenants have been large enterprise and hyperscale cloud providers. In many cases these hyperscale and enterprise tenants have also built their own data centers to support their own growth. Since roughly 2017 bitcoin mining has truly entered the picture at an industrial level, with entire data center complexes being built solely to support Bitcoin mining in areas with extreme deltas in produced and consumed electricity. Now in 2023 and 2024 we’ve seen shifts in the market even more notable and disruptive. With the explosion in demand for GPU infrastructure, many former colocation focused data centers have ventured into buying and hosting this GPU infrastructure themselves. Meanwhile hyperscalers are moving behind-the-meter to co-locate with large baseload power plants, seeking cheap reliable power for the new surge in HPC demand. This is particularly notable as intermittent renewables have been the most popular form of generation in recent years, primarily due to government subsidies. We anticipate the following: 1. Continued increase in energy demand for both forms of compute. 2. New data center construction as the next bottleneck of expanding HPC footprints, with large swaths of bitcoin mining facilities being repurposed for higher margin use cases. 3. Mining hardware will relocate to the fringes, seeking remote locations and variable inefficiencies that HPC workloads are ill-suited to monetize. 4. Co-mingling of both bitcoin mining and HPC in “mullet data centers” will leverage the high revenue potential of HPC and the flexible nature of bitcoin mining to effectively balance power draw and local grids, while outcompeting traditional data center strategies. <h2>Conclusion</h2> When new power-hungry technologies emerge, there is often concern about their energy utilization and its externalities. Bitcoin mining and HPC are no exceptions with politicians and arm-chair technologists alike crying for their mitigation or control. But such voracious technologies represent the natural trend of human progress. In addition to the self-evident utility provided by the Bitcoin settlement network and AI/ML workloads, we can demonstrate that they can be deployed in ways that efficiently maximize new and existing energy resources to useful economic ends.

Drew Armstrong

Discover how Bitcoin mining and GPUs drive energy efficiency and progress. Learn about hybrid data centers and strategies for compute maximalism.

Compute Maximalism: The Synergy of Bitcoin Mining and AI | Hydra Host

<h1>The Power of Cluster as a Service (CaaS)&nbsp;</h1> Cluster as a Service (CaaS) from Hydra Host provides easy access to GPU clusters in our partner data centers, enabling you to attain the highest levels of control and performance for your applications, reducing the challenges of cluster management.&nbsp; Clusters are like exotic cars: when they work, it is amazing; when they don't, it's a headache. Our experienced team has developed and managed clusters worldwide with success stories, and we are expanding this further.&nbsp;<h2> </h2><h2>Introducing Cluster as a Service (CaaS)&nbsp;</h2> Cluster as a Service is a fully integrated model where servers are provisioned, managed, and scaled as a single unit by our high-performance computing experts. This service provides a scalable, flexible infrastructure for running distributed applications, with the expertise needed to configure and tune the infrastructure to your specific needs while ensuring optimal end-user performance.&nbsp; With CaaS, the complexity and high-touch work of managing HPC hardware, storage, and networking are done for you, letting you focus on building and deploying your applications.&nbsp; <h3>Key features of CaaS include:&nbsp;</h3> <ul><li>Provisioning and Management: The service provider provides and manages the cluster, so manual configuration and management are not required.&nbsp;</li></ul> <ul><li>Scalability: The cluster can scale up or down as needed, providing resource elasticity and optimized costs.&nbsp;</li></ul> <ul><li>Abstraction: This abstracts the underlying cluster infrastructure from the user, giving a simple and easy-to-use interface for deploying and managing applications.&nbsp;</li></ul> <h3>Benefits of CaaS&nbsp;</h3> <ul><li>Faster Time-to-Market: Developers can deploy and scale applications faster using CaaS, which would mean reduced time-to-market and an increase in competitiveness.</li></ul> <ul><li>Cost-Effective: The worst secret in this industry is that clusters are complicated to manage and maintain. Utilization rates and bug frequency are abysmally poor. That said, with a veteran team of experts available to provide prompt diagnosis and resolution, CaaS quickly pays for itself and saves you both precious time and resources.&nbsp;</li></ul> <ul><li>Scalability: CaaS infrastructure can scale up or down based on load requirements; it can also hold big data and real-time analytics. This enables businesses to manage diverse workloads in a way that is optimal for the highest performance for applications such as data processing, machine learning, and large-scale simulations.&nbsp;</li></ul> <ul><li>Flexibility: CaaS provides a flexible infrastructure that can be used for a wide range of applications, from model training to inference workloads. This versatility allows businesses to adapt their infrastructure to various needs, including data analytics, software development, and high-performance computing tasks.&nbsp;&nbsp;</li></ul> <h3>CaaS Offering: Hydra Host&nbsp;</h3> We are proud to be among the first to introduce Cluster as a Service. The new CaaS platform offers direct rental of GPU clusters from the data center, maintaining ultimate control and top-level performance. We want to make cluster management more accessible for you on our reliable and fast platform.&nbsp; <h3>Why Use Hydra Host for CaaS?&nbsp;</h3> Hydra Host is the most eminent leader in the CaaS market. Unlike the others, we provide direct access to GPU clusters at bare metal, an option with no match in performance and control.&nbsp; Key Advantages:&nbsp; <ul><li>More Control: You can rent GPU clusters directly from our data centers while having complete control over your infrastructure.&nbsp;</li><li>High Performance: We maximize high performance in our clusters, allowing your applications to run seamlessly and efficiently.&nbsp;</li><li>Experienced Team: Over the years, our professional team has continued to excel in the management of GPUs globally, ensuring that you benefit from superb support and expertise.&nbsp;</li></ul> <h3>Compare Hydra Host CaaS vs. Microsoft Azure vs. AWS (Amazon Web Services) EKS&nbsp;</h3> Though Microsoft Azure and Amazon EKS present Cluster as a Service solution for potential customers, these few differences outline why Hydra Host offers superior benefits.&nbsp;&nbsp; <h4>Direct Bare Metal Access:&nbsp;&nbsp;</h4> Hydra Host: Bare metal servers provide maximum effectiveness and speed for your applications.&nbsp; Microsoft Azure: Runs on a virtualized environment mostly, introducing overhead and performance variability.&nbsp; Amazon EKS: Virtualized infrastructure can affect performance consistency and efficiency.&nbsp; <h4>Improved Control and Customization:&nbsp;</h4> Hydra Host: Your infrastructure is as flexible and custom as it gets. Our GPU marketplace offers options, from NVIDIA reference architectures to completely bespoke builds for precise use cases. Consult with us to find the perfect configuration that meets your needs; do not settle for a one-size-fits-all approach from legacy cloud providers.&nbsp; Microsoft Azure: Reduces the ability to fully optimize unique workload requirements as it limits the depth of customization compared to direct access on bare metal.&nbsp; Amazon EKS: Provides a solid platform but also restricts hardware-level customizations, which might hold back the performance for some critical, specialized tasks.&nbsp; <h4>Cost-Effective and Transparent Pricing:&nbsp;</h4> Hydra Host: Simplified reservation-based model means no hidden costs and consequently an assured aspect of transparency and predictability of budgeting.&nbsp; Microsoft Azure: The pricing is sometimes complicated due to additional costs for various underlying services, complicating the budget.&nbsp; Amazon EKS: Other charges for the AWS services in which it is embedded might add some confusion to its cost management and predictability.&nbsp; CaaS will redefine your infrastructure with Hydra's new platform. Unlock the potential of your applications with scalable, flexible, and cost-effective bare metal&nbsp;GPU clusters.&nbsp;Hydra provides instant access to bare metal servers, ensuring maximum efficiency and top performance without virtualization overhead. Our platform offers better control and flexibility, aligning configurations with your workload. Transparent, pay-as-you-go pricing eliminates hidden costs, making budgeting straightforward. Our expert team manages GPU clusters globally, ensuring optimized performance and quick issue resolution. Whether Big Data, real-time analytics, or machine learning, our CaaS solution sets you up for success. Discover the power of Cluster as a Service with Hydra Host and join us in leading the next wave of infrastructure innovation.&nbsp;&nbsp;&nbsp;

Discover why using Cluster as a Service (CaaS) transforms infrastructure with scalable GPU clusters, bare-metal access, and expert management.

The Power of Cluster as a Service (CaaS)

<h1>Affordable GPU Systems for AI Inferencing: Top Picks and Benefits&nbsp;</h1> As artificial intelligence applications become increasingly integrated into real-world processes, the demand for efficient model inferencing — the stage where trained models make predictions on new data — grows significantly. This crucial phase requires high-speed, low-latency processing capabilities to ensure timely and accurate predictions. Hydra Host's Brokkr platform addresses this need by offering ultramodern GPUs designed to deliver fast and reliable inferencing performance. In this article, we explore the best GPU systems tailored for inferencing tasks.&nbsp; The Role of GPUs in Inferencing&nbsp;Inferencing is critical in deploying AI models effectively, as it determines how quickly and accurately a model can respond to new data. GPUs are essential in this phase because they can process multiple computations simultaneously. This parallel processing capability is crucial for reducing latency and increasing throughput, ensuring that inferencing tasks are efficiently performed in real time.&nbsp; Top GPUs for Inferencing&nbsp;Hydra Host offers access to premier GPUs ideal for inferencing's rigorous demands. These GPUs are selected for their powerful processing capabilities, energy efficiency, and cost-effectiveness, making them suitable for a wide range of applications:&nbsp;<h3> </h3><h3><a href="https://hydrahost.com/nvidia/geforce-rtx-4090/" rel="noopener noreferrer" target="_blank">NVIDIA RTX 4090:</a></h3> Compute Power: The RTX 4090 stands out for its exceptional inferencing capabilities, equipped with cutting-edge architecture that includes new Tensor Cores optimized for AI workloads. This GPU is designed to handle demanding real-time applications requiring rapid data processing.&nbsp; Memory Capacity: With a substantial amount of GDDR6 memory, the GPU efficiently manages large datasets and complex AI models. This capacity is essential for ensuring smooth and fast inferencing performance.&nbsp; Compatibility: The RTX 4090 offers extensive support for various AI frameworks and applications, catering to the diverse needs of developers and researchers across different AI projects. Its compatibility with popular software guarantees effortless integration into existing workflows.&nbsp; Pricing: The RTX 4090 is positioned as a high-end consumer GPU, offering impressive performance at a competitive price compared to enterprise-level GPUs. This makes it a cost-effective choice for high-demand applications.&nbsp; Suitability: Well-suited for demanding applications like interactive AI services, real-time medical imaging, and complex data analytics that necessitate rapid inferencing.&nbsp; <h3>&nbsp;&nbsp;</h3><h3><a href="https://hydrahost.com/nvidia/a100-pcie-40-gb/" rel="noopener noreferrer" target="_blank">NVIDIA A100 PCIe:</a>&nbsp;</h3> Compute Power: The A100 PCIe is engineered to provide a balanced solution for various inferencing tasks, from moderate to highly demanding. It features 6912 CUDA cores and offers up to 19.5 teraflops of single-precision (FP32) performance, making it a powerhouse for AI workloads.&nbsp; Memory Capacity: This GPU includes a high-bandwidth memory capacity that allows it to easily handle large-scale AI models and datasets, ensuring efficient and accurate inferencing.&nbsp; Compatibility: The A100 PCIe supports multi-instance GPU (MIG) technology, allowing it to run multiple inferencing tasks simultaneously. Its broad compatibility with AI frameworks makes it suitable for various industries.&nbsp; Pricing: As an enterprise-grade GPU, the A100 PCIe commands a higher price, reflecting its advanced features and capabilities. It is an investment for businesses needing top-tier performance and versatility.&nbsp; Application Range: This GPU is exceptionally effective in both cloud and on-premises environments, capable of scaling as inferencing demands grow, making it a versatile choice for industries like telecommunications, finance, and healthcare.&nbsp;<h3> </h3><h3><a href="https://hydrahost.com/nvidia/rtx-a6000/" rel="noopener noreferrer" target="_blank">NVIDIA RTX A6000:</a>&nbsp;</h3> Compute Power: Specifically designed for professional and enterprise deployment, the RTX A6000 offers robust performance capabilities, essential for tasks requiring precise and reliable inferencing.&nbsp; Memory Capacity: It comes with 48 GB of GDDR6 memory, enabling it to manage extensive datasets and complex AI models with ease. This is critical for detailed inferencing tasks such as high-resolution image processing and large-scale simulations.&nbsp; Compatibility: The RTX A6000 is designed for seamless integration into enterprise environments, supporting a wide range of AI applications and frameworks. Its high compatibility ensures it can meet the needs of various professional use cases.&nbsp; Pricing: The RTX A6000 is priced competitively within the enterprise GPU market. It offers a balance between cost and performance, making it a strong contender for businesses requiring reliable and efficient inferencing power.&nbsp; Performance Efficiency: The A6000 not only delivers high-end performance but also emphasizes energy efficiency and reliability. This focus is crucial for sustaining operations in enterprise environments where 24/7 application availability is mandatory.&nbsp; &nbsp;&nbsp;Choosing the Right GPU for Your Inferencing Needs&nbsp;Selecting a GPU for inferencing involves considering several critical factors:&nbsp; <ul><li>Performance Needs: Assess the complexity and volume of the data to be processed to determine the required computational power.&nbsp;</li><li>Budget Constraints: Balance the performance needs with budgetary limits, choosing a GPU that provides the best return on investment.&nbsp;</li><li>Energy Consumption: For continuous operations, consider the energy efficiency of the GPU to minimize operational costs.&nbsp;</li></ul> Exploring Bare Metal Solutions and Hydra Host for Inferencing&nbsp; In an era where cloud computing often dominates the conversation, it's crucial to recognize the unparalleled advantages of bare metal infrastructure. Bare metal servers provide dedicated hardware resources, eliminating the latency and performance issues associated with shared environments. For businesses demanding speed, security, and control, bare metal is the definitive choice.&nbsp; We believe in the power of bare metal. Our Brokkr platform seamlessly integrates top-tier GPUs directly with bare metal servers from independent data centers. This approach offers superior performance, reliability, and security, far surpassing the limitations of traditional cloud services.&nbsp; <ul><li>Performance: Bare metal servers deliver consistent, high-speed processing power, free from the overhead and unpredictability of virtualized environments. This ensures your AI models perform inferencing tasks with maximum efficiency and minimal latency.&nbsp;</li><li>Control: With dedicated hardware, you keep complete control over your environment, allowing for customized configurations and optimizations specific to your workloads. This level of control is essential for fine-tuning performance and ensuring data sovereignty.&nbsp;</li><li>Cost-Effectiveness: By bypassing the premiums and hidden costs of cloud services, bare metal solutions offer a more transparent and economical option for accessing high-performance GPU resources. This ensures you get the best return on your investment without sacrificing quality or capability.&nbsp;</li></ul>&nbsp;Hydra Host is committed to providing innovative bare metal solutions that empower businesses to harness the full potential of AI inferencing. With Brokkr, you unlock the raw power of dedicated GPUs, enabling faster,&nbsp;more reliable AI operations tailored to your specific needs.&nbsp; &nbsp;&nbsp;Conclusion&nbsp;Efficient inferencing is pivotal for the success of AI applications in real-world scenarios. Hydra Host's GPU offerings allow organizations to access top-tier inferencing capabilities that drive faster, more effective AI operations. Whether you are involved in healthcare, finance, or any sector relying on quick data processing, choosing the right GPU system is crucial.&nbsp;

Explore top GPU systems for AI inferencing with Hydra Host. Learn about RTX 4090, A100, and A6000 GPUs for high-performance, low-latency tasks.

Top Affordable GPU Systems for AI Inferencing

<h1>Top Affordable GPU Systems for Training&nbsp;</h1> In the competitive realm of AI, the efficacy and efficiency of training models are pivotal. This exploration, influenced by the foundational needs of training across various applications, highlights Hydra Host's offerings, reshaping how professionals approach this crucial phase of AI development.&nbsp;The Fundamental Role of GPUs in Training&nbsp; Training models, whether for AI, data analytics, or complex simulations, involve teaching a system to make decisions or predictions based on data. This training is computationally intensive because it requires numerous iterations over extensive datasets to optimally adjust the model's internal parameters. Here, the architecture of GPUs becomes critical due to their ability to perform parallel processing, which significantly accelerates the computational tasks involved in training.&nbsp;GPUs are designed to handle multiple operations simultaneously, vastly reducing the time required for each training cycle. This capability is essential for efficiently managing the large-scale matrix operations and data transformations typical in training scenarios. By enabling faster computation times, GPUs allow for more iterations and deeper model refinement within practical time frames.&nbsp; Hydra Host's Optimal GPUs for Training&nbsp; Hydra Host leverages its Brokkr platform to offer some of the most advanced GPUs available, tailored for the rigorous demands of training various applications. Each GPU option optimizes performance and cost-effectiveness, ensuring users can handle intensive training tasks efficiently. Here is a detailed look at three top GPUs available through Hydra Host:&nbsp; <a href="https://hydrahost.com/nvidia/h100-sxm5-80-gb/" rel="noopener noreferrer" target="_blank">NVIDIA H100 SXM</a>&nbsp; Compute Power: The H100 SXM is a powerhouse in the GPU market, designed to tackle the most demanding computational tasks. It features an advanced AI-enhanced architecture that significantly boosts training capabilities, making it ideal for highly complex model training.&nbsp; Memory Capacity: This GPU excels in double-precision tasks, delivering up to 60 teraflops of FP64 performance, which is crucial for data-intensive applications such as simulation and scientific computing. Additionally, it includes new DPX instructions for dynamic programming, enhancing performance in applications like genomic sequencing.&nbsp; Compatibility: The H100 SXM is best suited for environments where cutting-edge AI training is critical, such as in research institutions and high-tech industries where speed and accuracy are paramount.&nbsp; Pricing: Positioned as a high-end GPU, the H100 SXM offers unmatched performance at a premium price, making it an investment for those needing top-tier capabilities.&nbsp; <a href="https://hydrahost.com/nvidia/a100-sxm4-40-gb/" rel="noopener noreferrer" target="_blank">NVIDIA A100 SXM</a>&nbsp; Compute Power: Known for its versatility, the A100 SXM balances performance and energy efficiency. It supports a wide range of precision formats, catering to different training precision needs from FP32 to TF32, enabling it to handle diverse training scenarios.&nbsp; Memory Capacity: It boasts a high CUDA core count and offers up to 312 teraflops of performance with its Tensor Cores in TF32 precision mode, accelerating the training process dramatically.&nbsp; Compatibility: The A100 SXM is particularly effective in commercial and academic settings where large models and datasets are common. Its ability to reduce training times makes it valuable for projects requiring rapid iteration and deployment.&nbsp; Pricing: As an enterprise-grade GPU, the A100 SXM is priced accordingly, offering significant capabilities for businesses and institutions needing robust performance.&nbsp; <a href="https://hydrahost.com/nvidia/l40s/" rel="noopener noreferrer" target="_blank">NVIDIA L40S</a>&nbsp; Compute Power: The L40S is engineered for specialized training needs, offering efficient processing and robust memory management. It provides a balanced performance suitable for training tasks that do not require the extreme power of the H100 or A100 but still demand high reliability and precision.&nbsp; Memory Capacity: With a focus on energy efficiency and cost-effectiveness, the L40S is an excellent choice for organizations looking to optimize their training operations without overspending.&nbsp; Compatibility: Ideal for small to medium-sized enterprises or startups that require dependable performance for training less complex models, the L40S provides a practical solution that aligns with budget considerations and performance needs.&nbsp; Pricing: The L40S offers a cost-effective option for those needing reliable performance without the higher costs associated with top-tier GPUs.&nbsp; Choosing the Right GPU for Your Training Needs&nbsp; Selecting an appropriate GPU for training involves considering several critical factors:&nbsp; <ul><li>Compute Power: Assess the computational demands of your training tasks to determine the necessary GPU power.&nbsp;</li><li>Memory Capacity: Consider the data size and model complexity to choose a GPU with adequate memory.&nbsp;</li><li>Energy Efficiency: Evaluate energy consumption to manage operational costs effectively.&nbsp;</li><li>Price: Balance the performance needs with your budget to find the most cost-effective solution.&nbsp;</li><li>Compatibility: Ensure the GPU is compatible with your existing systems and software.&nbsp;</li></ul> Embracing Bare Metal Solutions for AI Training&nbsp; While cloud computing often takes the spotlight, it is essential to recognize the significant benefits of bare metal infrastructure. Bare metal servers provide exclusive access to hardware resources, eliminating the latency and performance issues common in shared environments. For businesses prioritizing speed, security, and complete control, bare metal servers are the optimal choice.&nbsp;At Hydra Host, we champion the advantages of bare metal. Our Brokkr platform integrates cutting-edge GPUs directly with bare metal servers sourced from independent data centers. This strategy offers exceptional performance, reliability, and security, outstripping the capabilities of conventional cloud services.&nbsp; Performance: Bare metal servers deliver unparalleled, high-speed processing power. Without the overhead and unpredictability of virtualized environments, your AI models can perform training tasks with maximum efficiency and minimal latency, leading to quicker and more accurate results.&nbsp; Control: Utilizing dedicated hardware means you retain complete control over your environment. This allows for tailored configurations and optimizations specific to your training needs, ensuring your systems are fine-tuned for peak performance and data sovereignty.&nbsp; Cost-Effectiveness: Hydra Host's bare metal solutions eliminate the high premiums and hidden costs often associated with cloud services. This approach provides a more transparent and economical way to access high-performance GPU resources, ensuring you receive the best return on your investment without compromising on quality or capability.&nbsp; Hydra Host is dedicated to delivering state-of-the-art bare metal solutions that empower businesses to fully leverage the potential of AI training. With Brokkr, you gain access to the raw power of dedicated GPUs, enabling faster, more reliable AI operations customized to your specific requirements.&nbsp;&nbsp;&nbsp; Conclusion&nbsp;Training is a critical phase in AI development, and the quality of the training can significantly influence the overall performance of the AI model. With Hydra’s access to affordable, robust GPU systems through the Brokkr platform, AI professionals and organizations can achieve superior training outcomes without compromising cost or efficiency. This accessibility is crucial for fostering innovation and advancement in AI, making innovative technology attainable for a broader range of developers and researchers.&nbsp; Discover how Hydra Host can empower your training projects by exploring our Brokkr Marketplace.&nbsp;

Discover top affordable GPUs for AI training. Maximize performance and efficiency with Hydra Host’s bare metal solutions. Compare today!

Top Affordable GPU Systems for Training 

The GPU computing wars aren't just between AMD, Intel, and NVIDIA. This war isn't just between the imminent public cloud GPUs and NVIDIA. Nation-states are joining the mix because computing power and AI will define the next ten years, and pre-existing geo-political trends will push the GPU market to fragment.&nbsp;&nbsp;One of the unintended consequences of US restrictions on GPUs has been the rapid escalation of the <a href="https://www.theinformation.com/articles/chinese-regulators-tell-local-tech-firms-to-buy-fewer-nvidia-chips" rel="noopener noreferrer" target="_blank">GPU arms race</a>. These restrictions have pitted countries against each other and against international cloud companies and NVIDIA. The US government's <a href="https://www.reuters.com/technology/upcoming-us-rules-ai-chip-exports-aim-stop-workarounds-us-official-2023-10-15/" rel="noopener noreferrer" target="_blank">decision to annex NVIDIA</a> as a national security asset, a move many consider unconstitutional, has further fueled this race, undermining the world's confidence in NVIDIA's ability to transact freely.&nbsp;&nbsp;The fear of falling behind in the AI race is about more than losing technological prowess. It's a fear that power, stability, and even national defense could be at stake. The concept of "Bring your own GPU" (BYOG) is positioned to dominate the global computing race, leading to a rapid fragmentation with prevailing players in both the hardware and GPU rental space. This global GPU footprint will mirror broader geo-political relationships that defined the world pre-H100 and A100. Moreover, the potential for several countries to misuse AI against their own citizens is a reality we cannot ignore.&nbsp;

At Hydra Host, we are betting on data centers. A bet on data centers is a bet on NVIDIA. As competition for accelerators and GPUs heats up, NVIDIA will return to its reliable moat and bread and butter: data centers. As the public cloud pursues its budget GPUs and nation-state subsidies for commodity GPU alternatives, NVIDIA will remain the king and continue growing its data center footprint as the data center industry has no interest or capability of building its own GPU. You can see this clearly with NVIDIA's push towards fully incorporated builds like the GH200 and GB200.&nbsp;&nbsp;GPUs, being naturally agnostic and with a limited-to-zero need for associated cloud services, broke the back of a public cloud. There was no longer a need to stay locked in with these mega-cloud companies. GPU sets the computing world free. As a result, priorities have shifted, and non-GPU considerations are driving the global battle for data center capacity and GPU computing.&nbsp;&nbsp;Aaron Ginn, CEO

Explore the GPU computing wars shaping AI and geopolitics. Learn how NVIDIA, cloud providers, and nation-states compete for global GPU dominance.

Navigating the GPU Computing Wars

Hydra Host and Sortium Forge Strategic Partnership to Enhance AI-Driven 3D Content Creation&nbsp; Hydra Host, a trailblazer in data center operations and bare metal provisioning, is excited to announce a groundbreaking partnership with Sortium, an AI-powered 3D asset and texture generation innovator. This collaboration aims to integrate Hydra Host's robust digital infrastructure solutions with Sortium's innovative AI technology to revolutionize the creation and deployment of 3D content across various industries.&nbsp; &nbsp;&nbsp; Hydra Host: Pioneering Data Center Solutions&nbsp; Hydra Host stands at the forefront of redefining data center operations by offering solutions prioritizing autonomy, efficiency, and security for managing digital infrastructure. Their products, such as Brokkr, facilitate seamless server management and bare metal provisioning, ensuring high performance and reliability for data center clients and their end-users.&nbsp; &nbsp;&nbsp; Sortium: Transforming 3D Content Creation&nbsp; Sortium empowers creators with AI-driven tools to generate and modify professional-grade 3D assets and textures in seconds, streamlining the creative process and ensuring IP rights protection. This technology is accessible via the web or integrated directly into popular 3D software and game engines, offering limitless content creation possibilities.&nbsp; &nbsp; “Hydra Host has been our secret weapon in the world of GPU scaling. Offering an incredible partnership and industry best solutions to scale quickly through an intuitive global marketplace of compute.”&nbsp;&nbsp; - Marc Seal, CEO &amp; Co-founder of Sortium&nbsp; &nbsp;&nbsp; A Joint Effort Driving Innovation and Accessibility&nbsp; The synergy between Hydra Host and Sortium will leverage both companies' advanced digital infrastructure and AI capabilities to offer an unparalleled ecosystem for 3D content creators, game developers, and businesses. This partnership is poised to:&nbsp; &nbsp;&nbsp;<ul><li>Accelerate 3D Content Development: By providing robust infrastructure and AI-powered tools, the partnership facilitates faster development and deployment of 3D content.&nbsp;</li></ul> <ul><li>Enhance Creative Freedom: Artists and developers can utilize Sortium's technology on Hydra Host's secure and reliable platform, focusing on innovation without compromising performance or security.&nbsp;</li></ul> <ul><li>Expand Market Opportunities: This collaboration opens new avenues for businesses to explore AI-driven 3D content creation, leveraging the strengths of both Hydra Host and Sortium to tap into new markets and customer segments.&nbsp;</li></ul> Through this strategic partnership, Hydra Host and Sortium are setting new standards in AI-driven 3D content creation, fostering innovation, and offering scalable solutions to meet the evolving demands of the digital world.&nbsp; &nbsp;&nbsp; For further information, please contact:&nbsp;&nbsp;Hydra Host&nbsp;&nbsp;<a href="mailto:Sales@hydrahost.com" rel="noopener noreferrer" target="_blank">Sales@hydrahost.com</a>&nbsp;&nbsp; &nbsp;

Discover how Hydra Host's partnership with Sortium accelerates AI-driven 3D content creation, offering innovative solutions for developers and artists.

Hydra Host and Sortium Forge Strategic Partnership to Enhance AI-Driven 3D Content Creation 

Hydra Host and Massed Compute Announce Strategic Partnership to Empower the AI Community&nbsp;&nbsp;&nbsp;&nbsp;Hydra Host, a leader in data center operations and bare metal provisioning, and Massed Compute, a provider of high-performance computing solutions, are thrilled to announce a strategic partnership to transform the AI and computational landscape. This collaboration leverages Hydra Host's innovative data center solutions and Massed Compute's advanced GPU technology to offer the AI community unparalleled computing power and infrastructure efficiency.&nbsp;&nbsp;&nbsp;&nbsp;A Synergy to Drive AI Forward&nbsp;&nbsp;The partnership between Hydra Host and Massed Compute is set to redefine the standards of computational power and infrastructure support for AI research, development, and deployment. By combining Hydra Host's secure, scalable, and resilient infrastructure with Massed Compute's innovative GPU resources, this alliance offers the AI community the tools to push the boundaries of machine learning, deep learning, and AI-driven applications.&nbsp;&nbsp;&nbsp;&nbsp;Benefits of the Partnership&nbsp;&nbsp;Enhanced Computing Capabilities: Access to Massed Compute's high-performance GPUs and Hydra Host's robust infrastructure ensures researchers and developers can tackle complex AI problems more efficiently.&nbsp;&nbsp;Scalability and Flexibility: Customers benefit from scalable resources that grow with their projects, from initial development to large-scale deployment, without compromising performance or security.&nbsp;&nbsp;Cost-Effective Solutions: The partnership aims to provide cost-efficient computing resources, making high-performance AI research accessible to a broader audience.&nbsp;&nbsp;&nbsp;"Our collaboration with the team at Hydra Host has been marked by their commitment to bringing quality top-tier compute-processing solutions to a diverse array of customers and broadening our reach. Their efficiency and seamlessness have been instrumental in improving the experience we can offer to clients, and we look forward to continuing this fruitful journey together.”&nbsp; — Sunny Smith, Massed Compute Founder&nbsp; &nbsp;Empowering Innovation Across Industries&nbsp;&nbsp;This collaboration is a technical merger and a commitment to empowering innovation across various sectors, including healthcare, finance, automotive, and entertainment, by providing the computational resources necessary to drive advancements in AI and machine learning.&nbsp;&nbsp;&nbsp;&nbsp;Hydra Host and Massed Compute are poised to support the AI community's growing demand for computational power, fostering innovation and development across the globe.&nbsp;&nbsp;&nbsp;&nbsp;Contact Information&nbsp;&nbsp;For further information, please contact:&nbsp;&nbsp;Hydra Host&nbsp;&nbsp;<a href="mailto:Sales@hydrahost.com" rel="noopener noreferrer" target="_blank">Sales@hydrahost.com</a>&nbsp;&nbsp;&nbsp; &nbsp;

massed compute partnership with hydrahost

Discover how Hydra Host and Massed Compute's partnership delivers unparalleled GPU performance and scalable infrastructure for AI innovation.

Hydra Host and Massed Compute Announce Strategic Partnership to Empower the AI Community  

<h1>Top Affordable GPU Systems for Deep Learning</h1> Deep learning popularity has skyrocketed, and it shows no signs of slowing down. With its ability to ingest vast amounts of data and create novel and useful outputs, deep learning is actively revolutionizing various industries, including healthcare, finance, and content creation. However, to harness the power of deep learning algorithms, you need to leverage powerful compute, and that's where GPUs come into play. <h2>Understanding the Basics of Deep Learning</h2> Before delving into the world of affordable GPU systems for deep learning, it's essential to have a basic understanding of how deep learning works. Deep learning is a subset of machine learning that focuses on artificial neural networks and their ability to learn and make decisions in a similar way to the human brain. These neural networks are composed of interconnected layers that process and analyze data to generate accurate predictions or classifications.Deep learning algorithms are data-driven, meaning they require large datasets to train and fine-tune their models. This data-intensive nature poses a dilemma for many deep learning enthusiasts, as it requires significant computational power to handle the immense workload.One of the key components of deep learning is the concept of neural networks. These networks are inspired by the structure and function of the human brain. Just like our brains have interconnected neurons that transmit information, artificial neural networks consist of interconnected layers of artificial neurons called perceptrons. These perceptrons receive inputs, apply mathematical operations to them, and produce outputs that are passed on to the next layer of neurons.Each layer in a neural network performs a specific task. The first layer, known as the input layer, receives raw data, such as images or text. The subsequent layers, called hidden layers, process the data by applying mathematical operations and extracting relevant features. Finally, the output layer produces the final prediction or classification based on the processed data.Training a deep learning model involves feeding it with a large dataset and adjusting the weights and biases of the neural network to minimize the difference between the predicted outputs and the actual outputs. This process is known as back propagation, where the error is propagated backward through the network, allowing the model to learn from its mistakes and improve its performance over time. One of the challenges in deep learning is the need for significant computational power. As deep learning models become more complex and datasets grow larger, the demand for computational resources increases exponentially. This has led to the development of specialized hardware, such as deep learning optimized GPUs (Graphics Processing Units), which excel at parallel processing and can significantly speed up deep learning tasks.Fortunately, with advancements in technology, affordable GPU systems for deep learning have become more accessible to enthusiasts and researchers. These systems allow individuals to harness the power of parallel processing and train deep learning models more efficiently. By utilizing GPUs, deep learning practitioners can reduce training times and experiment with larger datasets, leading to more accurate and robust models. <h2>The Role of GPUs in Deep Learning</h2> Graphics Processing Units (GPUs) have revolutionized the field of deep learning with their exceptional parallel processing capabilities. Unlike traditional Central Processing Units (CPUs) that excel in sequential processing, GPUs have emerged as the go-to hardware for accelerating deep learning algorithms.Matrix computations lie at the heart of deep learning algorithms, and this is where GPUs shine. With their thousands of cores, GPUs can efficiently parallelize matrix operations, allowing for lightning-fast computations. This parallel processing capability enables deep learning practitioners to train models on massive datasets and experiment with complex architectures.Moreover, GPUs are not limited to training deep learning models; they also play a crucial role in the inference phase. Inference refers to the process of using a trained model to make predictions on new, unseen data. GPUs excel at accelerating the inference process, allowing for real-time predictions in applications such as image recognition, natural language processing, and autonomous vehicles. It's worth noting that GPUs are not a one-size-fits-all solution for deep learning. The choice between using GPUs or other types of hardware such as DPUs (Data Processing Unit) or TPUs (Tensor Processing Unit) depends on various factors, including the size of the dataset, the complexity of the model, and the available resources. However, in most cases, GPUs provide a significant advantage in terms of speed and performance. <h2>Finding the Perfect GPU for Your Deep Learning Needs</h2> Now that we understand the importance of GPUs in deep learning, it's time to explore the options available in the market. When searching for an affordable GPU system for deep learning, there are several factors you should consider: <ol><li>Compute Power: The higher the compute power, the faster your deep learning models will train. Look for GPUs with high CUDA core counts and high memory bandwidth.</li><li>Memory Capacity: Deep learning models require a significant amount of memory, especially when dealing with large datasets. Choose GPUs with ample VRAM to avoid memory constraints.</li><li>Price: Affordability is a crucial aspect, especially for enthusiasts and beginners. Look for GPUs that offer a balance between performance and cost.</li><li>Compatibility: Ensure that the GPU you choose is compatible with your existing hardware and software stack.</li><li> </li></ol>By considering these factors, you can find a GPU that meets your deep learning requirements without breaking the bank. Several affordable options are available in the market, providing excellent performance for their price. <h2>Affordable GPU Options for Deep Learning</h2> If you're on a budget but still want to venture into the world of deep learning, don't worry! There are several affordable GPU options that offer exceptional performance. Some of the popular choices include: NVIDIA GeForce GTX 1660 Super <ul><li>Compute Power: Features a respectable count of CUDA cores and good memory bandwidth. It's well-suited for moderate deep learning tasks but might lag behind in highly demanding scenarios.</li><li>Memory Capacity: Comes with adequate VRAM, suitable for small to medium-sized models. However, it may struggle with very large datasets or complex neural networks.</li><li>Price: Offers great value for money, positioned as a budget-friendly option while still providing sufficient capabilities for many deep learning tasks.</li><li>Compatibility: Generally compatible with most deep learning frameworks and software stacks. However, being an older model, it might lack some of the latest features and optimizations present in newer GPUs.</li></ul> AMD Radeon RX 5700 XT <ul><li>Compute Power: Strong performance with a high count of stream processors. It's generally more powerful than the GTX 1660 Super, making it suitable for more intensive deep learning applications.</li><li>Memory Capacity: Equipped with ample VRAM, which allows for efficient handling of larger models and datasets.</li><li>Price: Sits at a slightly higher price point than the GTX 1660 Super but offers better performance, making it a good middle-ground option.</li><li>Compatibility: While AMD GPUs are supported by many deep learning libraries, they might not have as extensive support as NVIDIA GPUs, particularly in terms of CUDA optimization.</li></ul> NVIDIA GeForce RTX 2060 <ul><li>Compute Power: Offers excellent compute performance with a high CUDA core count and the advantage of RTX-series features like ray tracing and Tensor cores, which can accelerate certain types of neural network computations.</li><li>Memory Capacity: Sufficient VRAM for most deep learning tasks, but may not be enough for the most demanding tasks.</li><li>Price: More expensive than the GTX 1660 Super but offers a significant performance boost, making it a worthwhile investment for more serious enthusiasts.</li><li>Compatibility: Excellent compatibility with the vast majority of deep learning frameworks, and benefits from NVIDIA's consistent driver and software updates.</li></ul> AMD Radeon RX 5600 XT <ul><li>Compute Power: Delivers strong performance, though slightly lower than the RX 5700 XT. Suitable for a wide range of deep learning tasks.</li><li>Memory Capacity: Comes with adequate VRAM, but like the GTX 1660 Super, it might struggle with very large models.</li><li>Price: Positioned as an affordable option, offering good performance for its price point.</li><li>Compatibility: Similar to the RX 5700 XT, it may not have as extensive framework support as NVIDIA GPUs, but it is still a viable option for many deep learning applications.</li></ul> These GPUs strike a balance between price and performance, making them ideal for beginners or enthusiasts looking to explore deep learning without breaking the bank. <h2>Exploring Cloud Computing and Hydra Host for Deep Learning</h2> Another option to consider when searching for affordable GPU systems for deep learning is utilizing cloud computing services. Cloud service providers offer virtual machines equipped with powerful GPUs, allowing you to leverage their computational resources without investing in hardware upfront. However, they do charge a premium for the advantage of not requiring an upfront hardware investment. At HydraHost.com, we can help get the best of both worlds. Our compute marketplace Brokkr allows us to integrate GPUs directly from independent data centers, which are generally able to out compete traditional cloud providers. At Hydra Host we're about to source GPU resources at all levels, from consumer to high end data center GPUs such as the A10, A100, and H100. <h2>Additional Resources for Deep Learning Enthusiasts</h2> Deep learning is a rapidly moving field, with notable advancements and breakthroughs consistently taking place within weeks of each other. If you're eager to keep up with the world of deep learning, here are some additional resources to explore:<ul><li>Online courses: Platforms like Coursera, Udemy, and edX offer comprehensive deep learning courses taught by industry experts.</li><li>Research papers: Stay up to date with the latest advancements in deep learning by reading research papers published in prominent journals and conferences.</li><li>Open-source libraries and frameworks: Popular frameworks like TensorFlow and PyTorch provide extensive documentation, tutorials, and community support to help you on your deep learning journey.</li></ul> <h2>Conclusion</h2> Deep learning has unlocked remarkable opportunities across various industries, but harnessing its potential requires powerful hardware infrastructure. Thankfully, affordable GPU systems are available, enabling deep learning enthusiasts to explore complex models and large datasets without breaking the bank.By understanding the basics of deep learning, the role of GPUs, and the options available in the market, you can make an informed decision when choosing an affordable GPU for your deep learning needs. Additionally, exploring cloud computing services and utilizing additional resources can further enhance your deep learning journey.So, equip yourself with an affordable GPU system, dive into the exciting world of deep learning, and unlock the possibilities that await!

Explore top affordable GPUs for deep learning to boost your AI model training without breaking the bank. Choose and compare our 4 top picks!

Top Affordable GPU Systems for Deep Learning

We are on the horizon of the inevitable shift towards GPU-dominated infrastructure, and this movement will build several unicorn companies. <a href="https://www.reuters.com/technology/crypto-billionaires-nonprofit-buys-500-million-ai-data-center-chips-2023-10-30/" rel="noopener noreferrer" target="_blank">Voltage Park</a>, a soon-to-be billion-dollar H100 provider, shows the momentum of the shift towards GPUs. Accessible GPUs are going to transform the infrastructure of the web. As Jensen says, “Moore’s law is dead,” and it is time for the next generation of computing infrastructure. CPU-based public cloud operations can’t move quickly and capture this demand for GPUs because they have built their entire business model on low-cost, low-power, commoditized CPUs. GPUs are energy-dense and require more advanced cooling than CPUs. GPU-specific suppliers, like Voltage Park, are designing their operations and infrastructure to support these requirements. These new types of suppliers are capturing this growing demand while the market waits for overpriced public cloud-supplied GPUs. The next unicorn companies within AI are infrastructure companies. Demand is only increasing as large enterprises are now beginning to adopt AI and LLMs. NVIDIA is sold out for years to come. The GPU deficit, combined with the public cloud’s subpar design for GPU operations, has pushed customers to alternatives like Voltage Park and the secondary market. The nascent secondary market for GPUs is a forcing function of the annual billion-dollar shortage of GPUs. The supply and pricing you get from system integrators and direct relationships with NVIDIA are consistent and stiff, just like buying a new car. You aren’t going to get a fantastic deal anymore from OEMs or integrators. NVIDIA has a tight grip on every single hardware builder. From shortages and high demand, a new bare metal marketplace is here. When you need a car, the inevitable choice is presented: new or used. Buying a new car is a relatively standard and inflexible process with little room to make a deal. If you find a deal, it usually comes from the manufacturer as a financial incentive to the dealer (ergo, NVIDIA Inception Program). Typically, the dealership has a minimal margin on new car sales. The used car market, on the other hand, has a higher variance between cars, making it harder to price consistently. Therefore, good deals exist predominantly in the secondary market. <a href="https://www.linkedin.com/company/hydrahost/" rel="noopener noreferrer" target="_blank">Hydra Host</a>’s platform supports this new secondary market for GPUs, and we are seeing many massive and insane deals. Rising prices and endless demand have given birth to a bare metal marketplace. Like with cars, pricing is better on the secondary market. Some suppliers bought early and can run GPUs 30% cheaper than the public cloud. Data centers are refurbishing GPUs and running them at half the price. Resellers are buying cards on cheap and holding them. If you want to see what is happening in the GPU market, sign up at Hydra Host and see what is happening.

Discover how the transition to GPU-centric infrastructure is creating new unicorn companies and transforming the tech landscape.

The Inevitable Shift

<h2>Brokkr | Hydra Host's High-Performance Computing Marketplace</h2>

Today the Hydra Host team is excited to announce Brokkr, a two sided marketplace for high-performance computing. The Brokkr platform aims to connect users with an extensive network of computing resources, offering unparalleled optionality and flexibility when it comes to compute heavy workloads. From scientific research and data analytics to artificial intelligence development and simulations, Brokkr matches users with the power they need to tackle complex tasks efficiently and effectively.&nbsp; "Brokkr" is a reference not only to the platform's function as a compute brokerage or marketplace but also to the mythological Norse figure, known as a skilled blacksmith and metalworker. This intentional terminology emphasizes Hydra Host's focus in designing and building Brokkr: enabling the provisioning of bare metal servers from any data center, anywhere.&nbsp; By addressing bare metal provisioning, Brokkr paves the way for aggregating compute resources from providers worldwide, irrespective of size, location, or existing provisioning or API availability. For end users, this translates to a gateway and the first genuine marketplace for compute. For data centers, it enables them to monetize and optimize infrastructure utilization by joining the Brokkr network with no need for provisioning expertise or marketing prowess.

<h2>Understanding the Benefits of High-Performance Computing with Brokkr</h2>

High-performance computing is swiftly emerging as a vital instrument across industries, from scientific research and engineering to financial modeling and artificial intelligence. Yet, these computing resources are capital-intensive, and sourcing, acquiring, and maintaining them can be an intimidating undertaking for many organizations. In practice, this often means navigating disparate supply chains and providers or becoming excessively dependent on a single provider. Brokkr introduces a novel approach by furnishing a centralized marketplace where users can effortlessly access and provision high-performance computing resources. Brokkr allows you to harness a growing network of providers and data centers to find the best match for your specific computing needs. Regardless of the provider you choose, there's no need to concern yourself with managing multiple accounts, billing, or learning and integrating new APIs. Hydra Host standardizes all these aspects to minimize operational overhead and ensure a smooth and vendor-neutral experience for users.

Hosting has been largely monopolized by a few major hyperscaler providers for over a decade. However, in the past few years, the landscape has been changing. A rapidly evolving digital environment has amplified the need for dramatically more compute, and compute in more places than ever before. From the unprecedented demand for cost-effective GPUs for AI/ML workloads to an escalating focus on edge computing and optimized latency, the hyperscalers are struggling to keep pace. This situation stems from the inherent unsustainability of the hyperscaler model, which has been living on borrowed time. By creating isolated ecosystems and maximizing vendor lock-in, they've endeavored to insulate themselves from competition and camouflage this simple reality: compute is a commodity, and ought to be treated as such. The Brokkr platform seizes this evolving shift in the industry by offering a distributed alternative to the hyperscaler model, thereby enabling independent providers with unique insights and localized advantages to bring them to the table. With Brokkr, users seeking the highest performance, lowest cost, or least latency compute for their respective use cases will be able to discover and provision it seamlessly, from anyone and anywhere.

The key to Brokkr lies in our growing network of data center partners. While there are a plethora of multi-cloud platforms and hosting solutions available, none of them permit independent data centers to easily offer and monetize their resources. Brokkr bridges this gap by incorporating bare metal provisioning capabilities, an essential step towards commoditizing compute. From the outset, our focus has been to support independent data centers, enabling them to concentrate on their core competency, i.e., setting up and maintaining physical hosting infrastructure, by resolving provisioning and customer acquisition challenges. Through our dialogues and industry research, we've interacted with several independent data centers and gained a first-hand glimpse at emerging industry trends poised for acceleration. This includes the modular DCs established and managed by one of our partners, EnergyX. Their approach allows for industry-leading construction and setup times, and the flexibility to deploy to any location with superior economic factors, such as latency, power cost, or environmental cooling. Brokkr aspires to make innovative approaches like EnergyX's accessible and transfer the advantages they offer over traditional hyperscaler resources directly to end users. If you're a data center or a supplier of computing, we welcome you to discuss listing your resources on Brokkr.

Brokkr is still in its initial stages, and there's lots of work to be done. We're looking for early testers to help us iterate quickly and effectively. Below is a list of features currently on our roadmap, which you can expect in the comings weeks and months: <ul><li>A growing list of inventory from our hardware providers</li><li>Fine-grained filtering and search capabilities to allow users to easily find the specific compute resources they need based on performance, cost, and latency requirements.</li><li>Supply chain visibility: It's no surprise GPUs are in high demand, with most high-end models like the new H100 being sold out before they hit the import docks. Currently, buyers of high-end computing resources face challenges in tracking the supply chain and availability of these resources, and often have to do so by juggling several email threads and contacting multiple vendors. We aim to address this problem in Brokkr by enabling suppliers to list upcoming inventory available for pre-order and enabling interested buyers to browse this upcoming inventory all on one centralized dashboard for shopping and pre-ordering.</li><li>Terraform provider: In addition to our web interface and API, we are developing a Terraform provider for Brokkr. This will allow users to easily provision and manage their compute resources using modern infrastructure as code patterns.</li><li>Advanced billing dashboard: We understand that managing and tracking billing can be a complex task, especially in the world of high-performance computing. But it doesn't have to be. With Brokkr we are working to provide an easy-to-understand high-level billing experience, with the ability to zoom in to any desired level of granularity for accurate tracking and analysis. Compare and project compute resource and pricing options, and gain insights into resource utilization and costs, all in one comprehensive dashboard.</li><li>Secure boot and other security add ons: To ensure the highest level of security for our users, Brokkr will provide secure boot capabilities and other features to ensure the hardening of critical workloads regardless of where they are deployed.</li><li>Spot market: Utilization is one of the biggest problems when it comes to high performance compute. High end hardware is a massive investment, and making sure the most is being made out of it is critical. That's why Hydra Host is planning on extending Brokkr with a spot market for interruptible workloads, tailored to high end GPUs, in 2024.&nbsp;</li></ul>

<h2>Getting Started: How to Join the Brokkr Community</h2>

For both data centers and end users, all Brokkr usage requires manual on-boarding with the aim of working closely with users to identify potential bugs and gather feedback for further improvement. Whether you're a data center or end users, you can get started by registering below.

The Hydra Host team is looking forward to rapidly improve on the Brokkr platform, and add more compute partners and inventory. Be among the first with us on this journey, and enjoy limited time access to extremely cost effective Hydra managed CPU resources, and Founder level engagement and support.&nbsp;

Discover Brokkr by Hydra Host—a marketplace connecting users with high-performance computing resources for AI, data analytics, and more.

Introducing Brokkr

This was a wild week for the GPU market. Brand new export restrictions caused a tumultuous week. Additional limitations on future H100 orders are expected. The steady pricing from previous weeks was thrown out the window. Here is what my team at&nbsp;<a href="https://www.linkedin.com/company/hydrahost/" rel="noopener noreferrer" target="_blank">Hydra Host</a>&nbsp;is seeing. New export restrictions lay down the hammer - The US government continues to turn up the heat on advanced computing chips with export chips. The looming new rules released this week are substantial and include additional manufacturing equipment restrictions and expanded qualifications. The new proposed regulations made “Chinese” NVIDIA chips irrelevant in design and, at the same time, valuable. These chips were essentially designed to evade export restrictions, and the new rules significantly expanded their scope and authority (see images below). Almost all advanced chips from NVIDIA and some gaming chips qualify. There are some exceptions to be more targeted, but the US government’s explicit goal is enterprise data centers. The US government has capped China’s computing power, forcing it to accelerate its internal development. The black market for GPUs will aggressively develop over the coming year. I wouldn’t be surprised if GPUs become like a new drug trade. Slapping OEMs selling only cards - NVIDIA is tracking down OEMs selling GPU cards rather than building complete systems. NVIDIA aims to expand its footprint as fast as possible in data centers worldwide, and this under-the-table market is slowing it down. It is also exacerbating the shortage, so we heard NVIDIA removing allocation to OEMs, continuing to sell just chips and not building servers.

Buy. Buy. Buy whatever you can get - From these looming restrictions from the US government and NVIDIA applying more pressure to GPU-as-a-service vendors, there is a renewed aggressive buying spree. Some companies are hoarding to resell in the future, while others are concerned that nothing will be left. A handful of companies are scooping every H800 they can find, betting that the same thing that happened to A100s will happen to H800s. We saw new and used A100 supply disappear the same day. H100 servers moved faster than usual as companies expect future price increases. GPU prices will increase soon - Due to restricted supply and higher demand, prices are going up again. Orders in November and December are already higher than in October. We expect H100s to have the most considerable price increase. Resellers are also increasing their prices due to fewer buyers and more shortages. Feels like we are in a prison yard-style market again. We have supply today if you need A100, H100, or other GPUs. Ping <a href="mailto:sales@hydrahost.com" rel="noopener noreferrer" target="_blank">sales@hydrahost.com</a> for availability. We can help find any GPU for you, whether renting or buying.

Learn about the latest U.S. export restrictions on GPUs and their implications for the tech industry.

GPU Chip Restrictions

The joke within the venture community is that they are not funding AI companies but funding infrastructure to buy NVIDIA chips. <a href="https://www.theinformation.com/articles/andreessen-horowitz-discussed-backing-a-midjourney-rival-seeking-500-million-valuation" rel="noopener noreferrer" target="_blank">Investing in AI companies </a>today is just a direct transfer to NVIDIA. Due to GPU demand, that is true today, but supply and pricing pressures on HPC infra will evolve the market in the next 12 months.

DCs are going direct and selling wholesale. Before the GPU revolution, there were generally only two hosting options - hyper-scalers or colocation. That is changing now due to the cost of GPUs and limited supply. Pre-GPU revolution, CPUs were highly commoditized, and you needed a massive scale to have any competitive advantage. It didn't make sense for most companies to expend capital on a commodity bare metal. The world today is changing quickly. GPUs vastly differ from CPUs: compact, immensely powerful, expensive, and not commoditized. In this new world, DCs are creating a new business line by bypassing cloud companies and renting GPUS directly to companies. Colocation space that would have previously gone to the public cloud or big corp colocation will go to the rent-a-GPU business to make cloud-like margins. Data centers are even adding financing capabilities to get more GPU business. Going direct saves AI or platform companies 15-20% on their computing and hosting costs.

Companies are starting to resell their GPUs to others. HPC hardware is being over-acquired and underutilized, but the reasons are similar to the situation with Web 1.0 companies (which spawned the public cloud). Due to infra limitations, Web 1.0 companies needed extra bare metal capacity to scale for something as simple as visitors. You had to build your sovereign infra to handle traffic spikes. Today, the market situation looks familiar. Companies need bursting capacity to massive clusters for better training runs. Rather than waiting for an unknown period of time, constantly hunting for capacity, or having your training run cut off by another customer, companies are sitting on underutilized hardware or inefficiently using GPUs ("Well, it is there, so might as well use it"). Companies are now taking advantage of the market dynamics and reselling their GPUs to the public. Companies can recoup 100% of their hardware investment at current rental prices in less than 12 months.

These two reasons are why we built&nbsp;<a href="https://www.linkedin.com/company/hydrahost/" rel="noopener noreferrer" target="_blank">Hydra Host</a>&nbsp;to take advantage of the next phases of the global bare metal market. We make it super easy to manage and sell GPUs with our bare metal marketplace -&gt;&nbsp;<a href="http://www.hydrahost.com/" rel="noopener noreferrer" target="_blank">www.hydrahost.com</a>

 Explore how data centers are reshaping the bare metal market by renting GPUs directly, enhancing performance and reducing costs.

Next Phases of the Bare Metal Market

It is difficult to overemphasize how much “The Cloud” has become a mainstay of the Silicon Valley zeitgeist since its meteoric ascendancy from 2008 onward. Consequently, it may seem like an impossibly far-fetched idea that the growth and evolution of the cloud was a phenomenon as artificial as the interest rates which fueled it. The public cloud has witnessed roughly 2350% growth between the years 2008 and 2020, with even more headwind made immediately following the COVID-19 response and global lock downs. This particular spike in growth has been reasonably traced to the skyrocketing demand for digital services from an unprecedentedly shut-in population. But here’s something else this period had in common with the initial 2008 takeoff: historically low interest rates. Federal Funds&nbsp;Rates Since the 2008 financial crisis, central banks around the world have maintained historically low interest rates to stimulate economic growth and recovery. These artificially low interest rates have had an expansive range of effects on the economy, including influencing the growth and evolution of the public cloud. Recently, interest rates have experienced a sudden and sharp reversal. Correspondingly, the decade-long trend of ceaseless migration of digital infrastructure to hyperscalers has also witnessed its first major slowdown and vocal push backs. This article will explore the causative connection between low interest rates and the expansion of public cloud services. Furthermore, we’ll be discussing the alternatives to the public cloud paradigm that are poised to surge in growth, and what that means for various players in the industry. Interest Rates and the Public Cloud Growth The tech industry does not operate in a vacuum and is heavily influenced by macroeconomic forces in simultaneously subtle and dramatically impactful ways. We’ve already pointed at the correlation between the rapid growth of the cloud and historically low interest rates beginning in 2008. But to explore the causative mechanisms at play between these concepts, we need to ensure we understand the economic backdrop of the cloud’s development.First and foremost, interest rates represent the cost of borrowing money, which is ultimately determined by the Federal Reserve and other central banks worldwide. When interest rates are pushed artificially low, liquidity is abundant and inexpensive, resulting in both profound and non-obvious downstream impacts on the behaviors and trends of entire economic sectors. In such conditions, companies and investors are strongly incentivized to pursue riskier investments and are more likely to take on debt and fundraising to finance their ventures. The market begins optimizing for growth, often and even inevitably at the cost of capital efficiency, fiscal discipline, and long-term strategic planning. This is due to the reality that if a company is not leveraging the lower interest rates and increased credit availability to focus on growth while their competitors are, they are unlikely to survive. Consequently, all market participants eventually adopt similar optimization strategies. This leads to the development of entire industries on shaky foundations that would have been impractical and imprudent outside of the influence of extraordinary macroeconomic conditions. It’s only once conditions return to the norm, that the decision making that seemed sound and rational previously is met with a harsh dose of reality. When interest rates have been kept low for an unprecedented amount of time, it should be no surprise then that the downstream consequences too are unprecedented. Most obvious amongst these consequences has been the total fragility of the financial system, as highlighted by the recent collapses of Silicon Valley Bank, Signature Bank, and First Republic Bank, in rapid response to the semi-normalization in interest rates. This is only the tip of the iceberg, and the longer interest rates remain raised, the more the economic growth of the last decade will be shown to have severe structural problems. In the case of cloud computing, there are already signs that a half a trillion dollar industry that came to maturity entirely in this easy money time period is in trouble. Condensing the Cloud What do we mean when we talk about the “The Cloud”? For most, the public cloud is defined solely by the outsourcing of hardware management to a specialized provider. Despite the adage, the cloud isn’t just someone else’s computer. To be abundantly clear, there are incredibly good reasons for companies to outsource the physical management of compute to other market players better suited to the task. These decisions are rooted in the simple ideas of comparative advantage and economic specialization.&nbsp;However the major hyperscalers in the cloud oligopoly (AWS, GCP, Azure) share specific traits which define the entirety of their business models, modes of operation, and trade-offs that are far more nuanced than simply outsourcing hardware management. These massive platforms derive most of their usage, stickiness, and earnings not from compute alone, but ultimately from the additional services and tools tightly bundled with that compute. This includes a vast catalog ranging from managed databases, to identity management, CDNs, managed orchestration tools like Kubernetes, and so much more. In essence, the hyperscale cloud providers today are an intricate and often confoundingly complex array of proprietary tools and managed services. Why? To lock users into their platforms by bundling their software and services add-ons with their hardware. Want to switch to using a competitor’s compute resources instead? Not so fast: a cobweb of interlocking managed software dependencies now keeps you tethered to your host with no easy way out. This is the cloud today: a few major walled gardens defined by the bundling of hardware, software, and services in an effort to de-commoditize what is and should be a commodity business: supplying compute.&nbsp;The resulting size, concentration, and control over digital infrastructure that these providers leverage have subsequently become a far cry from the early visions of an open and decentralized internet. This state of affairs is a direct result of the macroeconomic incentives that have been in place almost continuously since 2008.&nbsp; All That Glitters isn’t Gold The deliberately “bundled” design of the cloud offers a clear tradeoff: speed over portability and flexibility. Lack of portability results in higher fiscal costs to customers, especially long term, as these providers know moving to a competitor is no trivial matter. The more services and tools customers use from their current cloud host, the more they are disadvantaged over time as the cost of migrating to a competitor tends to only increase. While using these more easily integrated tools and managed services to increase productivity and optimize for growth may have seemed like a straightforward choice to the customer it is ultimately a Faustian bargain. For most players operating in an economic environment defined by unprecedentedly low interest rates, this optimization for immediate growth over longer term considerations made total economic sense.In such a case, fundraising is easy, and as such fiscal discipline is not as valued by investors as speed to market and growth at all costs. If this sounds far fetched, believe me, it was extremely common up to early 2022 for angels and VCs to tell portfolio companies they weren’t spending money fast enough. When cash is cheap, other things are simply more important, and rationally so within the context of the economic environment. The costs of increasingly large AWS bills hardly registered as long as revenue growth continued and fundraising remained extremely easy, both themselves buoyed by low interest rates driving less discretionary spending all across the economy. If your competitors are leveraging the dynamics of the easy money environment, and you’re not, you’re likely dead in the water before the longer term considerations even matter. However unlike death or taxes, artificially low interest rates aren’t and can never be constant. As inflation concerns began to reach fever pitch over the last year, the Federal Reserve and other central banks have quickly reversed course by aggressively hiking interest rates once more. Very abruptly, the calculus driving economic decision making has turned on its head, and the impact on many industries, like banking, have been hard and obvious. Many other’s have been heavily impacted as well, albeit less dramatically. For now. The cloud included. Revenue and fundraising capabilities are both sinking in lockstep, and suddenly the abrupt need for aggressive fiscal discipline for the first time in over a decade is bringing to light certain glaringly obvious truths.&nbsp; The Cloud Doesn’t Actually Make Much Sense As it turns out, making yourself entirely reliant on a single provider with absolute leverage over your infrastructure is a really bad deal if you actually have to think long term or care about fiduciary responsibilities. That’s the conclusion many companies are rapidly coming to, a notable recent example being 37 Signals, as explained by co-founder David Hansson the creator of Ruby on Rails.In their effort to move from the Cloud, the team at 37Signals has built MRSK, a framework to help manage higher level tooling and application orchestration on top of raw compute, completely agnostic to where that compute is hosted. This serves a crucial function: It effectively helps unbundle some of the functionality typically bundled by cloud providers, giving you greater portability and control over your software. There is still a bit more additional work involved with this solution compared to ready-to-go cloud tooling to be sure, but MRSK and tools like it are relatively niche and underdeveloped, especially when compared to the vast investment the public cloud tooling has seen over the past decade plus.These tools and the paradigm it represents offers a starkly different alternative to how the cloud works today. By simply buying or renting raw compute and running the open source infrastructure stack of your choice on top of it, an “un-bundling” occurs which undoes the position of power the cloud oligopoly have created for themselves while the industry has slept. In this model, businesses can crucially still leverage more specialized third parties to manage their physical compute in the actual actual data center, and still leverage third party software to maximize their productivity, but in a portable and natively agnostic fashion. In our now higher interest rate environment, that reality of cloud costs and it’s long term implications for organizational flexibility and efficiency is becoming much less palatable for a huge swath of companies all at once. As more continue to flee the lock in and corresponding costs of the cloud out of pure fiscal necessity, the tools to make this “un-bundling” movement even easier and more practical will continue to be improved upon. With that in mind, we can scarcely imagine how the ecosystem of independent bare metal providers and portable infrastructure tooling might look like 10 years from now, or how they might have looked like today had there not been more than 10 years of artificially low interest rates to begin with. “The Cloud” as we know it today, may never have come to be. Indeed, the fact that it did evolve in the way it did increasingly seems like the anomaly. A development that was broadly accepted but is in fact seeming more counterintuitive in hindsight by the day. In it’s stead, we are seeing the growth of a much more diverse, distributed, and commoditized pool of compute providers, upon which an ecosystem of open source and highly portable software can flourish.&nbsp; Unwinding The Cloud The problem with the growth of major cloud providers isn’t the idea that certain businesses shouldn’t specialize in running the hardware on behalf of other businesses with other priorities. The problem is actually that that specialization doesn’t go far enough. There should be a competitive industry for offering compute as a commodity, and distinct industries for orchestration, managed services, and tool chains to effectively utilize that compute, independent of where the physical hardware is located and who manages it. Certainly, there will always be a market for vertically integrated hardware and software products, typically thought of as hosted platforms as a service. But if we accept that the expansive proliferation of this bundled pattern was in large part driven by unsustainable incentives derived from low interest rates, then we must conclude that the relative market for these offerings is much smaller than what we’ve seen up to this point. The logical conclusion of this is a continued “un-bundling” movement, and the gradual but accelerating transformation of a more than half a trillion dollar industry. That means growth for providers of "less-strings-attached" bare metal as a service (BMaaS) and basic virtualized offerings, and vastly increased interest, attention, and development of portable software and services that can be deployed on top of any provider of compute resources. This reorientation of how we build and deploy applications will have profound effects, but ultimately will likely be a huge blow to the defacto oligopoly and over centralization of infrastructure in the hands of AWS/GCP/Azure, and a big win for industry disintermediation and competition in the long run.In the midst of ongoing economic uncertainty, we might actually be seeing the internet trending towards a more natural and distributed equilibrium as skewed incentives are removed from the markets comprising it. One thing seems certain: Were it not for artificially low interest rates to begin with, the way we would use “someone else’s computer” would look very different from the cloud we see today, and much more in line with the original vision of an open, decentralized, and interoperable internet. Practical Takeaways For developers and dev ops engineers: Instead of that next AWS certification, start familiarizing yourself more seriously with bare metal, and how to deploy and work with more fundamental building blocks. Frameworks like MRSK, Supabase, and others offer paths to effectively deploy to bare metal as well as VPSs in manageable ways. Demand for technical talent that can facilitate migrations to this paradigm will continue to grow rapidly. For companies and organizations: Are you on the cloud? If so, you’ve likely already taken a recent look at your AWS bill and are wondering how to make some improvements. It’s time to start thinking outside of the box, and ask your engineering teams to take a serious look at some of these trends and technologies mentioned in this write up and present a plan to get to higher ground. Still operating on-premise infrastructure? You might have actually dodged a bullet there, and you’re in a position to carefully plan what your future looks like. There’s plenty of options between remaining on-prem, out sourcing on-prem management, going to a BMaaS model, or some hybrid of these options. For data centers and independent compute providers: This is your time to shine. There’s never been a bigger opportunity to grow your business, but to do so you need to offer comparable user experiences and programmatic capabilities to the large cloud providers to make transitions easier for new customers, but otherwise simply focus on delivering superior hardware for the price. At Hydrahost.com, we’re building a data center directory, and helping our data center partners offer modern Bare Metal Self Service capabilities with easy to use UIs and robust API. Contact us to learn more.

Discover how low interest rates accelerated cloud adoption and the challenges emerging as rates climb, reshaping digital infrastructure strategies.

The Cloud: A Low Interest Rate Phenomena 

There are two types of moments in history: those that feel like nothing happens and those that feel as if the world changes multiple times over in a single lifetime. The last decades have seen the rise of mega tech platforms that promised obvious efficiencies with few downsides. These platforms changed our entire society. What was promised was more choice and freedom for all with lower prices and what we got was an assault on our core identity as a society. Today, we have a choice — what do we want our future to be? In the past decade, the single greatest accelerant of societal change was mega internet platforms. With widespread adoption and near infinite scale, big tech platforms were sold as a public good and innovation free from moral responsibilities for every consumer and business to benefit from. On the surface, things appeared harmless and basically free, like selling books or providing information. Underneath their full ownership of the core infrastructure stack for the free and open web, from domains to computing power, lurked increasing lock-in with walled gardens that grew into a power that was destined to be abused. Without a check, this type of absolute power will be corrupted.

<blockquote>“Power tends to corrupt, and absolute power corrupts absolutely.” — Lord Acton</blockquote>

Then, COVID changed everything and brought light to the brewing darkness that had been brewing behind the meticulously designed and crafted walled gardens. The mask was taken off, so mega-tech platforms attempted to mask the world. They executed a near autocrat-level control over global society at once. Convenience and subsidized lock-in with “free services” turned into a society-wide hostage situation. The wolf was in the hen house. With scale, came brutal centralized control that threatens every citizen and business. With COVID, quiet conversations about censorship that only impact the fringe and politically involved now reached the mainstream. What previously impacted the 1% now suddenly impacted the other 99%. The world was in shock at the speed and intensity of this change. Business owners were thrown into jail for normal operations. Dissent led to mob-like censorship the likes infamous dictators would have been in awe of and public witch burning to unperson the heterodox made a massive comeback. People were literally arrested for surfing in the ocean and walking down the street without a cloth mask on. I was a victim of the thumb of mega tech platforms. I&nbsp;<a href="https://www.wsj.com/articles/controlling-the-virus-narrative-11584899715" rel="noopener noreferrer" target="_blank">wrote a piece in March of 2020</a>&nbsp;that was the first seminal criticism of lockdowns and COVID hysteria. It garnered millions and millions of views in less than 24 hours. My core argument warned that our politicians were taking things way too far and the evidence did not justify the rash and immoral dilution of basic freedoms. Then, I suffered a fate like many others —&nbsp;<a href="https://www.wsj.com/articles/the-lockdown-skeptic-they-couldnt-silence-11589566245" rel="noopener noreferrer" target="_blank">I was canceled</a>. My predictions and assessments were accurate, yet my access to the internet was revoked. I was deemed a thought criminal and put into internet jail for expressing precise and well-supported views. For the length of COVID hysteria, we watched the free and open internet suffocate under the weight of mega tech platforms. These platforms were founded to achieve scale but did so with a cornerstone of lock-in and centralization. These values prohibited the abilities of companies and people to freely operate and utilize the open web when it mattered the most. We asked for the promise of a free and open internet. Instead, we got an elimination of customer choice and mega tech profiting from lock-in by bundling sub-par products and abusing power without a second thought. Despite how much society is opposed, mega tech platforms have become too big to fail. It is time to change that. The world needs a new social contract with technology companies that won’t take away power but instead enables freedom, choice, sovereignty, and dignity. The world needs a new type of infrastructure platform that follows the initial vision of a free and open web with a renewed promise to ALWAYS give you choice and privacy for whatever your needs or priorities are. This is why I started&nbsp;<a href="http://www.hydrahost.com/" rel="noopener noreferrer" target="_blank">Hydra Host</a>&nbsp;with two longtime friends, Garrett and Ariel. Hydra Host is here to offer&nbsp;real&nbsp;customer choice in hosting, web infrastructure, and most importantly, compute. Infrastructure is the heartbeat of how the internet works and is fundamental to the social contract between platforms and users. Infrastructure is how every company or individual accesses the web. Without sound infrastructure, you could be cut off from your right to the web. Hydra Host is here to fix that. Let’s be real. No one really likes their hosting provider. The public cloud failed. It can’t do everything you want. Bills are confusing. In your gut, the price feels out of proportion compared to what you receive. There is constant pressure from providers to pull you further into their toolchains so you can’t leave. Their “free credits” feel like a gateway drug because you are always hearing about the better hosting provider down the road. Your infrastructure provider should not control your business or control you. <blockquote>YOU should be in charge so YOU can do choose to do what is best for YOUR company and YOU should never be concerned about your cloud infrastructure provider deciding your company can’t do business.</blockquote> From Hydra Host’s founding moment, we have followed the best security and privacy practices and adopted cutting-edge agnosticism and open-source frameworks.&nbsp;Our vision is for any user to have the ability to access any infrastructure provider in the world, whether for cost or security reasons, with a single API.&nbsp;At any time, you can decide to leave any provider or try a new provider. No refactoring. No complexity. Freedom to choose, freedom to move as easy as a click. The choice is yours. Just like with Kayak or any other marketplace, you are in the driver’s seat. Hydra Host has standardized and simplified every point of the provisioning process. Each hosting company is a common and uniform node that you can access in a simple and single programmatic framework. With our API, we prevent you from getting locked in ever again. Our commitment with Hydra Host is to constantly deliver the best hosting possible while respecting our customer's and client’s privacy. We believe the internet should be accessible to every single person and company regardless of background, creed, faith, or tradition. You can test our initial Terraform provider API for yourself —&nbsp;<a href="https://github.com/Hydra-Host/terraform-provider-hydra" rel="noopener noreferrer" target="_blank">https://github.com/Hydra-Host/terraform-provider-hydra</a> As&nbsp;<a href="https://twitter.com/get_hydrahost/status/1501280353838792706?s=20&amp;t=DLKQggKEfJyXdRyLpU8t3g" rel="noopener noreferrer" target="_blank">we allow mega tech companies to control more and abuse their power</a>, our ability to see what humanity can truly do is limited. We are choking humanity’s greatest asset for survival — creativity. The future is too important to let a handful of mega-tech companies determine what we’re allowed to do. It is harder than ever to see where the future could take us. At Hydra Host, we’re paving the way and working to ensure your right to the free and open web doesn’t ride or die on the whims of a random tech platform. With Hydra Host, we believe your future should be in your own hands. <a href="https://www.hydrahost.com/" rel="noopener noreferrer" target="_blank">Join us</a>. Choose liberty and not tech tyranny. The time is now to storm centralized infrastructure and seize the future we want, for the people and the free and open web.

<blockquote>“A revolution without dancing is a revolution not worth having.” — V for Vendetta</blockquote>

Explore how centralized cloud hosting threatens internet freedom and how Hydra Host champions user autonomy and choice.

The dangerous remedy to the desperate disease of cloud hosting

In software, everyone wants to be in on the Next Big Thing™. Of course, identifying that Thing can be tricky. Today, Hydra is doing that work for you. In this primer we’ll cover: <ul><li>What is Sky Computing, and why it’s quickly emerging as the Next Big Thing</li><li>Sky Computing use cases, and how it compliments other trends such as AI/ML</li><li>How it works, the different implementation approaches, and their trade-offs</li><li>How industry adoption is likely to play out</li><li>The early companies and projects, like Hydra Host, poised to be early major players</li></ul> Whether you’re a software developer, platform/DevOps engineer, or a business owner, if you rely on the cloud and want to keep a competitive edge, being early to the Sky Computing discussion is a must. The goal of this article is to provide a high-level primer on this subject and its ongoing emergence, with actionable starting points on how to get in on the ground floor to the benefit of your career, business, and customers. <h2>What even is this Thing</h2> “Sky Computing” registers under 1,000 monthly searches on Google, so what is it, and why are we certain it’s not just a Thing, but poised to be a Big Thing? The term was cemented relatively recently in a paper titled <a href="https://arxiv.org/ftp/arxiv/papers/2205/2205.07147.pdf" rel="noopener noreferrer" target="_blank">“The Sky Above the Clouds: A Berkley View on the Future of Cloud Computing” by Ion Stoica and company</a>, and is meant to evoke a radical extension and enhancement of today’s cloud computing landscape. As the name suggests, the central idea of Sky Computing is users should be able to leverage all different clouds and other resource providers, whether major hyperscalers, independent datacenters, or edge compute, as easily as they’re able to leverage a single cloud provider. Rather than being reliant on any single cloud, the entire skyscape of computing should be at all of our fingertips. It’s worth noting that there are competing terms being used to describe the same ideas, including <a href="https://siliconangle.com/2022/05/21/supercloud-becoming-thing/" rel="noopener noreferrer" target="_blank">“Supercloud” and even “Metacloud”</a>. None have a clear edge yet, but Sky Computing has the benefit of being a completely novel term without any of the potential baggage of previous associations. The “Sky Above The Clouds” paper also does the best job of comprehensively describing the technology and challenges so far. <h2>The Problem</h2> Cloud was a pivotal step forward for the software industry. The cloud allowed a natural division between two compute spaces - the building and maintaining of applications and the operating hardware required to host them. However, the cloud as it works today also has significant down-sides. Today, all the major clouds have one anti-feature in common: cloud vendor lock in. Once you’ve built your entire infrastructure to a particular provider, shifting to another one entirely is typically a complicated and expensive undertaking. As a result, what we have today are different isolated “clouds”, or walled gardens with little agility and optionality for businesses. On the macro scale, this reduces competition and increases pricing. On the micro scale, this means the norm is to be forced to entirely rely on a single hosting operator for business operations, continuity, and their ongoing fair pricing and policies. <h2>Multi-Cloud is a Cry for Help</h2> Businesses already intuitively understand the criticality of the issues associated with being stuck with a single provider and are working to imperfectly address them. <a href="https://www.prnewswire.com/news-releases/virtana-research-finds-more-than-80-of-enterprises-have-a-multi-cloud-strategy-and-78-are-using-more-than-three-public-clouds-301548266.html" rel="noopener noreferrer" target="_blank">In a recent survey, more than 80% of enterprises have a multi-cloud strategy and 78% are already using more than 3 public clouds.</a> The factors pushing these enterprises towards leveraging new providers stretch from cost optimization, to available geographic locations, and to the need for diversification in the event of a provider outage. However, in Hydra’s experience of offering multi-cloud advisory and consultation services, most businesses are multi-cloud in the sense that they leverage other providers to deploy new services, rather than making legacy services cloud agnostic and multi-cloud capable. This is because migrating legacy infrastructure built to a specific cloud remains so difficult. Managing disparate resources across providers creates its own problems however, due to the burden of orchestrating and maintaining multiple different cloud infrastructure stacks and accounts which sport their own distinct tool chains and interfaces. There’s a better way, and that’s exactly what Sky Computing aims to achieve. <h2>The Next Big Thing</h2> Imagine a single dashboard that allows you to click and choose which provider you deploy your resources to, filterable by variables such as prices, locations, latency estimates, historical uptime, performance, compliance standards, and legal jurisdictions. You can deploy any of your services and applications independently to whichever provider is a superior fit for the needs of that particular component, while also redeploying them as often as there is new favorable options to take advantage of. Substituting this dashboard with an API or Terraform provider enables the same capabilities programatically, allowing you to build pipelines which can deploy and redeploy services to different providers automatically depending on stated preferences. One obvious use case for these capabilities is the ability to shop around for the best deal on compute intensive operations, such as model training for machine learning. Whenever you’re ready to begin a new job training a model from a large data set, you can do an active search of the least expensive (or most performant) hardware available on the market to leverage. Another use case is the ability to compare and choose between all resources available that meet required jurisdictional or compliance guidelines, such as GDPR, HIPAA, or ISO. Yet another can be optimizing for uptime, by spreading clusters of the same critical service across multiple different providers as an active risk management strategy. <h2>How It Works</h2> The key component required for unlocking this experience and the subsequent use cases is described by the Sky Above the Clouds authors as a “intercloud brokerage”, or what Hydra Host originally conceived as an “arbitrage layer.” These brokers serve as a middleware layer and API gateway for each cloud provider, providing a unified and standardized interface for leveraging all of their resources. They do the work of integrating and maintaining the interfaces of the various cloud providers, so you can focus on your application level services. Several projects are already pursuing differing visions of this broker-like model today, Hydra Host included. Differing approaches exist with different sets of tradeoffs. The largest distinction to be made in categorizing them so far is what we call either the maximalist approach or the minimalist approach (what the Sky Above the Clouds refers to as a “shim layer”). <h2>Maximalist vs Minimalist</h2> The goal of the maximalist cloud broker approach is to piece together and standardize the interfaces for the various product catalogues of the major cloud service providers. This can include high level services such as EKS, RDS, data analytics tools, and more. The advantage here is the significant demand and use cases for combining the best services from all providers, say RDS from AWS and data analytic tools from GCP, as they are widely used. Given the sheer number of services that cloud providers now provide, and the inherent complexity of higher-level services, there is a disadvantage. Building and maintaining an ever-growing list of products across clouds becomes tricky to scale and maintain even for a company dedicated to the function. On the other hand, the goal of the minimalist approach is to conversely bridge only the lowest levels of cloud and hosting resources together, such as compute, storage, and networking. Once bridged, a protocol like standard is provided on top so higher-level services can be rebuilt on to a common interface. This interface is natively cloud agnostic, meaning applications built on top of it are able to run uniformly in all hosting environments without changes. The advantage to the minimalist approach is that integrations with different providers are far simpler to build and maintain than the maximalist approach. There are fewer moving targets and less complexity. Like any good protocol, this approach makes the “broker” strategy much simpler to understand and lightweight to run. Additionally, it is easier to quickly integrate hosting providers. This expands possibilities far beyond the major cloud providers, including the large landscape of independent data centers and rapidly growing number of edge compute locations. Furthermore, this approach forces providers to compete on the commodity level that is the hardware, increasing competition where it can have the biggest daily impact: your wallet. The downside of the minimalist approach is development complexity. Development shifts from integration work to redeveloping the infrastructure stack, meaning that the platform won’t be ideal right away for every application if they are heavily reliant on higher level cloud services like EKS. This will be overcome through time as the infrastructure stack matures and offers effective analogues to common high level cloud services. <h2>Adoption Roadmap</h2> So, what will the rollout of Sky Computing look like? Right now, adoption is nascent, which provides more upside for the innovators and early adopters. Ultimately, the adoption path will be influenced by one major factor: friction. While cloud brokers make it easy to deploy applications to multiple clouds by offering a unified interface, migrating off cloud interfaces to sky brokers takes the same type of work that makes cloud migration a pain to begin with. This means that similar to current multi-cloud trends, most early adoption of the intercloud broker model will come from new services and applications. Emerging startups and SMBs are uniquely positioned to leverage this technology, as they are relatively unencumbered by sprawling legacy applications. However, another group that that is well positioned to be early adopters includes large enterprises who are already facing the need to undergo infrastructure migrations. Perhaps this is due to recent M&amp;A or simply an effort to dramatically cut costs. For comparable effort, these enterprises can migrate to a Sky Computing model to solve their dilemma and never have to worry about extensive hosting migration pains ever again. <h2>Getting Involved</h2> It’s not often that you’re able to get in on the ground floor of a powerful new paradigm, but here we are. There are multiple “broker” providers to explore which we’ve outlined here (non-exhaustively, we’re sure), and given the early stages all are easy to reach out to. We, of course, encourage you to read the original <a href="https://arxiv.org/ftp/arxiv/papers/2205/2205.07147.pdf" rel="noopener noreferrer" target="_blank">"The Sky Above the Clouds"</a> paper, and other links in this article. Here at Hydra Host we’re working on launching a State of the Sky newsletter you can subscribe to if interested, as well as a community chat which we’ll keep subscribers alerted to. Sky Computing is poised to effectively address rampant pain points in today’s cloud computing landscape and change the entire cost benefit equation distributed computing. Together, we can build a community of early practitioners that can accelerate this change and provide outsized benefits for early adopters. <h2>Inter-Cloud Brokers:</h2> <ul><li>Hydra Host: We’ve been researching and experimenting with various approaches to the “arbitrage/broker” function since April 2021, settling on a Minimalist strategy. We’re working with early adopters to onboard them on to our fully managed platform, with plans to open source our software in the second half of 2023. 2023 roadmap goes beyond public clouds, and integrating independent bare metal value providers. Make sure to contact us for a demo or if interested in our future newsletter, and keep an eye on our blog!</li><li><a href="https://controlplane.com/" rel="noopener noreferrer" target="_blank">Controlplane</a>: Founded in 2019, it allows user to mix and match cloud services from the big 3 (AWS, GCP, Azure) in the vein of our described “Maximalist” Sky strategy.&nbsp;</li><li><a href="https://www.upbound.io/" rel="noopener noreferrer" target="_blank">Upbound</a>: Founded in 2017, Unbound is building a universal cloud platform powered by their crossplane framework, which can be self hosted to help manage a variety of service providers.&nbsp;</li><li><a href="https://www.anyscale.com/" rel="noopener noreferrer" target="_blank">Anyscale</a>: Founded in 2019, Anyscale is a platform to run “Ray” based AI/ML workloads on top of any cloud. From the outside they seem to employ a more minimalist strategy to ensure workloads are inherently more portable, and is focused entirely on the hosted Ray use case, for now.</li></ul> We’ll be expanding this list on an ongoing basis as we see more players in the inter-cloud broker space continue to appear. If we missed anyone, let us know!

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