As artificial intelligence (AI) adoption accelerates, so does the complexity of the workloads AI teams are tasked with managing. Cloud infrastructure was once the great enabler of innovation, allowing researchers and developers to spin up resources quickly, run experiments, and deploy models with relatively low upfront cost. But now, the scale and nature of AI have outgrown traditional cloud infrastructure models.

Foundational models like GPT-4, LLaMA 2, and Claude 2 require massive GPU power to train and serve effectively. When every second of training time and every watt of power draw can significantly impact both your timeline and budget, performance predictability becomes a strategic advantage. That’s where the promise of bare metal GPU infrastructure enters the conversation.

Most mainstream cloud providers (AWS, Google Cloud, Azure) offer GPU instances in a virtualized environment. While these are great for general workloads, they come with a set of compromises for high-performance AI compute:

• Virtualization Overhead: Hypervisors and shared tenancy mean you’re not getting full access to the GPU’s potential.
• Inconsistent Performance: Noisy neighbors, shared interconnects, and throttling can reduce throughput.
• Limited Flexibility: Quotas, region availability, and provisioning delays slow down workflows.

These challenges become particularly painful at scale. Whether you’re training a 70B parameter LLM or deploying a model for real-time inference, consistency matters more than ever.

Bare metal means full control—no hypervisors, no virtualization layer, no multi-tenancy bottlenecks. IonStream.ai delivers GPU-as-a-Service using dedicated NVIDIA B200 and H200 GPUs on bare metal. The result is:

• True performance isolation
• Direct access to full GPU memory and bandwidth
• Predictable latency and throughput

Our infrastructure is designed specifically for AI workloads, not general-purpose cloud computing. That includes tuned interconnects, optimized software stacks, and low-level access to performance features like NVIDIA’s Transformer Engine.

Modern AI use cases demand real infrastructure. Examples include:

• Training Transformer models that need high memory bandwidth (B200 offers up to 5.3 TB/s)
• Running multi-modal AI applications with fast token throughput
• Deploying real-time inference where response times can’t tolerate virtualized lag

Bare metal GPU infrastructure ensures you’re not sacrificing performance for convenience. And with the rise of GPU-as-a-Service, you don’t need to build your own data center to access it.

At ionstream.ai, we believe in infrastructure that works for the AI era:

• On-demand provisioning of H200 and B200 GPUs
• No noisy neighbors—your workloads run on dedicated, high-performance servers
• Flexible pricing to support experimentation and scale

Unlike traditional cloud platforms, we focus exclusively on high-performance AI infrastructure. That means we’re not hosting websites, databases, or enterprise apps—we’re optimizing every watt and FLOP for AI training and inference.

According to Precedence Research, the global AI infrastructure market is forecast to reach $60 billion by the end of 2025, and nearly $500 billion by 2034. Much of this growth is driven by demand for training and serving large models, which increasingly require high-performance GPUs like the H200 and B200.

Teams that adopt bare metal infrastructure early—especially with access to cutting-edge GPUs—will unlock measurable advantages in both speed and cost.

Bare metal is no longer just for hyperscalers and Fortune 500s. With GPU-as-a-Service platforms like ionstream.ai, anyone building or deploying AI can now access cloud infrastructure purpose-built for AI.

If you’re still relying on virtualized cloud GPUs to train cutting-edge models or deploy real-time inference, it’s time to rethink your stack.

Ready to power your AI with bare metal B200 and H200 performance? Talk to us and see what next-gen infrastructure looks like.