Blog: What Is Bare Metal—and Why It Matters for AI Infrastructure

What Is Bare Metal—and Why It Matters for AI Infrastructure

When you're pushing the boundaries of AI model training or need rock-solid performance for real-time inference, infrastructure selection and configuration is everything. One option that’s gaining renewed attention in the AI space is bare metal—and for good reason.

What Is Bare Metal?

At its core, bare metal means you’re running directly on the physical server—no virtualization layer, no hypervisor, and no resource contention with other tenants. You get dedicated access to the CPU, memory, PCIe lanes, and GPUs. That’s a huge deal for AI workloads, where predictability and throughput can make or break a project.

With bare metal, there are no noisy neighbors, no hypervisor overhead, and no interruptions due to spot instance reclaims. You own the machine in full, and that translates to maximum performance, minimal latency, and full control.

For compute-heavy tasks like training trillion-parameter models or deploying high-throughput inference APIs, this level of control and resource availability is critical. In fact, benchmarks have shown that bare metal can deliver up to 15–30% higher GPU throughput compared to virtualized environments, depending on the workload and configuration.

When Bare Metal Makes Sense—and When It Doesn’t

That said, bare metal isn’t always the right tool for the job.

If you're doing exploratory work, spinning up short-term prototypes, or running workloads that scale up and down unpredictably, the flexibility of virtual machines (VMs) deployments might outweigh the performance cost. You can spin up a few GPUs quickly, iterate fast, and shut them down when you're done—without managing low-level configuration or hardware directly.

But there’s a middle ground: Kubernetes on bare metal. In this setup, you avoid the overhead of a hypervisor but still get the flexibility of containers, orchestrated with tools like Containerd or CRI-O. You can autoscale your inference pods, manage deployments, and take advantage of the ecosystem of cloud-native tools—without giving up true GPU passthrough or bare-metal level performance.

This hybrid model has become a preferred option for teams who want both flexibility and raw speed, especially when running containerized LLMs or agent systems that require low latency.

Not All Bare Metal Is Created Equal

If you’re considering a bare metal provider, it’s important to understand that hardware alone isn’t enough. The infrastructure around it matters just as much.

Here’s what to evaluate:

• Data Center Quality: Look for Tier III or higher facilities with redundant power and cooling. If your models are mission-critical, you can’t afford downtime due to infrastructure gaps.
  
  
• SLA-backed Reliability: Uptime guarantees should be 99.9% or better, backed by meaningful service credits. AI workloads can run for weeks—you need hardware that won’t flake mid-run.
  
  
• Fast Networks without Over-subscription: Ensure every GPU and CPU gets the bandwidth it expects, without contention or bottlenecks.
  
  
• Blazing-fast Scaled Off-node Storage: Is data streaming to your GPUs at the same pace they’re capable of processing it? Great storage can greatly increase efficiency.
  
  Support Quality: Even if you’re an expert, you’ll eventually need help. Whether it’s kernel tuning, BIOS updates, or GPU driver mismatches, having real engineers available at 3 a.m. makes a huge difference. Look for 24/7 support with a track record of responsiveness and technical depth.
  
  
• Hardware Lifecycle and Flexibility: Can you request specific firmware versions? Swap nodes or expand easily? True bare metal providers offer that kind of transparency and configurability.
  
  

Bare Metal, Backed by Experts

At Corvex, we provide liquid-cooled, AI-optimized bare metal infrastructure designed for serious workloads. Whether you're training large language models or deploying inference pipelines with zero latency tolerance, our clusters are engineered for speed, reliability, and scale.

And we don’t just hand over the keys—we support you with real experts, 24/7, who understand the difference between a driver issue and a PCIe bottleneck.

Bare metal is not for everyone. But if you’re pushing the limits of what AI can do—and you need infrastructure that keeps up—bare metal might just be the edge you need.

Ready to run your AI workloads on infrastructure built for performance?
Talk to us at Corvex—we’re here to help you scale with confidence.

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