Why Bare Metal Matters for AI Training

When you're training foundation models across hundreds or thousands of GPUs, every percentage point of overhead matters. Virtualized GPU instances — the default offering from most cloud providers — introduce a measurable performance tax that compounds at scale.

The virtualization tax

Hypervisor-based GPU passthrough adds 10–15% overhead on memory-intensive workloads. For a single GPU instance running inference, this is negligible. For a 512-GPU training cluster running for months, it translates directly into longer training times and higher costs.

Bare metal eliminates this entirely. Your workloads run directly on the hardware with no abstraction layer between your code and the GPU.

Direct interconnect access

NVLink and InfiniBand were designed for direct hardware communication. Virtualization layers can interfere with RDMA operations and introduce latency in collective communication patterns like all-reduce — the backbone of distributed training.

On bare metal, you get the full bandwidth of NVLink between GPUs within a node and InfiniBand NDR/NDR400 between nodes, exactly as the hardware was designed to operate.

Predictable performance

Noisy-neighbor effects don't exist on dedicated hardware. Your training runs produce consistent throughput numbers from hour one to hour ten thousand. This predictability matters for capacity planning and cost modeling.

When cloud makes sense

Bare metal isn't for everyone. If you need a single GPU for a few hours of fine-tuning, on-demand cloud instances are the right choice. Bare metal superclusters are for teams that need sustained, large-scale compute with predictable performance and economics.

If that sounds like your workload, get in touch.