AI Cloud · GPUs

GPU Compute

Heterogeneous GPU pool across Bare Metal, Virtual Machines and Containers. Pick a form factor, then pick AMD or NVIDIA silicon.

Form factors

Three ways to consume a GPU

Same silicon, three consumption models. Pick the one that matches your workload — or mix and match within a single private cluster.

Single-tenant physical servers with direct PCIe access. Best for training large models and latency-sensitive HPC workloads.

KVM-based GPU passthrough for multi-tenant workloads. Production-grade isolation with predictable performance.

GPU-accelerated Kubernetes pods with shared or dedicated GPUs. Elastic scaling from 1 to 1000+ GPUs.

By vendor

Same fabric, same scheduler, two leading GPU ecosystems. Build on either, or split workloads by silicon strength.

MI350X (288GB HBM3e), MI300X (192GB HBM3) and MI325X (128GB HBM3). Exceptional memory capacity for LLM training and ROCm cost-efficient inference.

H200 (141GB HBM3e) and RTX PRO 6000 (96GB GDDR7). The world's most mature AI ecosystem with full CUDA support.

RoCE v2 and NDR InfiniBand fabrics with sub-2µs latency. All GPUs are fabric-connected by default.

Parallel filesystem with 500+ GB/s and S3-compatible object storage with 11-nines durability.

Talk to our team about a custom GPU cluster, managed Slurm or one of our vertical AI solutions.