GPU clusters for AI model training
Training runs fail on infrastructure, not on math. We operate the cluster, the interconnect and the checkpoint path so a multi-week run survives the hardware underneath it.
RDMA fabric
Interconnect
Multi-node
Topology
Shared NVMe
Checkpointing
24 / 7
Operations
Multi-node training that stays up for weeks
Dedicated GPU nodes with root access: bare metal, containers or a managed Kubernetes namespace
High-bandwidth, low-latency interconnect configured and NCCL-validated before handover
Shared high-throughput storage for datasets and checkpoints, sized to the run
Health monitoring on GPU temperature, ECC errors, link flaps and job liveness
Node replacement and run resumption handled by our operations team, not yours
Before you ask
- Can I bring my own training framework and container image?
- Yes. Nodes are handed over with root access and the NVIDIA Container Toolkit installed. You can run your own images, your own scheduler, or ask us to configure Slurm or Kubernetes for you.
- What happens when a node fails mid-run?
- Our monitoring covers GPU temperature, ECC errors and interconnect link state, so degradation is usually caught before a job dies. When a node does fail we replace it and help resume from the last checkpoint, using the same operational discipline we built running proof-of-work fleets since 2019.
- Do you support multi-node distributed training?
- Yes. Multi-node topologies are validated with NCCL bandwidth and latency tests before handover, so you are not debugging our fabric on your training budget.
Other workloads
AI Inference
Production inference on vLLM, SGLang or TensorRT-LLM, metered per token and reachable through a single API surface.
Fine-tuning
LoRA, QLoRA and full-parameter fine-tuning, followed by the quantization and evaluation work that makes the result deployable.
On-premise
Localized deployment for teams whose data cannot leave the building, including non-NVIDIA accelerators.