Distributed Data Centers

Tell us about the workload, whether it's a training run, a fine-tune or steady inference traffic. We allocate capacity from across our data center network to fit it.

Options:

• Reserved capacity for long-running jobs
• On-demand capacity for bursts
• Placement close to your users or your data

Multi-GPU nodes configured for distributed training, with storage placed close to the compute.

Ready for:

• Fine-tuning and continued pre-training of open models
• Distributed training with PyTorch, DeepSpeed or FSDP
• Scheduling with Kubernetes or Slurm

Dedicated endpoints for open models, tuned by the same team that does our AI Inference work.

Includes:

• vLLM or SGLang serving with OpenAI-compatible APIs
• Autoscaling to match your traffic
• Latency and throughput monitoring

We build on this infrastructure ourselves, so it's handed over ready to work rather than as bare machines.

Handled for you:

• Drivers, CUDA and container images
• Networking and access control
• Monitoring and support