AI training & fine-tuning
Plan model size, precision, training data and target completion time. For distributed workloads, confirm topology, interconnect, storage throughput and recovery requirements.
Discuss a training cluster ↗Built around your workload.
Start with the problem you want to solve. Shape your infrastructure around it.
Production inference
Define concurrency, context length, latency and throughput targets. Evaluate performance with your own model and traffic profile before committing to capacity.
Discuss inference compute ↗Dedicated & bare-metal compute
Specify your requirements for hardware isolation, operating system access, networking, deployment region and support. Agree on responsibilities before delivery.
Discuss dedicated infrastructure ↗