AI Model Training
Train at scale without building your own supercomputer. OWS provisions multi-node GPU clusters, optimizes network and storage for collective operations, and keeps your team focused on model quality.
What we deliver
- Reference stacks — PyTorch FSDP, Megatron-style parallelism, DeepSpeed-compatible images maintained by OWS.
- Interconnect — NVLink within nodes; InfiniBand or high-bandwidth Ethernet between nodes for low tail latency.
- Checkpoint & recovery — Resilient checkpoint paths to object storage; automatic restart policies on node loss.
- Elastic bursts — Surge onto OWS PowerGrid when you need thousands of GPUs for a short window, then release.
Typical engagement
- 1Discovery — workload profile, SLOs, data residency, and budget.
- 2Architecture — cluster topology, APIs, and integration points.
- 3Pilot — limited production or benchmark phase with clear exit criteria.
- 4Scale — hardening, FinOps, and continuous optimization.
Architecture & security
Designs are adapted per customer: VPC-style isolation, encryption in transit and at rest, secrets management, and least-privilege access to control planes. We document data flows for security review and support private connectivity options where required.
Success metrics
We align on measurable outcomes — training throughput (tokens or samples per dollar), inference p99 latency, cost per 1M tokens, job completion rates, and uptime against agreed SLOs. Dashboards and monthly reviews keep both teams honest.
Related products
This solution composes OWS products. Your team can start from any layer and expand.