Building Blocks for Foundation Model Training and Inference on AWS

Failure to architect for the full model lifecycle leads to chronic underutilization of expensive accelerator clusters and stalled training progress. Organizations that master these integration points gain the ability to scale complex reinforcement learning and long-thinking inference strategies efficiently. A primary use case involves optimizing multi-node H100 or H200 deployments to reduce checkpointing overhead during distributed pre-training. However, a persistent limitation remains the operational complexity of managing observability across disparate hardware and software layers.

The industry has shifted from the Kaplan et al. (2020) model of scaling compute to a three-pronged approach involving pre-training, post-training, and test-time compute. Infrastructure requirements have evolved to prioritize memory bandwidth and interconnect capacity to support these diverse workloads. The following table illustrates the technical progression of GPU capabilities available on AWS:

While throughput remains critical, the move toward FP8 and FP4 precision limits current software support for legacy model architectures.

This week

• Audit current cluster utilization metrics to identify bottlenecks in data loading versus compute saturation.
• Review existing Kubernetes or Slurm configurations for compatibility with high-bandwidth P5 instance networking.

This quarter

• Benchmark current model training workflows against H200 or upcoming B200 instance performance profiles.
• Standardize observability tooling using Prometheus and Grafana across all distributed training environments.

This year

• Migrate legacy training pipelines to frameworks optimized for distributed HBM utilization.
• Refactor inference architectures to incorporate test-time compute strategies beyond simple pre-training scaling.