Scaling foundation models currently relies on a multi-dimensional approach, evolving from simple pre-training compute increases to include post-training methods and test-time compute. This transition requires a converged infrastructure architecture consisting of high-bandwidth networking, distributed storage, and tightly coupled accelerator compute. On AWS, these requirements are met through a layered stack where hardware infrastructure supports resource orchestration via Slurm or Kubernetes, enabling frameworks like PyTorch and JAX to manage model development.

The hardware foundation utilizes NVIDIA GPU-based Amazon EC2 instances. The P5 family leverages H100 and H200 GPUs, while the P6 family integrates Blackwell B200 and Blackwell Ultra B300 architectures. Performance scales through peak Tensor throughput and interconnect bandwidth. For instance, the B300 offers up to 288 GB HBM3e capacity per GPU with 8 TB/s bandwidth. Intra-node communication uses NVLink for low-latency connectivity, while inter-node communication is handled by the Elastic Fabric Adapter (EFA), which provides OS-bypass networking using the Scalable Reliable Datagram (SRD) protocol. EFAv4, available on P6 instances, improves collective communication performance by 18% over EFAv3.

To manage storage at scale, a tiered hierarchy is used, including local NVMe SSDs, Amazon FSx for Lustre for high-throughput distributed file access, and Amazon S3 for durable persistence. For workloads requiring high-intensity communication, Amazon EC2 UltraClusters provide petabit-scale nonblocking networks. Furthermore, P6e-GB200 UltraServers, built on the NVIDIA GB200 NVL72 platform, extend the NVLink domain to up to 72 Blackwell GPUs, using NVLink-C2C to allow cache-coherent access between CPU and GPU memory. These systems are orchestrated using Slurm, favored for its atomic job scheduling and topology-aware placement, or Kubernetes, which provides a declarative API-driven approach to cluster management. Managed services such as AWS Parallel Computing Service and Amazon SageMaker HyperPod facilitate these deployments for large-scale training tasks.

Scaling foundation models currently relies on a multi-dimensional approach, evolving from simple pre-training compute increases to include post-training methods and test-time compute. This transition requires a converged infrastructure architecture consisting of high-bandwidth networking, distributed storage, and tightly coupled accelerator compute. On AWS, these requirements are met through a layered stack where hardware infrastructure supports resource orchestration via Slurm or Kubernetes, enabling frameworks like PyTorch and JAX to manage model development.

The hardware foundation utilizes NVIDIA GPU-based Amazon EC2 instances. The P5 family leverages H100 and H200 GPUs, while the P6 family integrates Blackwell B200 and Blackwell Ultra B300 architectures. Performance scales through peak Tensor throughput and interconnect bandwidth. For instance, the B300 offers up to 288 GB HBM3e capacity per GPU with 8 TB/s bandwidth. Intra-node communication uses NVLink for low-latency connectivity, while inter-node communication is handled by the Elastic Fabric Adapter (EFA), which provides OS-bypass networking using the Scalable Reliable Datagram (SRD) protocol. EFAv4, available on P6 instances, improves collective communication performance by 18% over EFAv3.

To manage storage at scale, a tiered hierarchy is used, including local NVMe SSDs, Amazon FSx for Lustre for high-throughput distributed file access, and Amazon S3 for durable persistence. For workloads requiring high-intensity communication, Amazon EC2 UltraClusters provide petabit-scale nonblocking networks. Furthermore, P6e-GB200 UltraServers, built on the NVIDIA GB200 NVL72 platform, extend the NVLink domain to up to 72 Blackwell GPUs, using NVLink-C2C to allow cache-coherent access between CPU and GPU memory. These systems are orchestrated using Slurm, favored for its atomic job scheduling and topology-aware placement, or Kubernetes, which provides a declarative API-driven approach to cluster management. Managed services such as AWS Parallel Computing Service and Amazon SageMaker HyperPod facilitate these deployments for large-scale training tasks.