This whitepaper is a practical guide for teams building or modernizing high-performance environments for AI training, simulation, scientific research, and large-scale analytics. It explains the core principles behind HPC and translates them into real AWS architecture decisions - so you can scale demanding workloads without the capital expense, long lead times, and operational complexity of traditional on-premises clusters.

You’ll get a clear, architecture-driven overview of the AWS building blocks for HPC at any scale - covering compute selection (x86, GPU, and AWS Graviton), low-latency networking with placement groups and EFA, high-throughput storage patterns with FSx for Lustre and S3, and secure data ingestion through DataSync and Direct Connect.

The paper also includes guidance on governance, security, limitations, and operational best practices to help you run HPC workloads with consistent performance and predictable cost.

• Design elastic HPC architectures that scale from tens to thousands of nodes
• Select the right compute architecture (x86, Arm/Graviton, GPU, Trainium) for different workloads
• Run tightly coupled MPI workloads using low-latency, high-bandwidth networking
• Optimize cost, performance and energy efficiency across HPC environments
• Build secure, governed and repeatable HPC platforms using AWS native controls
• Support AI model training, inference and simulation workloads at extreme scale
• Ingest and manage terabyte- to petabyte-scale datasets efficiently