Artificial Intelligence and Scientific Workloads - HPC on AWS

High-Performance Computing (HPC) is no longer limited to academic supercomputers or national research labs. Today, organizations across engineering, life sciences, finance, cybersecurity, and artificial intelligence rely on HPC techniques to solve problems that exceed the capabilities of traditional IT infrastructure. As datasets grow larger and models become more computationally demanding, access to scalable, performance-optimized compute has become a prerequisite for innovation.

Modern AI workloads - including large language model training, Generative AI, and multi-model neural networks - depend heavily on HPC concepts such as massive parallelism, low-latency communication, and high-throughput storage. These workloads require thousands of CPU or GPU cores working in parallel, exchanging data continuously, and accessing large shared datasets. Traditional on-premises clusters struggle to meet these requirements due to fixed capacity, long provisioning cycle,s and high capital costs.

Why cloud-native HPC matters now

AWS has fundamentally reshaped the HPC landscape by enabling cloud-native HPC environments that can be provisioned in minutes instead of months. Organizations can now access thousands of compute nodes on demand, run workloads at peak parallelism and release resources immediately after completion. This eliminates idle capacity, reduces operational overhead, and transforms HPC into a usage-based operational expense.

The AWS platform provides a wide range of compute options - including CPU-optimized, GPU-optimized, memory-optimized, and Arm-based AWS Graviton instances - allowing teams to match infrastructure precisely to workload requirements. Combined with high-performance networking and scalable storage services, AWS enables both traditional HPC simulations and modern AI workloads to run efficiently at a global scale.

As AI and scientific workloads continue to grow in size and complexity, cloud-native HPC on AWS has become a foundational capability for organizations seeking faster time-to-insight, greater flexibility, and improved cost efficiency.

Several Clouds supports organizations in implementing this architecture end to end. Whether designing the multi-account landing zone, integrating IAM and networking controls, optimizing cluster performance or customizing workflow patterns for domain-specific applications, Several Clouds can help build and automate HPC platforms tailored to each organization’s requirements. Beyond initial deployment, Several Clouds can extend the reference architecture with enhanced monitoring, CI/CD integrations, cost-governance tooling and workload scheduling improvements, ensuring the HPC environment continues to grow in capability and maturity over time.

If you’re evaluating HPC for AI training, scientific computing, or large-scale analytics, download the full whitepaper for the complete architecture overview, service breakdown, and operational best practices.