AWS GPU Optimization

Software Sushi’s GPU Optimization on AWS is a professional service designed to help organizations get better performance and value from GPU-based workloads running on AWS. Whether customers are training machine learning models, running AI inference, processing large datasets, supporting computer vision workloads, performing simulations, or powering rendering pipelines, our team helps evaluate and optimize the AWS infrastructure supporting those workloads.

Through this engagement, Software Sushi reviews the customer’s current GPU usage, workload requirements, instance selection, scaling patterns, storage configuration, networking needs, deployment architecture, monitoring setup, and cost profile. We identify opportunities to improve utilization, reduce waste, select better-fit AWS instance types, optimize compute configuration, tune workload execution, and improve the balance between performance and cost.

The service may include analysis of AWS GPU instance families, Amazon EC2 configuration, Amazon SageMaker workloads, containerized GPU workloads on Amazon ECS or Amazon EKS, storage performance, data movement, autoscaling, batch processing, monitoring, and cost management. Depending on the workload, recommendations may also include right-sizing, scheduling improvements, Spot Instances, Savings Plans, model optimization, inference endpoint optimization, or architecture modernization.

At the end of the engagement, Software Sushi provides clear findings, performance and cost optimization recommendations, and a prioritized roadmap for improving GPU workload efficiency on AWS. Our approach helps customers reduce unnecessary GPU spend, improve workload reliability, and ensure their AWS architecture is aligned with the technical demands of GPU-intensive applications.