BoltzGen on Amazon SageMaker AI accelerates protein binder design by managing GPU compute infrastructure end to end. BoltzGen is a diffusion-based generative model that designs proteins and peptides capable of binding to specific biomolecular targets. A typical design campaign involves multiple GPU-intensive steps: backbone generation, inverse folding, structural validation, and candidate ranking. On a 4-GPU instance (ml.g5.12xlarge), a campaign of 1,000 samples takes approximately 375 hours to complete. SageMaker AI provisions GPU instances, executes BoltzGen inside the container, writes results to Amazon Simple Storage Service (Amazon S3), and releases the instances when processing completes. Billing is per-second, so there are no idle GPU costs. A 2-hour design run on ml.g4dn.xlarge costs approximately $1.50 based on on-demand pricing. The implementation supports multi-GPU parallelization within a single instance and multi-instance scaling across a pipeline. In pipeline mode, each step’s output is cached in Amazon S3 with a 7-day expiry, so the design generation step that accounts for approximately 90 percent of compute cost does not re-run when iterating on filtering parameters. Setup scripts build the container and push it to Amazon Elastic Container Registry (Amazon ECR), allowing users to submit a first design job within minutes.