This article demonstrates how to deploy PyTorch models on AWS Inferentia instances using FastAPI for optimal hardware utilization and cost efficiency.

• FastAPI with ASGI enables asynchronous request handling for low-latency inference serving
• Deploy multiple models across NeuronCores in parallel for maximum throughput without sacrificing performance
• Use environment variables NEURON_RT_VISIBLE_CORES and NEURON_RT_NUM_CORES to bind processes to specific cores
• Inf2 instances offer 4x higher throughput and 10x lower latency than Inf1 despite fewer cores
• Compile PyTorch models using torch.neuron.trace() for Inf1 or torch.neuronx.trace() for Inf2
• Docker containers isolate model servers, enabling easy scaling and management across NeuronCores
• Monitor utilization with neuron-top CLI tool to track core, vCPU, and memory consumption
• GitHub repository provides complete scripts for model compilation, deployment, and monitoring

This approach maximizes Inferentia accelerator utilization, enabling cost-effective production inference at scale with multiple concurrent models.