Best Practices

Production-ready guidelines for optimizing performance, managing costs, and operating ML workloads on AWS Inferentia and Trainium.

Compilation Optimization

• Pre-compile models: Always compile during CI/CD, never at runtime; cache compiled artifacts in S3
• Pin input shapes: Use fixed batch sizes and sequence lengths to avoid recompilation; pad inputs to match
• Use parallel compilation: Run neuron_parallel_compile to compile subgraphs concurrently
• Version-lock SDK: Pin your Neuron SDK version; compiled models are not portable across versions
• Benchmark data types: Compare BF16 vs FP16 accuracy and throughput for your specific model

Performance Tuning

Cost Optimization

• Right-size instances: Start with the smallest Inf2 instance that fits your model; scale horizontally
• Use Savings Plans: Commit to 1-year or 3-year compute savings plans for predictable workloads
• Spot instances: Use Spot for fault-tolerant batch inference workloads for up to 90% savings
• Multi-model endpoints: Serve multiple models on a single instance to maximize utilization
• Auto-scaling: Scale to zero during off-peak hours; use scheduled scaling for predictable patterns

Monitoring and Operations

Production Checklist

• Model validation: Compare Neuron inference output against CPU/GPU baseline for accuracy regression
• Load testing: Benchmark with realistic traffic patterns before production deployment
• Rollback plan: Maintain GPU-based fallback endpoints for critical workloads
• Health checks: Implement NeuronCore health checks in your serving container
• Logging: Capture compilation logs, runtime errors, and inference metrics
• SDK updates: Test new Neuron SDK versions in staging before production upgrades