Optimizing LLM inference on Amazon SageMaker AI with BentoML’s LLM- Optimizer

This article demonstrates how to optimize LLM inference on Amazon SageMaker AI using BentoML's LLM-Optimizer tool, replacing manual trial-and-error tuning with automated benchmarking.

• BentoML's LLM-Optimizer systematically benchmarks parameter configurations to find optimal serving settings
• Theoretical roofline analysis estimates GPU performance before empirical testing begins
• Key tuning parameters: tensor parallelism degree, batch size, sequence length, concurrency limits
• Benchmark generates Pareto dashboard showing latency vs. throughput trade-offs across configurations
• Qwen3-4B on ml.g6.12xlarge achieved 7.51 req/s with 4-way tensor parallelism vs. 2.74 baseline
• Optimal configuration reduced p99 latency to 24 seconds while doubling throughput
• SageMaker LMI containers deploy optimized vLLM configurations via environment variables
• Workflow bridges experimentation and production, eliminating manual infrastructure tuning

By automating LLM inference optimization, teams can achieve 2-4x better resource efficiency and deploy production-ready models in hours instead of weeks.