LLM Inference Optimization Made Easy: BentoML llm-optimizer Revolutionizes Model Deployment

Deploying large language models (LLMs) in production environments presents a significant challenge: how to find the optimal configuration for latency, throughput, and cost without relying on tedious manual trial and error. BentoML’s recently released llm-optimizer addresses this exact problem, providing a systematic approach to LLM performance tuning. Why Is LLM Inference Tuning So Challenging? Optimizing LLM inference requires balancing multiple dynamic parameters—batch size, framework selection (such as vLLM or SGLang), tensor parallelism strategies, sequence lengths, and hardware utilization. Each factor influences performance differently, making it extremely difficult to find the perfect combination of speed, efficiency, and cost. Most teams still …