The Inference Engine Wars: How SGLang, vLLM, and LMDeploy Are Redefining LLM Production Deployment in 2026
The LLM serving landscape has fundamentally shifted. What was once a simple choice between HuggingFace Transformers and early optimization frameworks has evolved into a sophisticated ecosystem where three engines dominate: SGLang, vLLM, and LMDeploy. The throughput gap between them—up to 29%—translates to tens of thousands of dollars in monthly GPU costs at production scale. This isn’t just about speed. Each engine embodies a fundamentally different philosophy about how to solve the same problems: memory fragmentation, computation redundancy, and the tension between latency and throughput. Understanding these architectures is essential for making the right deployment decision. ...