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Serving Engines: vLLM, SGLang and TensorRT-LLM

Three engines serve most open-weight models in production, and they converged on the same mechanisms (paged KV, continuous batching, chunked prefill, prefix caching, speculation, disaggregation) while differing in what they optimize first. vLLM is the default for breadth and hardware coverage, SGLang leads on prefix reuse and structured generation, TensorRT-LLM squeezes the most from NVIDIA hardware at the cost of a compile step. This is a dated page, September 2026; the decision table is what to carry into the room, not the version numbers.

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RELATED CONCEPTS

PagedAttention Continuous Batching Prefix Caching and KV Reuse Disaggregated Prefill and Decode Quantization for Inference Inference Platform Architecture

LESSONS THAT TEACH THIS

Inference EngineeringA serving engine is a scheduler with a memory allocator attached13 min→Inference EngineeringChoose an engine by the layer it occupies, not by its throughput claim14 min→

PRACTICE THIS IN REAL QUESTIONS

LLM Inference & ServingvLLM, SGLang or TensorRT-LLM: which engine do you pick for a new deployment, and what would change your mind?→Open-Weights Models & Serving EnginesYou are serving an agent product with long shared prompts. vLLM or SGLang?→CUDA, Triton & Kernel EngineeringWhat does torch.compile actually do to your model, and when does it fail to help?→Kubernetes, Slurm & GPU SchedulingWe want to run LLM inference on Kubernetes. What does the deployment actually need beyond a Deployment with a GPU limit?→LLM Inference & ServingWhat is the difference between static and continuous batching, and why did it change LLM serving?→Open-Weights Models & Serving EnginesAn SGLang deployment underperforms. Tune it against the project's own published targets.→