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Serving Engines: vLLM, SGLang and TensorRT-LLM

An inference engine coordinates model work and request state. Memory fit and batch throughput do not establish a response deadline; test the actual execution loop before choosing an engine.

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LESSONS THAT TEACH THIS

Inference EngineeringFollow two requests through one loaded model21 min→Inference EngineeringChoose an engine against explicit responsibilities14 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?→Open-Weights Models & Serving EnginesDeploy one open-weights model on Kubernetes with vLLM. What goes in the manifest, and what breaks first?→LLM Inference & ServingWhat is the difference between static and continuous batching, and why did it change LLM serving?→