Why it matters

TensorRT-LLM often fastest on NVIDIA. Understanding enables optimal NVIDIA deployment.

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The architecture

Model compilation: convert model to TensorRT engine. Optimizations baked in.

Runtime: efficient serving.

TensorRT-LLM flowModel conversionPyTorch → TRT engineOptimizationskernels + quantRuntime servinghigh throughputModel-specific optimizations mean fewer models supported vs vLLM
TensorRT-LLM pipeline.
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How it works end to end

Quantization: FP8, INT8, INT4 with post-training or QAT.

Tensor parallel + pipeline parallel across GPUs.

Speculative decoding + Medusa integration.

Triton Inference Server integration for serving.