Notes on Serving LLMs with TensorRT-LLM and Triton

Notes on Serving LLMs with TensorRT-LLM and Triton 2026-05-31 · LLM serving / NVIDIA stack These are working notes on taking an open-weights LLM from a Hugging Face checkpoint to a production-style serving endpoint on the NVIDIA stack — TensorRT-LLM for the engine, Triton Inference Server for the deployment surface — and benchmarking it honestly against vLLM on multi-GPU hardware. They follow the harness in trtllm-triton-serving (4× H100, NVLink). The goal is to move from "I use vLLM" to "I can stand up the NVIDIA inference stack on real multi-GPU hardware and reason about the trade-offs." 1. The serving pipeline The path from checkpoint to endpoint has four stages. Each one is a place where a decision affects latency, throughput, or accuracy: Checkpoint — a Hugging Face model. Engine build — compile to a TensorRT-LLM engine for a fixed tensor-parallel degree, precision, and batching policy. Model repository — wrap the engine in a Triton tensorrt_llm -backend model repo. Serving + load test — trtllm-serve (or Triton) exposes an OpenAI-compatible endpoint; a load generator drives it under controlled concurrency. The key mental shift from vLLM: TensorRT-LLM does ahead-of-time compilation . vLLM is a runtime that takes the model and serves it; TensorRT-LLM builds an engine specialized to your GPU, TP degree, and precision first. That build is where the performance comes from, and also where the rigidity comes from. 2. Tensor parallelism (TP) For a model that doesn't fit on one GPU — or to cut latency — TensorRT-LLM shards each layer across GPUs. On a 4× H100 NVLink box, TP=4 means every forward pass does an all-reduce across the four GPUs over NVLink. The all-reduce is not free. On this fabric it tops out around 77 % of the NVLink budget (see the separate NVLink-wall notes ). For prefill (large tensors) you're bandwidth-bound and TP helps. For decode (one token at a time) you're pinned against the small-message latency floor, and past a point more TP makes decode slowe