TensorRT-LLM
github.comruntimeself-hosted
TensorRT-LLM is a self-hosted runtime in the Inference & Serving layer of the AI stack. NVIDIA-optimized inference with inflight batching and FP8.
By Lattice editorial · Facts verified · method
- licence
- Apache-2.0
- language
- CUDA
- cost
- free
- owned by
- AI Infrastructure
Verified 2026-09build refuses it from 2027-04
Use TensorRT-LLM when
You are committed to NVIDIA hardware and need the last of the throughput.
Skip TensorRT-LLM when
Hardware portability matters, or you have no GPUs to tune against.
Turns weights into throughput. — that is what this layer of the stack is answerable for. See the full Inference & Serving section for the rest of the options.
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[](https://lattice.kunjshah.in/inference-serving/tensorrt-llm)Quick answers
- Is TensorRT-LLM open source?
- Yes. TensorRT-LLM is open source under the Apache-2.0 licence. It is free to use.
- What are the alternatives to TensorRT-LLM?
- The closest alternatives to TensorRT-LLM are vLLM and SGLang.
Reading on inference
- Choosing an inference runtimeThe wrong runtime costs you throughput you never knew you were leaving on the table. The right one is usually decided by traffic shape, not model size.
- Context is a budget, not a containerThe instinct is to keep everything in case it is needed. The models are not filing systems, and past a certain length the middle of your context is doing nothing but costing money.
- Choosing a model is a routing problemThere is no best model. There is a best model for a task, at a quality bar, within a latency and cost budget — and that decision is a routing policy, not a benchmark.
Alternatives to TensorRT-LLM
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