For most teams, start with vLLM. It is open source, runs almost any new model within days of release, works on NVIDIA and other hardware, and delivers strong multi-user throughput with little tuning. Choose TensorRT-LLM when you run on NVIDIA GPUs only, serve a small, stable set of models at high volume, and have the engineering time to tune for the last margin of latency or throughput, or when you are using NVIDIA's packaged NIM services, which can use it under the hood. Neither is always faster. Results depend on the model, the precision, the GPU and the traffic pattern, so the honest answer includes a benchmark on your own workload.
TensorRT-LLM vs vLLM at a glance
| vLLM | TensorRT-LLM | |
|---|---|---|
| Maintained by | Open-source community; originated at UC Berkeley | NVIDIA |
| Licence | Apache 2.0 | Apache 2.0, built on NVIDIA's TensorRT |
| Hardware | NVIDIA, plus AMD, Intel and others | NVIDIA GPUs only |
| New model support | Very fast; loads Hugging Face models directly | Narrower list; popular models prioritised |
| Setup | One command to serve a model | More steps; engine building or configuration per model and GPU |
| Batching | Continuous batching | In-flight batching, the same idea |
| Low-precision formats | FP8, and FP4 formats on supported GPUs | FP8 and NVFP4, with NVIDIA's own optimisation recipes |
| API | OpenAI-compatible server built in | OpenAI-compatible server; also used inside NIM and Triton |
Source: project documentation and NVIDIA technical blogs (2025 and 2026). Compiled 11 Nov 2026. Both projects change quickly.
Which one is faster?
It varies, and published tests disagree. TensorRT-LLM compiles or configures a model specifically for the GPU, which can produce the best latency for a tuned deployment, and it is where NVIDIA's newest hardware features tend to appear first. vLLM has closed much of the gap and is often ahead out of the box. In one published test of OpenAI's gpt-oss-120b on an RTX PRO 6000 Blackwell Max-Q, an engineering firm found TensorRT-LLM slightly quicker to the first token while vLLM was better on the other measures, and chose vLLM as its baseline. That is one model on one card. Treat any single benchmark, including that one, as a hint.
What reliably matters more than the engine:
- Precision. An FP4 or FP8 model on a GPU that accelerates it natively. See quantization formats.
- Concurrency settings. How many requests run together. See continuous batching.
- Context length. Long prompts fill the KV cache and cut how many users fit.
- Speculative decoding. Both engines support it. See how it works.
What about model support?
This is where vLLM usually wins for businesses. New open models, vision-language models, embedding models and unusual architectures tend to work in vLLM first, often on release day. TensorRT-LLM concentrates on widely used models and on getting the most from them. If your plan involves trying several models over the next year, flexibility is worth more than a few percent of throughput.
How much operational work is each?
- vLLM: a Python package or container, one command, sensible defaults. Upgrades are frequent, so pin versions and test before updating.
- TensorRT-LLM: more moving parts. Optimised engines are tied to a GPU type and software version, so a hardware or driver change can mean rebuilding. The reward is a tightly optimised, repeatable deployment.
- NIM: NVIDIA's packaged route hides much of this, at the cost of a licence for production. See NVIDIA NIM vs Ollama.
The engine is the last ten percent. Model choice, precision and batch settings are the first ninety. Pick the engine your team can operate, then tune those three.
How should you benchmark them?
- Use your real prompts. Short chat and long document questions behave very differently.
- Test at your real concurrency. Measure at 1, 8 and 32 simultaneous requests, or whatever matches your peak.
- Record three numbers. Time to first token, tokens per second per user, and total tokens per second.
- Hold everything else equal. Same model, same precision, same GPU, same context limit.
- Check answer quality. A faster configuration that changes the output isn't a fair comparison.
- Repeat after upgrades. Rankings change between releases.
Which should you use?
| Situation | Engine |
|---|---|
| First production deployment; models may change | vLLM |
| Mixed or non-NVIDIA hardware | vLLM |
| One or two models at high, steady volume on NVIDIA GPUs | Benchmark TensorRT-LLM |
| You want vendor support and packaging | NIM |
| Single user on a desktop | Neither; a simple runner is enough |
For the single-user end, see Ollama vs LM Studio vs vLLM. For deployment detail, see our vLLM guide for DGX Station and AI model serving explained.
Buying in Singapore
Both engines run on Blackwell GPUs with native FP4 and FP8: the NVIDIA RTX PRO 4500 Blackwell (S$8,880), RTX PRO 5000 Blackwell (S$14,971.20), RTX PRO 6000 Blackwell Workstation Edition (S$29,100) and, for servers, the Server Edition (S$32,999). For a compact team server, the NVIDIA DGX Spark 4TB (S$9,450) (listed 11 Nov 2026; prices change; ask for a current quotation). DGX Station GB300 systems such as the HP ZGX Fury G1n are price on request. To start, use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.
Frequently asked questions
What is the difference between TensorRT-LLM and vLLM? vLLM is a community open-source inference server that runs many models on several hardware vendors with little setup. TensorRT-LLM is NVIDIA's engine that optimises models specifically for NVIDIA GPUs and needs more configuration.
Is TensorRT-LLM faster than vLLM? Sometimes. It can give the best latency for a tuned deployment on NVIDIA GPUs, but vLLM is often equal or ahead out of the box. Results depend on the model, precision, GPU and traffic.
Does vLLM work on AMD GPUs? Yes. vLLM supports AMD GPUs through ROCm as well as NVIDIA and other accelerators. TensorRT-LLM is NVIDIA-only.
Which engine does NVIDIA NIM use? NIM containers select an optimised backend for the GPU and model, which can include TensorRT-LLM or vLLM, so NIM is a packaging choice rather than a third engine.
