Get efficient Large Language Model inference with TensorRT-LLM, a Python API for founders, backed by 14k+ GitHub stars.
intermediateโฑ 30 minutes๐ต Free + LLM API costs
14,112 stars2,557 forksPythonQuality 8/10Updated 7/14/2026100% free ยท open source
What it is
Use Python to define and run Large Language Models.
What you can make with it
Agents that perform tasks like answering customer queries, generating product descriptions.
How it helps
TensorRT LLM helps users perform inference efficiently on NVIDIA GPUs, saving time and costs.
Real use case example
"A founder uses TensorRT LLM to create a customer support chatbot. They write a Python script to define the model, train it on customer data, and deploy it on their GPU. The chatbot answers common questions, freeing up human support staff to focus on complex issues."
If you're new
Picking up this skill takes some prior programming experience and familiarity with Python and AI concepts.
If you're senior
Senior engineers and professionals use TensorRT LLM for demanding language model applications requiring high performance and efficient inference.
Common confusion cleared up
Don't confuse TensorRT LLM with other AI engines; it's specifically designed for large language models and NVIDIA GPU acceleration.
Best inside these AI tools
Claude DesktopCodex CLIContinue
Pairs with
Stripe webhookNotion databaseGemini
Why we list it on WorkflowStacks: A marketplace of AI tools includes this for access to state-of-the-art optimizations.
What it does
TensorRT-LLM provides an efficient way to run Large Language Models on NVIDIA GPUs using a simple Python API
Install / run
pip install tensorrt-llm
When to use it
โขWhen you need to deploy LLMs in production with high performance and low latency
โขWhen you want to optimize your LLM inference workflow on NVIDIA GPUs
โขWhen you need a Python-friendly interface to define and run LLMs
Quick start
1Clone the TensorRT-LLM repository from GitHub using `git clone https://github.com/NVIDIA/TensorRT-LLM.git`
2Navigate to the repository directory using `cd TensorRT-LLM`
3Install the required dependencies using `pip install -r requirements.txt`
4Define an LLM model using the Python API, for example, `from tensorrt_llm import LLM; model = LLM('model_name', 'model_path')`
5Run the model inference using `model.infer('input_text')`
Ready-to-paste prompt
python -c 'from tensorrt_llm import LLM; model = LLM("bert-base-uncased", "https://example.com/model.pth"); print(model.infer("What is the meaning of life?"))'
Heads up: You need to have an NVIDIA GPU and the CUDA Toolkit installed to use TensorRT-LLM, as well as a compatible version of Python (currently Python 3.8 or 3.9)
Saves to your device
Topics
blackwell
cuda
llm-serving
moe
pytorch
What's inside โ free to inspect
No purchase needed
Read the entire source before you build โ unlike paid marketplaces that hide it behind a buy button.