TensorRT-LLM
FreeNvidia Framework for LLM Inference
FreeFree tier
About TensorRT-LLM
TensorRT-LLM is an open-source library from NVIDIA that optimizes inference for large language models (LLMs) and visual generation models on NVIDIA GPUs. It provides an easy-to-use Python API to define models and includes components for building high-performance Python and C++ runtimes. The library features specialized kernels for common operations, an efficient runtime, and a pythonic framework that enables customization and extension. It is designed to deliver state-of-the-art inference performance on NVIDIA hardware.
Key Features
State-of-the-art optimizations for LLM and visual generation model inference
Easy-to-use Python API to define large language models
Python and C++ runtimes for performant inference execution
Specialized kernels for common operations
Efficient runtime with customizable and extensible pythonic framework
Support for both LLMs and visual generation models
Pros & Cons
Pros
- Open-source and free to use
- Optimized specifically for NVIDIA GPUs achieving high performance
- State-of-the-art inference techniques (e.g., sparse attention, speculative decoding)
- Pythonic design makes it accessible for researchers and developers
- Active development with regular tech blogs and updates
- Supports both LLMs and visual generation models
Cons
- Limited to NVIDIA GPUs; no support for other hardware
- Requires deep learning expertise and familiarity with NVIDIA tooling
- Setup and configuration can be complex for beginners
- Documentation is spread across GitHub, tech blogs, and architecture pages
Best For
Deploying large language models for production inference on NVIDIA GPUsRunning visual generation models such as diffusion-based generatorsBuilding custom inference pipelines for LLMs and generative AIAccelerating inference for agent applications and long-context scenarios
FAQ
What is TensorRT-LLM?
TensorRT-LLM is an open-source library from NVIDIA that optimizes inference for large language models (LLMs) and visual generation models on NVIDIA GPUs. It provides a Python API to define models and includes high-performance runtimes in Python and C++.
What models does TensorRT-LLM support?
It supports a wide range of large language models and visual generation models. Specific models are demonstrated in the examples directory and tech blogs on the repository.
Is TensorRT-LLM free to use?
Yes, TensorRT-LLM is open-source and free to use under the LICENSE file in the repository.
How do I get started with TensorRT-LLM?
Visit the GitHub repository for installation instructions, examples, and documentation. The README provides links to architecture docs and performance benchmarks.