TensorRT-LLM
TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.
TensorRT-LLM is NVIDIA's toolkit for running AI language models faster on NVIDIA GPUs by compiling them into an optimized format, enabling lower latency and higher throughput in production deployments.
TensorRT-LLM is a toolkit from NVIDIA for running AI language models faster on NVIDIA graphics cards. Language models are the systems behind chatbots and text generators like GPT. Running these models quickly requires a lot of computation, and this library squeezes more performance out of the hardware by compiling the model into an optimized format before running it.
The core idea is that a language model loaded directly from a research framework is not as fast as it could be on dedicated hardware. TensorRT-LLM takes those models and applies a set of low-level optimizations specific to NVIDIA GPUs, including techniques that reduce memory usage and increase how many requests the system can handle at once. The result is faster responses and the ability to serve more users simultaneously compared to running the model without these optimizations.
The library supports a wide range of popular language models and works with multi-GPU setups, meaning you can spread a large model across several graphics cards to handle models that would not fit on one. It also supports image and video generation models, not just text. Developers interact with it using Python, and it includes examples and documentation for common use cases.
This tool is primarily aimed at engineers deploying AI models in production, such as building an API that responds to user queries. It is not a tool for training models or for casual experimentation without programming knowledge. NVIDIA publishes a series of technical blog posts linked from the repository that describe specific performance improvements and advanced configuration options for those who want to dig into the details.
Where it fits
- Optimize a language model to run faster on NVIDIA GPUs and reduce response latency in a production API.
- Spread a large language model across multiple GPUs to handle models that would not fit on a single card.
- Build a scalable AI API that serves more simultaneous user requests by using GPU memory optimizations.