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TensorRT LLM - NVIDIA's open-source large model inference optimization framework

TensorRT LLM is an inference performance framework for large language models (LLMs) on NVIDIA GPUs, developed by NVIDIA. Based on the PyTorch architecture, TensorRT LLM provides an efficient and easy-to-use Python API...

What is TensorRT LLM?

TensorRT LLM is an inference performance framework for large language models (LLMs) on NVIDIA GPUs, optimized by NVIDIA. Based on the PyTorch architecture, it provides an efficient and easy-to-use Python API, supporting various inference scenarios from single-GPU to large-scale distributed deployments. Through advanced optimizations, TensorRT LLM significantly improves model inference efficiency while maintaining high flexibility and scalability. TensorRT LLM supports multiple popular LLM architectures and integrates seamlessly with NVIDIA's inference ecosystem, providing developers with powerful tools for rapidly deploying and optimizing language models, driving the development of generative AI.

Main functions of TensorRT LLM

  • High-performance inference optimizationThrough customized kernels and optimization techniques, such as tensor parallelism, pipelined parallelism, and expert parallelism, the inference efficiency of the model on NVIDIA GPUs is significantly improved.
  • Advanced quantification technologyIt supports multiple quantization formats (such as FP8, FP4, INT4 and INT8), which improves inference speed and throughput while reducing model accuracy loss.
  • High-efficiency cache managementIt adopts a paginated caching mechanism to optimize memory usage and supports long sequence inference and large-scale deployment.
  • Flexible reasoning and schedulingIt supports in-flight batching and various speculative decoding algorithms (such as Eagle, MTP, and N-Gram) to reduce latency and increase throughput.
  • Multimodal supportIt supports plain text models and multimodal models, such as LLaVA-NeXT and Qwen2-VL, to meet the needs of different application scenarios.
  • Seamless integration and expansionBased on the PyTorch architecture, it is easy to integrate with existing PyTorch models and supports seamless integration with NVIDIA Dynamo and Triton inference servers.
  • Extensive model supportIt supports a variety of popular LLM architectures, such as GPT-OSS, DeepSeek, and Llama.
  • Modular designIt features high modularity and scalability, allowing developers to easily customize and extend functionality to meet specific needs.

How to use TensorRT LLM

  • Install Docker and NVIDIA driversEnsure that Docker and the latest version of the NVIDIA GPU driver are installed on the system to support GPU acceleration.
  • Pull and start the TensorRT LLM containerRun command docker run --rm -it --ipc host --gpus all --ulimit memlock=-1 --ulimit stack=67108864 -p 8000:8000 nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc0Start a Docker container containing TensorRT LLM.
  • Launch online reasoning serviceUsing commands within a container trtllm-serve "TinyLlama/TinyLlama-1.1B-Chat-v1.0" Start the inference service to deploy the model as an online service.
  • Send inference request: via HTTP client (such as curlSend a request to the service, for example:curl -X POST http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{"model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "messages": [{"role": "user", "content": "Tell me about AI."}], "max_tokens": 32}'To obtain the reasoning results.
  • Offline reasoningIn the Python environment, through from tensorrt_llm import LLM Load the model and use llm.generate() The method performs local inference.
  • Optimization and expansionChoose the appropriate quantization format (such as FP8 or FP4) according to your needs, and further optimize inference efficiency using the advanced features provided by TensorRT LLM (such as paging buffers and speculative decoding).

TensorRT LLM project address

  • Project official websitehttps://nvidia.github.io/TensorRT-LLM/
  • GitHub repositoryhttps://github.com/NVIDIA/TensorRT-LLM

Application scenarios of TensorRT LLM

  • Online reasoning serviceNatural language processing services for real-time responses, such as chatbots and intelligent customer service, support high-concurrency requests.
  • Content creation and generationIt is used to generate news, creative writing, and code assistance content, meeting diverse content creation needs.
  • Multimodal applicationsIt combines image and video inputs to support multimodal tasks such as visual question answering and image description generation.
  • Enterprise applicationsIt can be used within enterprises for knowledge management, document generation, and intelligent search to improve work efficiency.
  • Academic research and developmentIt provides model optimization and performance evaluation tools for academic research, and assists in scientific research experiments.
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