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<li class="toctree-l1"><a class="reference internal" href="../blogs/H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/H200launch.html">H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/Falcon180B-H200.html">Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/quantization-in-TRT-LLM.html">Speed up inference with SOTA quantization techniques in TRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
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<section id="tensorrt-llm-architecture">
<span id="architecture-overview"></span><h1>TensorRT-LLM Architecture<a class="headerlink" href="#tensorrt-llm-architecture" title="Link to this heading"></a></h1>
<p>TensorRT-LLM is a toolkit to assemble optimized solutions to perform Large Language Model (LLM) inference. It offers a Model Definition API to define models and compile efficient <a class="reference external" href="https://developer.nvidia.com/tensorrt">TensorRT</a> engines for NVIDIA GPUs. It also contains Python and C++ components to build runtimes to execute those engines as well as backends for the <a class="reference external" href="https://developer.nvidia.com/nvidia-triton-inference-server">Triton Inference
Server</a> to easily create web-based services for LLMs. TensorRT-LLM supports multi-GPU and multi-node configurations (through MPI).</p>
<p>As a user, the very first step to create an inference solution is to either define your own model or select a pre-defined network architecture (refer to <span class="xref std std-ref">models</span> for the list of models supported by TensorRT-LLM). Once defined, that model must be trained using a training framework (training is outside of the scope of TensorRT-LLM). For pre-defined models, checkpoints can be downloaded from various providers. To illustrate that point, a lot of examples in TensorRT-LLM use model weights obtained from the <a class="reference external" href="https://huggingface.co">Hugging Face</a> hub and trained using <a class="reference external" href="https://developer.nvidia.com/nemo">NVIDIA Nemo</a> or <a class="reference external" href="https://pytorch.org">PyTorch</a>.</p>
<p>Equipped with the model definition and the weights, a user must use TensorRT-LLM’s Model Definition API to recreate the model in a way that can be compiled by TensorRT into an efficient engine. For ease of use, TensorRT-LLM already supports a handful of standard models.</p>
<p>Together with the Model Definition API to describe models, TensorRT-LLM provides users with components to create a runtime that executes the efficient TensorRT engine. Runtime components offer beam-search, along with extensive sampling functionalities such as top-K and top-P sampling. The exhaustive list can be found in the documentation of the <a class="reference internal" href="../advanced/gpt-runtime.html#gpt-runtime"><span class="std std-ref">C++ GPT Runtime</span></a>. The C++ runtime is the recommended runtime.</p>
<p>TensorRT-LLM also includes Python and C++ backends for NVIDIA Triton Inference Server to assemble solutions for LLM online serving. The C++ backend implements in-flight batching as explained in the <a class="reference internal" href="../advanced/executor.html#executor"><span class="std std-ref">Executor API</span></a> documentation and is the recommended backend.</p>
<section id="model-weights">
<h2>Model Weights<a class="headerlink" href="#model-weights" title="Link to this heading"></a></h2>
<p>TensorRT-LLM is a library for LLM inference, and so to use it, you need to supply a set of trained weights. You can either use your own model weights trained in a framework like <a class="reference external" href="https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/">NVIDIA NeMo</a> or pull a set of pretrained weights from repositories like the Hugging Face Hub.</p>
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