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<div class="bd-toc-item navbar-nav"><p aria-level="2" class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">Overview</a></li>
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<li class="toctree-l1"><a class="reference internal" href="quick-start-guide.html">Quick Start Guide</a></li>
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<li class="toctree-l1"><a class="reference internal" href="key-features.html">Key Features</a></li>
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<li class="toctree-l1"><a class="reference internal" href="torch.html">PyTorch Backend</a></li>
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<li class="toctree-l1"><a class="reference internal" href="release-notes.html">Release Notes</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Installation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="installation/linux.html">Installing on Linux</a></li>
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<li class="toctree-l1"><a class="reference internal" href="installation/build-from-source-linux.html">Building from Source Code on Linux</a></li>
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<li class="toctree-l1"><a class="reference internal" href="installation/grace-hopper.html">Installing on Grace Hopper</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="llm-api/index.html">API Introduction</a></li>
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<li class="toctree-l1"><a class="reference internal" href="llm-api/reference.html">API Reference</a></li>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1 has-children"><a class="reference internal" href="examples/index.html">LLM Examples Introduction</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2"><a class="reference internal" href="examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="examples/llm_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
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<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_eagle2_decoding.html">Generate Text Using Eagle2 Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_kv_events.html">Get KV Cache Events</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_quantization.html">Generation with Quantization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference.html">Generate text</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_customize.html">Generate text with customization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
|
||
</ul>
|
||
</details></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="examples/customization.html">LLM Common Customizations</a></li>
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||
<li class="toctree-l1 has-children"><a class="reference internal" href="examples/llm_api_examples.html">LLM Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_eagle2_decoding.html">Generate Text Using Eagle2 Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_kv_events.html">Get KV Cache Events</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_quantization.html">Generation with Quantization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference.html">Generate text</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference_customize.html">Generate text with customization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
|
||
</ul>
|
||
</details></li>
|
||
<li class="toctree-l1 has-children"><a class="reference internal" href="examples/trtllm_serve_examples.html">Online Serving Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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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>
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<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>
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<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>
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<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>
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<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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<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog2_DeepSeek_R1_MTP_Implementation_and_Optimization.html">DeepSeek R1 MTP Implementation and Optimization</a></li>
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<span id="product-overview"></span><h1>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h1>
|
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<section id="about-tensorrt-llm">
|
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<h2>About TensorRT-LLM<a class="headerlink" href="#about-tensorrt-llm" title="Link to this heading">#</a></h2>
|
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<p><a class="reference external" href="https://developer.nvidia.com/tensorrt">TensorRT-LLM</a> accelerates and optimizes inference performance for the latest large language models (LLMs) on NVIDIA GPUs. This open-source library is available for free on the <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM">TensorRT-LLM GitHub repo</a> and as part of the <a class="reference external" href="https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/">NVIDIA NeMo framework</a>.</p>
|
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<p>LLMs have revolutionized the field of artificial intelligence and created entirely new ways of interacting with the digital world. But, as organizations and application developers around the world look to incorporate LLMs into their work, some of the challenges with running these models become apparent. Put simply, LLMs are large. That fact can make them expensive and slow to run without the right techniques.</p>
|
||
<p>TensorRT-LLM offers a comprehensive library for compiling and optimizing LLMs for inference. TensorRT-LLM incorporates all of the optimizations (that is, kernel fusion and quantization, runtime optimizations like C++ implementations, KV caching, continuous in-flight batching, and paged attention) and more, while providing an intuitive Model Definition API for defining and building new models.</p>
|
||
<p>Some of the major benefits that TensorRT-LLM provides are:</p>
|
||
<section id="common-llm-support">
|
||
<h3>Common LLM Support<a class="headerlink" href="#common-llm-support" title="Link to this heading">#</a></h3>
|
||
<p>TensorRT-LLM supports the latest LLMs. Refer to the <a class="reference internal" href="reference/support-matrix.html#support-matrix-software"><span class="std std-ref">Software</span></a> for the full list.</p>
|
||
</section>
|
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<section id="in-flight-batching-and-paged-attention">
|
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<h3>In-Flight Batching and Paged Attention<a class="headerlink" href="#in-flight-batching-and-paged-attention" title="Link to this heading">#</a></h3>
|
||
<p><a class="reference internal" href="advanced/gpt-attention.html#inflight-batching"><span class="std std-ref">In-flight Batching</span></a> takes advantage of the overall text generation process for an LLM can be broken down into multiple iterations of execution on the model. Rather than waiting for the whole batch to finish before moving on to the next set of requests, the TensorRT-LLM runtime immediately evicts finished sequences from the batch. It then begins executing new requests while other requests are still in flight. It’s a <a class="reference internal" href="advanced/executor.html#executor"><span class="std std-ref">Executor API</span></a> that aims at reducing wait times in queues, eliminating the need for padding requests, and allowing for higher GPU utilization.</p>
|
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</section>
|
||
<section id="multi-gpu-multi-node-inference">
|
||
<h3>Multi-GPU Multi-Node Inference<a class="headerlink" href="#multi-gpu-multi-node-inference" title="Link to this heading">#</a></h3>
|
||
<p>TensorRT-LLM consists of pre– and post-processing steps and multi-GPU multi-node communication primitives in a simple, open-source Model Definition API for groundbreaking LLM inference performance on GPUs. Refer to the <a class="reference internal" href="architecture/core-concepts.html#multi-gpu-multi-node"><span class="std std-ref">Multi-GPU and Multi-Node Support</span></a> section for more information.</p>
|
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</section>
|
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<section id="fp8-support">
|
||
<h3>FP8 Support<a class="headerlink" href="#fp8-support" title="Link to this heading">#</a></h3>
|
||
<p><a class="reference external" href="https://www.nvidia.com/en-us/data-center/dgx-h100/">NVIDIA H100 GPUs</a> with TensorRT-LLM give you the ability to convert model weights into a new FP8 format easily and compile models to take advantage of optimized FP8 kernels automatically. This is made possible through <a class="reference external" href="https://blogs.nvidia.com/blog/h100-transformer-engine/">NVIDIA Hopper</a> and done without having to change any model code.</p>
|
||
</section>
|
||
<section id="latest-gpu-support">
|
||
<h3>Latest GPU Support<a class="headerlink" href="#latest-gpu-support" title="Link to this heading">#</a></h3>
|
||
<p>TensorRT-LLM supports GPUs based on the NVIDIA Hopper, NVIDIA Ada Lovelace, and NVIDIA Ampere architectures.
|
||
Certain limitations might apply. Refer to the <a class="reference internal" href="reference/support-matrix.html#support-matrix"><span class="std std-ref">Support Matrix</span></a> for more information.</p>
|
||
</section>
|
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<section id="native-windows-support">
|
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<h3>Native Windows Support<a class="headerlink" href="#native-windows-support" title="Link to this heading">#</a></h3>
|
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<p>Windows platform support is deprecated as of v0.18.0. All Windows-related code and functionality will be completely removed in future releases.</p>
|
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</section>
|
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</section>
|
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<section id="what-can-you-do-with-tensorrt-llm">
|
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<h2>What Can You Do With TensorRT-LLM?<a class="headerlink" href="#what-can-you-do-with-tensorrt-llm" title="Link to this heading">#</a></h2>
|
||
<p>Let TensorRT-LLM accelerate inference performance on the latest LLMs on NVIDIA GPUs. Use TensorRT-LLM as an optimization backbone for LLM inference in NVIDIA NeMo, an end-to-end framework to build, customize, and deploy generative AI applications into production. NeMo provides complete containers, including TensorRT-LLM and NVIDIA Triton, for generative AI deployments.</p>
|
||
<p>TensorRT-LLM improves ease of use and extensibility through an open-source modular Model Definition API for defining, optimizing, and executing new architectures and enhancements as LLMs evolve, and can be customized easily.</p>
|
||
<p>If you’re eager to dive into the world of LLMs, now is the time to get started with TensorRT-LLM. Explore its capabilities, experiment with different models and optimizations, and embark on your journey to unlock the incredible power of AI-driven language models. To get started, refer to the <a class="reference internal" href="quick-start-guide.html#quick-start-guide"><span class="std std-ref">Quick Start Guide</span></a>.</p>
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