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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 has-children"><a class="reference internal" href="installation/index.html">Installation</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="installation/containers.html">Pre-built release container images on NGC</a></li>
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<li class="toctree-l2"><a class="reference internal" href="installation/linux.html">Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="installation/build-from-source-linux.html">Building from Source Code on Linux</a></li>
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</ul>
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</details></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deployment Guide</span></p>
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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/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>
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<li class="toctree-l2"><a class="reference internal" href="examples/llm_inference.html">Generate text</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_async_streaming.html">Generate text in streaming</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_guided_decoding.html">Generate text with guided decoding</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_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_sparse_attention.html">Sparse Attention</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_speculative_decoding.html">Speculative Decoding</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_kv_cache_connector.html">KV Cache Connector</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_kv_cache_offloading.html">KV Cache Offloading</a></li>
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<li class="toctree-l2"><a class="reference internal" href="examples/llm_runtime.html">Runtime Configuration Examples</a></li>
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<li class="toctree-l2"><a class="reference internal" href="examples/llm_sampling.html">Sampling Techniques Showcase</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_mgmn_llm_distributed.html">Run LLM-API with pytorch backend on Slurm</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/llm_mgmn_trtllm_bench.html">Run trtllm-bench with pytorch backend on Slurm</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="examples/llm_mgmn_trtllm_serve.html">Run trtllm-serve with pytorch backend on Slurm</a></li>
|
||
</ul>
|
||
</details></li>
|
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<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-l2"><a class="reference internal" href="examples/curl_chat_client.html">Curl Chat Client</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/curl_chat_client_for_multimodal.html">Curl Chat Client For Multimodal</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/curl_completion_client.html">Curl Completion Client</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/deepseek_r1_reasoning_parser.html">Deepseek R1 Reasoning Parser</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/genai_perf_client.html">Genai Perf Client</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/genai_perf_client_for_multimodal.html">Genai Perf Client For Multimodal</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/openai_chat_client.html">OpenAI Chat Client</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/openai_chat_client_for_multimodal.html">OpenAI Chat Client for Multimodal</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/openai_completion_client.html">OpenAI Completion Client</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/openai_completion_client_for_lora.html">Openai Completion Client For Lora</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="examples/openai_completion_client_json_schema.html">OpenAI Completion Client with JSON Schema</a></li>
|
||
</ul>
|
||
</details></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="examples/dynamo_k8s_example.html">Dynamo K8s Example</a></li>
|
||
<li class="toctree-l1 has-children"><a class="reference internal" href="deployment-guide/index.html">Model Recipes</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="deployment-guide/deployment-guide-for-deepseek-r1-on-trtllm.html">Deployment Guide for DeepSeek R1 on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="deployment-guide/deployment-guide-for-llama3.3-70b-on-trtllm.html">Deployment Guide for Llama3.3 70B on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="deployment-guide/deployment-guide-for-llama4-scout-on-trtllm.html">Deployment Guide for Llama4 Scout 17B on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="deployment-guide/deployment-guide-for-gpt-oss-on-trtllm.html">Deployment Guide for GPT-OSS on TensorRT-LLM - Blackwell Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="deployment-guide/deployment-guide-for-qwen3-next-on-trtllm.html">Deployment Guide for Qwen3 Next on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
</ul>
|
||
</details></li>
|
||
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|
||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Models</span></p>
|
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<li class="toctree-l1"><a class="reference internal" href="models/supported-models.html">Supported Models</a></li>
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<li class="toctree-l1"><a class="reference internal" href="models/adding-new-model.html">Adding a New Model</a></li>
|
||
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">CLI Reference</span></p>
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|
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<li class="toctree-l1"><a class="reference internal" href="commands/trtllm-bench.html">trtllm-bench</a></li>
|
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<li class="toctree-l1 has-children"><a class="reference internal" href="commands/trtllm-serve/index.html">trtllm-serve</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="commands/trtllm-serve/trtllm-serve.html">trtllm-serve</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="commands/trtllm-serve/run-benchmark-with-trtllm-serve.html">Run benchmarking with <code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code></a></li>
|
||
</ul>
|
||
</details></li>
|
||
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">API Reference</span></p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="llm-api/index.html">LLM 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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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Features</span></p>
|
||
<ul class="nav bd-sidenav">
|
||
<li class="toctree-l1"><a class="reference internal" href="features/feature-combination-matrix.html">Feature Combination Matrix</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
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||
<li class="toctree-l1"><a class="reference internal" href="features/disagg-serving.html">Disaggregated Serving</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/kvcache.html">KV Cache System</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/long-sequence.html">Long Sequences</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/lora.html">LoRA (Low-Rank Adaptation)</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="features/multi-modality.html">Multimodal Support in TensorRT LLM</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="features/overlap-scheduler.html">Overlap Scheduler</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/paged-attention-ifb-scheduler.html">Paged Attention, IFB, and Request Scheduling</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/parallel-strategy.html">Parallelism in TensorRT LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/quantization.html">Quantization</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/sampling.html">Sampling</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/speculative-decoding.html">Speculative Decoding</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/checkpoint-loading.html">Checkpoint Loading</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/auto_deploy/auto-deploy.html">AutoDeploy (Prototype)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="features/torch_compile_and_piecewise_cuda_graph.html">Torch Compile & Piecewise CUDA Graph</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
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<li class="toctree-l1"><a class="reference internal" href="developer-guide/perf-benchmarking.html">TensorRT LLM Benchmarking</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="developer-guide/ci-overview.html">Continuous Integration Overview</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="developer-guide/dev-containers.html">Using Dev Containers</a></li>
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<li class="toctree-l1"><a class="reference internal" href="developer-guide/api-change.html">LLM API Change Guide</a></li>
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<li class="toctree-l1"><a class="reference internal" href="developer-guide/kv-transfer.html">Introduction to KV Cache Transmission</a></li>
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<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog10_ADP_Balance_Strategy.html">ADP Balance Strategy</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog11_GPT_OSS_Eagle3.html">Running GPT-OSS-120B with Eagle3 Speculative Decoding on GB200/B200 (TensorRT LLM)</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog12_Combining_Guided_Decoding_and_Speculative_Decoding.html">Combining Guided Decoding and Speculative Decoding: Making CPU and GPU Cooperate Seamlessly</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog13_Inference_Time_Compute_Implementation_in_TensorRT-LLM.html">Inference Time Compute Implementation in TensorRT LLM</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.html">Scaling Expert Parallelism in TensorRT LLM (Part 3: Pushing the Performance Boundary)</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.html">Pushing Latency Boundaries: Optimizing DeepSeek-R1 Performance on NVIDIA B200 GPUs</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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<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog3_Optimizing_DeepSeek_R1_Throughput_on_NVIDIA_Blackwell_GPUs.html">Optimizing DeepSeek R1 Throughput on NVIDIA Blackwell GPUs: A Deep Dive for Developers</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.html">Scaling Expert Parallelism in TensorRT LLM (Part 1: Design and Implementation of Large-scale EP)</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog5_Disaggregated_Serving_in_TensorRT-LLM.html">Disaggregated Serving in TensorRT LLM</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog6_Llama4_maverick_eagle_guide.html">How to launch Llama4 Maverick + Eagle3 TensorRT LLM server</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog7_NGram_performance_Analysis_And_Auto_Enablement.html">N-Gram Speculative Decoding in TensorRT LLM</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog8_Scaling_Expert_Parallelism_in_TensorRT-LLM_part2.html">Scaling Expert Parallelism in TensorRT LLM (Part 2: Performance Status and Optimization)</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="blogs/tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.html">Running a High Performance GPT-OSS-120B Inference Server with TensorRT LLM</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="blogs/Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.html">How to get best performance on DeepSeek-R1 in TensorRT LLM</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/XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
|
||
<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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<section class="tex2jax_ignore mathjax_ignore" id="overview">
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<span id="product-overview"></span><h1>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h1>
|
||
<section id="about-tensorrt-llm">
|
||
<h2>About TensorRT LLM<a class="headerlink" href="#about-tensorrt-llm" title="Link to this heading">#</a></h2>
|
||
<p><a class="reference external" href="https://developer.nvidia.com/tensorrt">TensorRT LLM</a> is NVIDIA’s comprehensive open-source library for accelerating and optimizing inference performance of the latest large language models (LLMs) on NVIDIA GPUs.</p>
|
||
</section>
|
||
<section id="key-capabilities">
|
||
<h2>Key Capabilities<a class="headerlink" href="#key-capabilities" title="Link to this heading">#</a></h2>
|
||
<section id="architected-on-pytorch">
|
||
<h3>🔥 <strong>Architected on Pytorch</strong><a class="headerlink" href="#architected-on-pytorch" title="Link to this heading">#</a></h3>
|
||
<p>TensorRT LLM provides a high-level Python <a class="reference internal" href="quick-start-guide.html#run-offline-inference-with-llm-api"><span class="std std-ref">LLM API</span></a> that supports a wide range of inference setups - from single-GPU to multi-GPU or multi-node deployments. It includes built-in support for various parallelism strategies and advanced features. The LLM API integrates seamlessly with the broader inference ecosystem, including NVIDIA <a class="reference external" href="https://github.com/ai-dynamo/dynamo">Dynamo</a> and the <a class="reference external" href="https://github.com/triton-inference-server/server">Triton Inference Server</a>.</p>
|
||
<p>TensorRT LLM is designed to be modular and easy to modify. Its PyTorch-native architecture allows developers to experiment with the runtime or extend functionality. Several popular models are also pre-defined and can be customized using <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/3111682/tensorrt_llm/_torch/models/modeling_deepseekv3.py">native PyTorch code</a>, making it easy to adapt the system to specific needs.</p>
|
||
</section>
|
||
<section id="state-of-the-art-performance">
|
||
<h3>⚡ <strong>State-of-the-Art Performance</strong><a class="headerlink" href="#state-of-the-art-performance" title="Link to this heading">#</a></h3>
|
||
<p>TensorRT LLM delivers breakthrough performance on the latest NVIDIA GPUs:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>DeepSeek R1</strong>: <a class="reference external" href="https://developer.nvidia.com/blog/nvidia-blackwell-delivers-world-record-deepseek-r1-inference-performance/">World-record inference performance on Blackwell GPUs</a></p></li>
|
||
<li><p><strong>Llama 4 Maverick</strong>: <a class="reference external" href="https://developer.nvidia.com/blog/blackwell-breaks-the-1000-tps-user-barrier-with-metas-llama-4-maverick/">Breaks the 1,000 TPS/User Barrier on B200 GPUs</a></p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="comprehensive-model-support">
|
||
<h3>🎯 <strong>Comprehensive Model Support</strong><a class="headerlink" href="#comprehensive-model-support" title="Link to this heading">#</a></h3>
|
||
<p>TensorRT LLM supports the latest and most popular LLM architectures:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>Language Models</strong>: GPT-OSS, Deepseek-R1/V3, Llama 3/4, Qwen2/3, Gemma 3, Phi 4…</p></li>
|
||
<li><p><strong>Multi-modal Models</strong>: LLaVA-NeXT, Qwen2-VL, VILA, Llama 3.2 Vision…</p></li>
|
||
</ul>
|
||
<p>TensorRT LLM strives to support the most popular models on <strong>Day 0</strong>.</p>
|
||
</section>
|
||
<section id="fp4-support">
|
||
<h3>FP4 Support<a class="headerlink" href="#fp4-support" title="Link to this heading">#</a></h3>
|
||
<p><a class="reference external" href="https://www.nvidia.com/en-us/data-center/dgx-b200/">NVIDIA B200 GPUs</a> , when used with TensorRT LLM, enable seamless loading of model weights in the new <a class="reference external" href="https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/#what_is_nvfp4">FP4 format</a>, allowing you to automatically leverage optimized FP4 kernels for efficient and accurate low-precision inference.</p>
|
||
</section>
|
||
<section id="fp8-support">
|
||
<h3>FP8 Support<a class="headerlink" href="#fp8-support" title="Link to this heading">#</a></h3>
|
||
<p>TensorRT LLM strives to support the most popular models on <strong>Day 0</strong>.</p>
|
||
</section>
|
||
<section id="advanced-optimization-production-features">
|
||
<h3>🚀 <strong>Advanced Optimization & Production Features</strong><a class="headerlink" href="#advanced-optimization-production-features" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>In-Flight Batching & Paged Attention</strong>: <a class="reference internal" href="legacy/advanced/gpt-attention.html#inflight-batching"><span class="std std-ref">In-flight Batching</span></a> eliminates wait times by dynamically managing request execution, processing context and generation phases together for maximum GPU utilization and reduced latency.</p></li>
|
||
<li><p><strong>Multi-GPU Multi-Node Inference</strong>: Seamless distributed inference with tensor, pipeline, and expert parallelism across multiple GPUs and nodes through the Model Definition API.</p></li>
|
||
<li><p><strong>Advanced Quantization</strong>:</p>
|
||
<ul>
|
||
<li><p><strong>FP4 Quantization</strong>: Native support on NVIDIA B200 GPUs with optimized FP4 kernels</p></li>
|
||
<li><p><strong>FP8 Quantization</strong>: Automatic conversion on NVIDIA H100 GPUs leveraging Hopper architecture</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p><strong>Speculative Decoding</strong>: Multiple algorithms including EAGLE, MTP and NGram</p></li>
|
||
<li><p><strong>KV Cache Management</strong>: Paged KV cache with intelligent block reuse and memory optimization</p></li>
|
||
<li><p><strong>Chunked Prefill</strong>: Efficient handling of long sequences by splitting context into manageable chunks</p></li>
|
||
<li><p><strong>LoRA Support</strong>: Multi-adapter support with HuggingFace and NeMo formats, efficient fine-tuning and adaptation</p></li>
|
||
<li><p><strong>Checkpoint Loading</strong>: Flexible model loading from various formats (HuggingFace, NeMo, custom)</p></li>
|
||
<li><p><strong>Guided Decoding</strong>: Advanced sampling with stop words, bad words, and custom constraints</p></li>
|
||
<li><p><strong>Disaggregated Serving (Beta)</strong>: Separate context and generation phases across different GPUs for optimal resource utilization</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="latest-gpu-architecture-support">
|
||
<h3>🔧 <strong>Latest GPU Architecture Support</strong><a class="headerlink" href="#latest-gpu-architecture-support" title="Link to this heading">#</a></h3>
|
||
<p>TensorRT LLM supports the full spectrum of NVIDIA GPU architectures:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>NVIDIA Blackwell</strong>: B200, GB200, RTX Pro 6000 SE with FP4 optimization</p></li>
|
||
<li><p><strong>NVIDIA Hopper</strong>: H100, H200,GH200 with FP8 acceleration</p></li>
|
||
<li><p><strong>NVIDIA Ada Lovelace</strong>: L40/L40S, RTX 40 series with FP8 acceleration</p></li>
|
||
<li><p><strong>NVIDIA Ampere</strong>: A100, RTX 30 series for production workloads</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<section id="what-can-you-do-with-tensorrt-llm">
|
||
<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>Whether you’re building the next generation of AI applications, optimizing existing LLM deployments, or exploring the frontiers of large language model technology, TensorRT LLM provides the tools, performance, and flexibility you need to succeed in the era of generative AI.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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</section>
|
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</section>
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