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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"><a class="reference internal" href="../overview.html">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 current active has-children"><a class="reference internal" href="index.html">Installation</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
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<li class="toctree-l2"><a class="reference internal" href="containers.html">Pre-built release container images on NGC</a></li>
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<li class="toctree-l2 current active"><a class="current reference internal" href="#">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="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>
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<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>
|
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</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-on-trtllm.html">Deployment Guide for Qwen3 on TensorRT LLM - Blackwell & Hopper 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>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-kimi-k2-thinking-on-trtllm.html">Deployment Guide for Kimi K2 Thinking on TensorRT LLM - Blackwell</a></li>
|
||
</ul>
|
||
</details></li>
|
||
</ul>
|
||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Models</span></p>
|
||
<ul class="nav bd-sidenav">
|
||
<li class="toctree-l1"><a class="reference internal" href="../models/supported-models.html">Supported Models</a></li>
|
||
|
||
<li class="toctree-l1"><a class="reference internal" href="../models/adding-new-model.html">Adding a New Model</a></li>
|
||
</ul>
|
||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">CLI Reference</span></p>
|
||
<ul class="nav bd-sidenav">
|
||
<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-bench.html">trtllm-bench</a></li>
|
||
|
||
<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-eval.html">trtllm-eval</a></li>
|
||
<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>
|
||
<li class="toctree-l2"><a class="reference internal" href="../commands/trtllm-serve/trtllm-serve.html">trtllm-serve</a></li>
|
||
<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>
|
||
</ul>
|
||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">API Reference</span></p>
|
||
<ul class="nav bd-sidenav">
|
||
<li class="toctree-l1"><a class="reference internal" href="../llm-api/index.html">LLM API Introduction</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../llm-api/reference.html">API Reference</a></li>
|
||
</ul>
|
||
<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>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/disagg-serving.html">Disaggregated Serving</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/kvcache.html">KV Cache System</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/long-sequence.html">Long Sequences</a></li>
|
||
<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>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/overlap-scheduler.html">Overlap Scheduler</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/paged-attention-ifb-scheduler.html">Paged Attention, IFB, and Request Scheduling</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/parallel-strategy.html">Parallelism in TensorRT LLM</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/quantization.html">Quantization</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/sampling.html">Sampling</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/additional-outputs.html">Additional Outputs</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/guided-decoding.html">Guided Decoding</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/speculative-decoding.html">Speculative Decoding</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/checkpoint-loading.html">Checkpoint Loading</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/auto_deploy/auto-deploy.html">AutoDeploy (Prototype)</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/ray-orchestrator.html">Ray Orchestrator (Prototype)</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/torch_compile_and_piecewise_cuda_graph.html">Torch Compile & Piecewise CUDA Graph</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/helix.html">Helix Parallelism</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../features/kv-cache-connector.html">KV Cache Connector</a></li>
|
||
</ul>
|
||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
|
||
<ul class="nav bd-sidenav">
|
||
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/overview.html">Architecture Overview</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/perf-analysis.html">Performance Analysis</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/perf-benchmarking.html">TensorRT LLM Benchmarking</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/ci-overview.html">Continuous Integration Overview</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/dev-containers.html">Using Dev Containers</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/api-change.html">LLM API Change Guide</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/kv-transfer.html">Introduction to KV Cache Transmission</a></li>
|
||
</ul>
|
||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Blogs</span></p>
|
||
<ul class="nav bd-sidenav">
|
||
<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>
|
||
<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>
|
||
<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>
|
||
<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>
|
||
<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>
|
||
<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>
|
||
</ul>
|
||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Quick Links</span></p>
|
||
<ul class="nav bd-sidenav">
|
||
<li class="toctree-l1"><a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/releases">Releases</a></li>
|
||
<li class="toctree-l1"><a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM">Github Code</a></li>
|
||
<li class="toctree-l1"><a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/issues?q=is%3Aissue%20state%3Aopen%20label%3Aroadmap">Roadmap</a></li>
|
||
</ul>
|
||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Use TensorRT Engine</span></p>
|
||
<ul class="nav bd-sidenav">
|
||
<li class="toctree-l1"><a class="reference internal" href="../legacy/tensorrt_quickstart.html">LLM API with TensorRT Engine</a></li>
|
||
</ul>
|
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</div>
|
||
</nav></div>
|
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</div>
|
||
|
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<div class="sidebar-primary-items__end sidebar-primary__section">
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</div>
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<main id="main-content" class="bd-main" role="main">
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<div class="bd-content">
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<div class="bd-article-container">
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<div class="bd-header-article d-print-none">
|
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<div class="header-article-items header-article__inner">
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<div class="header-article-items__start">
|
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<div class="header-article-item">
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<nav aria-label="Breadcrumb" class="d-print-none">
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<ul class="bd-breadcrumbs">
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<a href="../index.html" class="nav-link" aria-label="Home">
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</a>
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<li class="breadcrumb-item"><a href="index.html" class="nav-link">Installation</a></li>
|
||
|
||
<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code></span></li>
|
||
</ul>
|
||
</nav>
|
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</div>
|
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|
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</div>
|
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</div>
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<div id="searchbox"></div>
|
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<article class="bd-article">
|
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|
||
<section class="tex2jax_ignore mathjax_ignore" id="installing-on-linux-via-pip">
|
||
<span id="linux"></span><h1>Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code><a class="headerlink" href="#installing-on-linux-via-pip" title="Link to this heading">#</a></h1>
|
||
<ol class="arabic">
|
||
<li><p>Install TensorRT LLM (tested on Ubuntu 24.04).</p>
|
||
<p class="rubric" id="install-prerequisites">Install prerequisites</p>
|
||
<p>Before the pre-built Python wheel can be installed via <code class="docutils literal notranslate"><span class="pre">pip</span></code>, a few
|
||
prerequisites must be put into place:</p>
|
||
<p>Install CUDA Toolkit 13.0 following the <a class="reference external" href="https://docs.nvidia.com/cuda/cuda-installation-guide-linux/">CUDA Installation Guide for Linux</a>
|
||
and make sure <code class="docutils literal notranslate"><span class="pre">CUDA_HOME</span></code> environment variable is properly set.</p>
|
||
<p>The <code class="docutils literal notranslate"><span class="pre">cuda-compat-13-0</span></code> package may be required depending on your system’s NVIDIA GPU
|
||
driver version. For additional information, refer to the <a class="reference external" href="https://docs.nvidia.com/deploy/cuda-compatibility/forward-compatibility.html">CUDA Forward Compatibility</a>.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># By default, PyTorch CUDA 12.8 package is installed. Install PyTorch CUDA 13.0 package to align with the CUDA version used for building TensorRT LLM wheels.</span>
|
||
pip3<span class="w"> </span>install<span class="w"> </span><span class="nv">torch</span><span class="o">==</span><span class="m">2</span>.9.0<span class="w"> </span>torchvision<span class="w"> </span>--index-url<span class="w"> </span>https://download.pytorch.org/whl/cu130
|
||
|
||
sudo<span class="w"> </span>apt-get<span class="w"> </span>-y<span class="w"> </span>install<span class="w"> </span>libopenmpi-dev
|
||
|
||
<span class="c1"># Optional step: Only required for disagg-serving</span>
|
||
sudo<span class="w"> </span>apt-get<span class="w"> </span>-y<span class="w"> </span>install<span class="w"> </span>libzmq3-dev
|
||
</pre></div>
|
||
</div>
|
||
<div class="admonition tip">
|
||
<p class="admonition-title">Tip</p>
|
||
<p>Instead of manually installing the preqrequisites as described
|
||
above, it is also possible to use the pre-built <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/devel">TensorRT LLM Develop container
|
||
image hosted on NGC</a>
|
||
(see <a class="reference internal" href="containers.html#containers"><span class="std std-ref">here</span></a> for information on container tags).</p>
|
||
</div>
|
||
<p class="rubric" id="install-pre-built-tensorrt-llm-wheel">Install pre-built TensorRT LLM wheel</p>
|
||
<p>Once all prerequisites are in place, TensorRT LLM can be installed as follows:</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip3<span class="w"> </span>install<span class="w"> </span>--upgrade<span class="w"> </span>pip<span class="w"> </span>setuptools<span class="w"> </span><span class="o">&&</span><span class="w"> </span>pip3<span class="w"> </span>install<span class="w"> </span>tensorrt_llm
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.</strong></p>
|
||
</li>
|
||
<li><p>Sanity check the installation by running the following in Python (tested on Python 3.12):</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm</span><span class="w"> </span><span class="kn">import</span> <span class="n">LLM</span><span class="p">,</span> <span class="n">SamplingParams</span>
|
||
<span class="linenos"> 2</span>
|
||
<span class="linenos"> 3</span>
|
||
<span class="linenos"> 4</span><span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
|
||
<span class="linenos"> 5</span>
|
||
<span class="linenos"> 6</span> <span class="c1"># Model could accept HF model name, a path to local HF model,</span>
|
||
<span class="linenos"> 7</span> <span class="c1"># or TensorRT Model Optimizer's quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.</span>
|
||
<span class="linenos"> 8</span> <span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"TinyLlama/TinyLlama-1.1B-Chat-v1.0"</span><span class="p">)</span>
|
||
<span class="linenos"> 9</span>
|
||
<span class="linenos">10</span> <span class="c1"># Sample prompts.</span>
|
||
<span class="linenos">11</span> <span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
|
||
<span class="linenos">12</span> <span class="s2">"Hello, my name is"</span><span class="p">,</span>
|
||
<span class="linenos">13</span> <span class="s2">"The capital of France is"</span><span class="p">,</span>
|
||
<span class="linenos">14</span> <span class="s2">"The future of AI is"</span><span class="p">,</span>
|
||
<span class="linenos">15</span> <span class="p">]</span>
|
||
<span class="linenos">16</span>
|
||
<span class="linenos">17</span> <span class="c1"># Create a sampling params.</span>
|
||
<span class="linenos">18</span> <span class="n">sampling_params</span> <span class="o">=</span> <span class="n">SamplingParams</span><span class="p">(</span><span class="n">temperature</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">top_p</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
|
||
<span class="linenos">19</span>
|
||
<span class="linenos">20</span> <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">llm</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">prompts</span><span class="p">,</span> <span class="n">sampling_params</span><span class="p">):</span>
|
||
<span class="linenos">21</span> <span class="nb">print</span><span class="p">(</span>
|
||
<span class="linenos">22</span> <span class="sa">f</span><span class="s2">"Prompt: </span><span class="si">{</span><span class="n">output</span><span class="o">.</span><span class="n">prompt</span><span class="si">!r}</span><span class="s2">, Generated text: </span><span class="si">{</span><span class="n">output</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="si">!r}</span><span class="s2">"</span>
|
||
<span class="linenos">23</span> <span class="p">)</span>
|
||
<span class="linenos">24</span>
|
||
<span class="linenos">25</span> <span class="c1"># Got output like</span>
|
||
<span class="linenos">26</span> <span class="c1"># Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming'</span>
|
||
<span class="linenos">27</span> <span class="c1"># Prompt: 'The president of the United States is', Generated text: 'likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the'</span>
|
||
<span class="linenos">28</span> <span class="c1"># Prompt: 'The capital of France is', Generated text: 'Paris.'</span>
|
||
<span class="linenos">29</span> <span class="c1"># Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are'</span>
|
||
<span class="linenos">30</span>
|
||
<span class="linenos">31</span>
|
||
<span class="linenos">32</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||
<span class="linenos">33</span> <span class="n">main</span><span class="p">()</span>
|
||
</pre></div>
|
||
</div>
|
||
</li>
|
||
</ol>
|
||
<p><strong>Known limitations</strong></p>
|
||
<p>There are some known limitations when you pip install pre-built TensorRT LLM wheel package.</p>
|
||
<ol class="arabic">
|
||
<li><p>MPI in the Slurm environment</p>
|
||
<p>If you encounter an error while running TensorRT LLM in a Slurm-managed cluster, you need to reconfigure the MPI installation to work with Slurm.
|
||
The setup methods depends on your slurm configuration, pls check with your admin. This is not a TensorRT LLM specific, rather a general mpi+slurm issue.</p>
|
||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">The</span> <span class="n">application</span> <span class="n">appears</span> <span class="n">to</span> <span class="n">have</span> <span class="n">been</span> <span class="n">direct</span> <span class="n">launched</span> <span class="n">using</span> <span class="s2">"srun"</span><span class="p">,</span>
|
||
<span class="n">but</span> <span class="n">OMPI</span> <span class="n">was</span> <span class="ow">not</span> <span class="n">built</span> <span class="k">with</span> <span class="n">SLURM</span> <span class="n">support</span><span class="o">.</span> <span class="n">This</span> <span class="n">usually</span> <span class="n">happens</span>
|
||
<span class="n">when</span> <span class="n">OMPI</span> <span class="n">was</span> <span class="ow">not</span> <span class="n">configured</span> <span class="o">--</span><span class="k">with</span><span class="o">-</span><span class="n">slurm</span> <span class="ow">and</span> <span class="n">we</span> <span class="n">weren</span><span class="s1">'t able</span>
|
||
<span class="n">to</span> <span class="n">discover</span> <span class="n">a</span> <span class="n">SLURM</span> <span class="n">installation</span> <span class="ow">in</span> <span class="n">the</span> <span class="n">usual</span> <span class="n">places</span><span class="o">.</span>
|
||
</pre></div>
|
||
</div>
|
||
</li>
|
||
<li><p>Prevent <code class="docutils literal notranslate"><span class="pre">pip</span></code> from replacing existing PyTorch installation</p>
|
||
<p>On certain systems, particularly Ubuntu 22.04, users installing TensorRT LLM would find that their existing, CUDA 13.0 compatible PyTorch installation (e.g., <code class="docutils literal notranslate"><span class="pre">torch==2.9.0+cu130</span></code>) was being uninstalled by <code class="docutils literal notranslate"><span class="pre">pip</span></code>. It was then replaced by a CUDA 12.8 version (<code class="docutils literal notranslate"><span class="pre">torch==2.9.0</span></code>), causing the TensorRT LLM installation to be unusable and leading to runtime errors.</p>
|
||
<p>The solution is to create a <code class="docutils literal notranslate"><span class="pre">pip</span></code> constraints file, locking <code class="docutils literal notranslate"><span class="pre">torch</span></code> to the currently installed version. Here is an example of how this can be done manually:</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CURRENT_TORCH_VERSION</span><span class="o">=</span><span class="k">$(</span>python3<span class="w"> </span>-c<span class="w"> </span><span class="s2">"import torch; print(torch.__version__)"</span><span class="k">)</span>
|
||
<span class="nb">echo</span><span class="w"> </span><span class="s2">"torch==</span><span class="nv">$CURRENT_TORCH_VERSION</span><span class="s2">"</span><span class="w"> </span>><span class="w"> </span>/tmp/torch-constraint.txt
|
||
pip3<span class="w"> </span>install<span class="w"> </span>--upgrade<span class="w"> </span>pip<span class="w"> </span>setuptools<span class="w"> </span><span class="o">&&</span><span class="w"> </span>pip3<span class="w"> </span>install<span class="w"> </span>tensorrt_llm<span class="w"> </span>-c<span class="w"> </span>/tmp/torch-constraint.txt
|
||
</pre></div>
|
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</div>
|
||
</li>
|
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|
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<p>Last updated on December 07, 2025.</p>
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<p>This page is generated by TensorRT-LLM commit <a href="https://github.com/NVIDIA/TensorRT-LLM/tree/e4c7078">e4c7078</a>.</p>
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