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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 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>
|
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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_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>
|
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<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>
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</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/aiperf_client.html">Aiperf Client</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/aiperf_client_for_multimodal.html">Aiperf Client For Multimodal</a></li>
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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>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/curl_completion_client.html">Curl Completion Client</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/curl_responses_client.html">Curl Responses Client</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/deepseek_r1_reasoning_parser.html">Deepseek R1 Reasoning Parser</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/openai_chat_client.html">OpenAI Chat Client</a></li>
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<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>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_responses_client.html">OpenAI Responses Client</a></li>
|
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</ul>
|
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</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">
|
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<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>
|
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<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>
|
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</ul>
|
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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>
|
||
<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>
|
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<li class="toctree-l1"><a class="reference internal" href="../features/additional-outputs.html">Additional Outputs</a></li>
|
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<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>
|
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<li class="toctree-l1"><a class="reference internal" href="../features/auto_deploy/auto-deploy.html">AutoDeploy (Beta)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../features/ray-orchestrator.html">Ray Orchestrator (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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<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>
|
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</ul>
|
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
|
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<ul class="current nav bd-sidenav">
|
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">Architecture Overview</a></li>
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<li class="toctree-l1"><a class="reference internal" href="perf-analysis.html">Performance Analysis</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="perf-benchmarking.html">TensorRT LLM Benchmarking</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="ci-overview.html">Continuous Integration Overview</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="dev-containers.html">Using Dev Containers</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="api-change.html">LLM API Change Guide</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="kv-transfer.html">Introduction to KV Cache Transmission</a></li>
|
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</ul>
|
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Blogs</span></p>
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<ul class="nav bd-sidenav">
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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>
|
||
<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>
|
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</ul>
|
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Quick Links</span></p>
|
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/releases">Releases</a></li>
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<li class="toctree-l1"><a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM">Github Code</a></li>
|
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<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>
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</ul>
|
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Use TensorRT Engine</span></p>
|
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="../legacy/tensorrt_quickstart.html">LLM API with TensorRT Engine</a></li>
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<article class="bd-article">
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<section class="tex2jax_ignore mathjax_ignore" id="architecture-overview">
|
||
<h1>Architecture Overview<a class="headerlink" href="#architecture-overview" title="Link to this heading">#</a></h1>
|
||
<p>The <code class="docutils literal notranslate"><span class="pre">LLM</span></code> class is a core entry point for the TensorRT LLM, providing a simplified <code class="docutils literal notranslate"><span class="pre">generate()</span></code> API for efficient large language model inference. This abstraction aims to streamline the user experience, as demonstrated with TinyLlama:</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="c1"># Initialize the LLM with a specified model</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="c1"># Generate text using the model</span>
|
||
<span class="n">output</span> <span class="o">=</span> <span class="n">llm</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="s2">"Hello, my name is"</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>The <code class="docutils literal notranslate"><span class="pre">LLM</span></code> class automatically manages essential pre and post-processing steps, including tokenization (encoding input prompts into numerical representations) and detokenization (decoding model outputs back into human-readable text).</p>
|
||
<p>Internally, the <code class="docutils literal notranslate"><span class="pre">LLM</span></code> class orchestrates the creation of a dedicated <code class="docutils literal notranslate"><span class="pre">PyExecutor(Worker)</span></code> process on each rank.</p>
|
||
<p><img alt="TensorRT LLM Architecture Overview" src="../_images/TRTLLM_Architecture_Overview.png" /></p>
|
||
<p>This <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code> operates in a continuous background loop, designed for the efficient, asynchronous processing of inference requests.</p>
|
||
<p>The <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code>’s functionality is built upon several key components:</p>
|
||
<ul class="simple">
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">Scheduler</span></code>: Responsible for determining which active requests are ready for execution at each processing step.</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code>: Manages the allocation, deallocation, and maintenance of the Key-Value (KV) Cache. This is a critical optimization for Transformer models, significantly enhancing performance during autoregressive text generation by storing previously computed attention keys and values.</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">ModelEngine</span></code>: Handles the loading and highly efficient execution of the language model on the GPU hardware.</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">Sampler</span></code>: Takes the raw outputs (logits) from the ModelEngine and applies appropriate sampling strategies (e.g., greedy, top-k, top-p, beam search) to generate the final output tokens.</p></li>
|
||
</ul>
|
||
<p>During each iteration of its background loop, the <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code> performs the following sequence of operations:</p>
|
||
<ul class="simple">
|
||
<li><p>Request Fetching: Retrieves new inference requests from an internal request queue, if available.</p></li>
|
||
<li><p>Scheduling: Interacts with the <code class="docutils literal notranslate"><span class="pre">Scheduler</span></code> to identify and prioritize requests that are ready to be processed in the current step.</p></li>
|
||
<li><p>Resource Preparation: Coordinates with the <code class="docutils literal notranslate"><span class="pre">KVCacheManager</span></code> to ensure that the necessary Key-Value (KV) Cache resources are allocated for the selected requests.</p></li>
|
||
<li><p>Model Execution: Invokes the <code class="docutils literal notranslate"><span class="pre">ModelEngine</span></code> to perform a forward pass on the scheduled requests, predicting the next output tokens.</p></li>
|
||
<li><p>Output Handling: Updates the partial outputs for ongoing requests and finalizes the results for any requests that have reached completion, returning them to the user.</p></li>
|
||
</ul>
|
||
<section id="runtime-optimizations">
|
||
<h2>Runtime Optimizations<a class="headerlink" href="#runtime-optimizations" title="Link to this heading">#</a></h2>
|
||
<p>TensorRT LLM enhances inference throughput and reduces latency by integrating a suite of runtime optimizations, including CUDA Graph, <a class="reference internal" href="../features/overlap-scheduler.html"><span class="std std-doc">Overlap Scheduler</span></a>, <a class="reference internal" href="../features/speculative-decoding.html"><span class="std std-doc">Speculative decoding</span></a>, etc.</p>
|
||
<section id="cuda-graph">
|
||
<h3>CUDA Graph<a class="headerlink" href="#cuda-graph" title="Link to this heading">#</a></h3>
|
||
<p>CUDA Graphs drastically reduce the CPU-side overhead associated with launching GPU kernels, which is particularly impactful in PyTorch-based inference where Python’s host-side code can be a bottleneck. By capturing a sequence of CUDA operations as a single graph, the entire sequence can be launched with one API call, minimizing CPU-GPU synchronization and driver overhead.</p>
|
||
<p>To maximize the “hit rate” of these cached graphs, TensorRT LLM employs CUDA Graph padding. If an incoming batch’s size doesn’t match a captured graph, it’s padded to the nearest larger, supported size for which a graph exists. While this incurs minor overhead from computing “wasted” tokens, it’s often a better trade-off than falling back to slower eager mode execution. This optimization has a significant impact, demonstrating up to a 22% end-to-end throughput increase on certain models and hardware.</p>
|
||
</section>
|
||
<section id="overlap-scheduler">
|
||
<h3>Overlap Scheduler<a class="headerlink" href="#overlap-scheduler" title="Link to this heading">#</a></h3>
|
||
<p>The Overlap Scheduler maximizes GPU utilization by hiding CPU-bound latency behind GPU computation.</p>
|
||
<p>The key strategy is to launch the GPU’s work for the next step (n+1) immediately, without waiting for the CPU to finish processing the results of the current step (n). This allows the CPU to handle tasks like checking stop criteria or updating responses for one batch while the GPU is already executing the model for the subsequent batch.</p>
|
||
<p>This concurrent execution pipeline is illustrated in the <code class="docutils literal notranslate"><span class="pre">PyExecutor</span></code>’s logic:</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Schedule and launch GPU work for the current step (n)</span>
|
||
<span class="n">scheduled_batch</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_schedule</span><span class="p">()</span>
|
||
<span class="n">batch_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_step</span><span class="p">(</span><span class="n">scheduled_batch</span><span class="p">,</span> <span class="n">previous_tensors_device</span><span class="p">)</span>
|
||
<span class="n">sample_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_async</span><span class="p">(</span><span class="n">scheduled_batch</span><span class="p">,</span> <span class="n">batch_outputs</span><span class="p">)</span>
|
||
|
||
<span class="c1"># While the GPU is busy, process the CPU-bound results from the previous step (n-1)</span>
|
||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">previous_batch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">_process_previous_batch</span><span class="p">()</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>This approach effectively reduces GPU idle time and improves overall hardware occupancy. While it introduces one extra decoding step into the pipeline, the resulting throughput gain is a significant trade-off. For this reason, the Overlap Scheduler is enabled by default in TensorRT LLM.</p>
|
||
</section>
|
||
</section>
|
||
</section>
|
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|
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<p>This page is generated by TensorRT-LLM commit <a href="https://github.com/NVIDIA/TensorRT-LLM/tree/a65b0d4">a65b0d4</a>.</p>
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