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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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<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/curl_chat_client.html">Curl Chat Client</a></li>
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<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>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/genai_perf_client.html">Genai Perf Client</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/genai_perf_client_for_multimodal.html">Genai Perf Client For Multimodal</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">
|
||
<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="current nav bd-sidenav">
|
||
<li class="toctree-l1"><a class="reference internal" href="tech_blog/blog10_ADP_Balance_Strategy.html">ADP Balance Strategy</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="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="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="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="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="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="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="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="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="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="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="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="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="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="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="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="XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
|
||
<li class="toctree-l1 current active"><a class="current reference internal" href="#">H100 has 4.6x A100 Performance in TensorRT LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
|
||
</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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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">H100 has 4.6x A100 Performance in TensorRT LLM, achieving 10,000 tok/s at 100ms to first token</span></li>
|
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</ul>
|
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</nav>
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<div id="searchbox"></div>
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<article class="bd-article">
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<blockquote>
|
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<div><p>:bangbang: :new: <em>NVIDIA H200 has been announced & is optimized on TensorRT LLM. Learn more about H200, & H100 comparison, here:</em> <a class="reference internal" href="H200launch.html"><span class="std std-doc"><strong>H200</strong> achieves nearly <strong>12,000 tokens/sec on Llama2-13B</strong> with TensorRT LLM</span></a></p>
|
||
</div></blockquote>
|
||
<section class="tex2jax_ignore mathjax_ignore" id="h100-has-4-6x-a100-performance-in-tensorrt-llm-achieving-10-000-tok-s-at-100ms-to-first-token">
|
||
<h1>H100 has 4.6x A100 Performance in TensorRT LLM, achieving 10,000 tok/s at 100ms to first token<a class="headerlink" href="#h100-has-4-6x-a100-performance-in-tensorrt-llm-achieving-10-000-tok-s-at-100ms-to-first-token" title="Link to this heading">#</a></h1>
|
||
<p>TensorRT LLM evaluated on both Hopper and Ampere shows <strong>H100 FP8 is up to 4.6x max throughput and 4.4x faster 1st token latency than A100</strong>. H100 FP8 is able to achieve over 10,000 output tok/s at peak throughput for 64 concurrent requests, while maintaining a 1st token latency of 100ms. For min-latency applications, TRT-LLM H100 can achieve less than 10ms to 1st token latency.</p>
|
||
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/TRT_LLM_v0-5-0_H100vA100_tps.png?raw=true" alt="max throughput" width="500" height="auto">
|
||
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/TRT_LLM_v0-5-0_H100vA100_1st.png?raw=true" alt="1st token latency" width="500" height="auto">
|
||
<p><sub>TensorRT LLM throughput & first token latency on H100 & A100. H100 FP8, A100 FP16, SXM 80GB GPUs, ISL/OSL’s provided, TP=1, BS=32/64 max throughput, BS=1 1st token latency. TensorRT LLM v0.5.0, TensorRT 9.1. </sub>
|
||
<sub>Max throughput calculated by sweeping BS 1,2,…,64. Throughput taken at largest successful.</sub></p>
|
||
<p><strong>Max Throughput & Min Latency</strong></p>
|
||
<div class="pst-scrollable-table-container"><table class="table">
|
||
<thead>
|
||
<tr class="row-odd"><th class="head text-left"><p>Model</p></th>
|
||
<th class="head text-left"><p>Batch Size</p></th>
|
||
<th class="head text-left"><p>Input Length</p></th>
|
||
<th class="head text-left"><p>Output Length</p></th>
|
||
<th class="head text-right"><p>Throughput (out tok/s)</p></th>
|
||
<th class="head text-right"><p>1st Token Latency (ms)</p></th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="row-even"><td class="text-left"><p><strong>H100</strong></p></td>
|
||
<td class="text-left"><p></p></td>
|
||
<td class="text-left"><p></p></td>
|
||
<td class="text-left"><p></p></td>
|
||
<td class="text-right"><p></p></td>
|
||
<td class="text-right"><p></p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td class="text-left"><p>GPT-J 6B</p></td>
|
||
<td class="text-left"><p>64</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-right"><p><strong>10,907</strong></p></td>
|
||
<td class="text-right"><p>102</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td class="text-left"><p>GPT-J 6B</p></td>
|
||
<td class="text-left"><p>1</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>-</p></td>
|
||
<td class="text-right"><p>185</p></td>
|
||
<td class="text-right"><p><strong>7.1</strong></p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td class="text-left"><p><strong>A100</strong></p></td>
|
||
<td class="text-left"><p></p></td>
|
||
<td class="text-left"><p></p></td>
|
||
<td class="text-left"><p></p></td>
|
||
<td class="text-right"><p></p></td>
|
||
<td class="text-right"><p></p></td>
|
||
</tr>
|
||
<tr class="row-even"><td class="text-left"><p>GPT-J 6B</p></td>
|
||
<td class="text-left"><p>64</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-right"><p>3,679</p></td>
|
||
<td class="text-right"><p>481</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td class="text-left"><p>GPT-J 6B</p></td>
|
||
<td class="text-left"><p>1</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>-</p></td>
|
||
<td class="text-right"><p>111</p></td>
|
||
<td class="text-right"><p>12.5</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td class="text-left"><p><strong>Speedup</strong></p></td>
|
||
<td class="text-left"><p></p></td>
|
||
<td class="text-left"><p></p></td>
|
||
<td class="text-left"><p></p></td>
|
||
<td class="text-right"><p></p></td>
|
||
<td class="text-right"><p></p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td class="text-left"><p>GPT-J 6B</p></td>
|
||
<td class="text-left"><p>64</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-right"><p><strong>3.0x</strong></p></td>
|
||
<td class="text-right"><p><strong>4.7x</strong></p></td>
|
||
</tr>
|
||
<tr class="row-even"><td class="text-left"><p>GPT-J 6B</p></td>
|
||
<td class="text-left"><p>1</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>-</p></td>
|
||
<td class="text-right"><p><strong>2.4x</strong></p></td>
|
||
<td class="text-right"><p>1.7x</p></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
<p><sub>FP8 H100, FP16 A100, SXM 80GB GPUs, TP1, ISL/OSL’s provided, TensorRT LLM v0.5.0., TensorRT 9.1</sub></p>
|
||
<p>The full data behind these charts & tables and including larger models with higher TP values can be found in TensorRT LLM’s <a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/0.21.0/performance/perf-overview.html">Performance Documentation</a></p>
|
||
<p>Stay tuned for a highlight on Llama coming soon!</p>
|
||
<section id="mlperf-on-h100-with-fp8">
|
||
<h2>MLPerf on H100 with FP8<a class="headerlink" href="#mlperf-on-h100-with-fp8" title="Link to this heading">#</a></h2>
|
||
<p>In the most recent MLPerf results, NVIDIA demonstrated up to 4.5x speedup in model inference performance on the NVIDIA H100 compared to previous results on the NVIDIA A100 Tensor Core GPU. Using the same data types, the H100 showed a 2x increase over the A100. Switching to FP8 resulted in yet another 2x increase in speed.</p>
|
||
</section>
|
||
<section id="what-is-h100-fp8">
|
||
<h2>What is H100 FP8?<a class="headerlink" href="#what-is-h100-fp8" title="Link to this heading">#</a></h2>
|
||
<p>H100 is NVIDIA’s next-generation, highest-performing data center GPU. Based on the NVIDIA Hopper GPU architecture, H100 accelerates AI training and inference, HPC, and data analytics applications in cloud data centers, servers, systems at the edge, and workstations. Providing native support for FP8 data types H100 can double performance and halve memory consumption, compared to 16-bit floating point options on H100.</p>
|
||
<p>FP8 specification introduced in the paper <a class="reference external" href="https://arxiv.org/abs/2209.05433">FP8 Formats for Deep Learning</a> can be used to speed up training as well as inference with post-training-quantization of models trained using 16-bit formats. The specification consists of two encodings - E4M3 (4-bit exponent and 3-bit mantissa) and E5M2 (5-bit exponent and 2-bit mantissa). The recommended use of FP8 encodings is E4M3 for weight and activation tensors, and E5M2 for gradient tensors.</p>
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