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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>
<li class="toctree-l1"><a class="reference internal" href="../quick-start-guide.html">Quick Start Guide</a></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../installation/containers.html">Pre-built release container images on NGC</a></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../installation/build-from-source-linux.html">Building from Source Code on Linux</a></li>
</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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<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/llm_api_examples.html">LLM Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference.html">Generate text</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async.html">Generate text asynchronously</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async_streaming.html">Generate text in streaming</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_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>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_speculative_decoding.html">Speculative Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_kv_cache_connector.html">KV Cache Connector</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_runtime.html">Runtime Configuration Examples</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_sampling.html">Sampling Techniques Showcase</a></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Run trtllm-serve with pytorch backend on Slurm</a></li>
</ul>
</details></li>
<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>
<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/quick-start-recipe-for-deepseek-r1-on-trtllm.html">Quick Start Recipe for DeepSeek R1 on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/quick-start-recipe-for-llama3.3-70b-on-trtllm.html">Quick Start Recipe for Llama3.3 70B on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/quick-start-recipe-for-llama4-scout-on-trtllm.html">Quick Start Recipe for Llama4 Scout 17B on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/quick-start-recipe-for-gpt-oss-on-trtllm.html">Quick Start Recipe for GPT-OSS on TensorRT-LLM - Blackwell Hardware</a></li>
</ul>
</details></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Models</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../models/supported-models.html">Supported Models</a></li>
<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>
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<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/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 (Beta)</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/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>
</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="../architecture/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>
</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/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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<section id="disaggregated-service-prototype">
<span id="disaggregated-service"></span><h1>Disaggregated-Service (Prototype)<a class="headerlink" href="#disaggregated-service-prototype" title="Link to this heading">#</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Note:
This feature is currently in prototype, and the related API is subjected to change in future versions.</p>
</div>
<p>Currently TRT-LLM supports <code class="docutils literal notranslate"><span class="pre">disaggregated-service</span></code>, where the context and generation phases of a request can run on different executors. TRT-LLM’s disaggregated service relies on the executor API, please make sure to read the <a class="reference internal" href="executor.html"><span class="std std-doc">executor page</span></a> before reading the document.</p>
<p>For more information on disaggregated service in LLM inference, one can refer to papers such as <a class="reference external" href="https://arxiv.org/abs/2401.09670">DistServe</a>, <a class="reference external" href="https://arxiv.org/abs/2311.18677">SplitWise</a>.</p>
<p>An <a class="reference internal" href="../blogs/tech_blog/blog5_Disaggregated_Serving_in_TensorRT-LLM.html"><span class="std std-doc">architectural and performance overview</span></a>, as well as <span class="xref myst">usage examples</span>, are provided.</p>
<section id="environment-variables">
<h2>Environment Variables<a class="headerlink" href="#environment-variables" title="Link to this heading">#</a></h2>
<p>TRT-LLM uses some environment variables to control the behavior of disaggregated service.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">TRTLLM_PARALLEL_CACHE_SEND</span></code>: If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, contextExecutor will attempt to send KV cache for multiple requests in parallel. The default value is <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TRTLLM_DISABLE_KV_CACHE_TRANSFER_OVERLAP</span></code>: If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, generationExecutor will not overlap KV cache transfer with model inference. The default value is <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TRTLLM_ENABLE_KVCACHE_RECEIVE_PARALLEL</span></code>: When the generation rank receives KV cache from multiple context ranks within a single context instance, it will receive KV cache from each rank sequentially. If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, the generation rank will receive KV cache from each rank within one context instance in parallel. The default value is <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TRTLLM_REQUEST_KV_CACHE_CONCURRENT</span></code>: If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, generationExecutor prepares independent resources for each context executor to receive KV cache, requests whose KV cache are received from different context executors will be processed concurrently. If set to <code class="docutils literal notranslate"><span class="pre">0</span></code>, the generation executor will reuse the same resource to process KV cache transfer for each request sequentially, reducing the resources used by KV cache transmission and thereby lowering the risk of running out of memory. The default value is <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TRTLLM_TRY_ZCOPY_FOR_KVCACHE_TRANSFER</span></code>: TRT-LLM typically copies non-contiguous data into a temporary buffer before sending KV cache. If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, TRT-LLM will attempt to directly transmit each KV cache block, eliminating extra copies. The default value is <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE</span></code>: By default, TRT-LLM uses a <code class="docutils literal notranslate"><span class="pre">stream-ordered</span> <span class="pre">memory</span> <span class="pre">allocator</span></code> to allocate temporary buffers. If this environment variable is set to #Size, TRT-LLM will use <code class="docutils literal notranslate"><span class="pre">cudaMalloc</span></code> to allocate buffer of size #Size for KV cache transmission. The default value is <code class="docutils literal notranslate"><span class="pre">512MB</span></code>. Users can set <code class="docutils literal notranslate"><span class="pre">TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE=1GB</span></code> to allocate a 1 GB buffer with <code class="docutils literal notranslate"><span class="pre">cudaMalloc</span></code> for KV cache transmission.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TRTLLM_KVCACHE_TRANSFER_USE_ASYNC_BUFFER</span></code>: If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, TRT-LLM will use <code class="docutils literal notranslate"><span class="pre">cudaMallocAsync</span></code> to allocate buffers for KV cache transmission. The default value is <code class="docutils literal notranslate"><span class="pre">0</span></code>. This environment variable only takes effect when <code class="docutils literal notranslate"><span class="pre">TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE</span></code> is greater than 0.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TRTLLM_KVCACHE_SEND_MAX_CONCURRENCY_NUM</span></code>: The maximum number of concurrent KV cache sends. The default value is <code class="docutils literal notranslate"><span class="pre">4</span></code>. This environment variable only takes effect when <code class="docutils literal notranslate"><span class="pre">TRTLLM_KVCACHE_TRANSFER_BUFFER_SIZE</span></code> is greater than 0.</p></li>
</ul>
<p>There are some other useful environment variables that may help when encountering failures or performance issues.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">NCCL_GRAPH_MIXING_SUPPORT</span></code>: With the default value <code class="docutils literal notranslate"><span class="pre">1</span></code>, the CUDA driver may create too many CUDA streams while working with one CUDA graph, leading to performance drop. Setting it to <code class="docutils literal notranslate"><span class="pre">0</span></code> will reduce the number of CUDA streams, but please make sure there are no other NCCL ops outside the one CUDA graph, otherwise it’s unsafe.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">UCX_MAX_RNDV_RAILS</span></code>: With the default value <code class="docutils literal notranslate"><span class="pre">2</span></code>, UCX attempts to use two InfiniBand (IB) NIC devices per GPU for Rendezvous (RNDV) transfers. When both the context and generation instances enable tensor- and expert-parallel (TEP), multiple TP ranks may transfer KV cache concurrently. Because each TP rank can use up to two NIC devices, some NIC devices can be shared across GPUs, causing contention and reduced throughput. Setting <code class="docutils literal notranslate"><span class="pre">UCX_MAX_RNDV_RAILS=1</span></code> can reduce contention in this case.</p></li>
</ul>
</section>
<section id="troubleshooting-and-faq">
<h2>Troubleshooting and FAQ<a class="headerlink" href="#troubleshooting-and-faq" title="Link to this heading">#</a></h2>
<section id="general-faqs">
<h3>General FAQs<a class="headerlink" href="#general-faqs" title="Link to this heading">#</a></h3>
<p><em>Q. What are the limitations of disaggregated-service in TRT-LLM?</em></p>
<p>A. Currently, only <code class="docutils literal notranslate"><span class="pre">decoder-only</span> <span class="pre">engine</span></code> and <code class="docutils literal notranslate"><span class="pre">beamWidth=1</span></code> are supported, and the KV cache at each layer of the model is required to be homogeneous, with the same data type and the same number of attention headers.</p>
<p><em>Q. Is the engine used by disaggregated-service different from other engines?</em></p>
<p>A. No. There are no special requirements for the arguments to build engine.</p>
<p><em>Q. Do the engines used by the context executor and generation executor need to be the same?</em></p>
<p>A. No. The engines used by context executor and generation executor can be different, and their parallelism can be heterogeneous, i.e., TP,PP can be different, and TRT-LLM will handle the heterogeneity of KV cache.</p>
<p><em>Q. Does TRT-LLM support running multiple context executor instances and generation executor instances?</em></p>
<p>A. Yes. TRT-LLM supports running multiple context executors and generation executors at the same time, and each executor can use different engine, but it is the user’s responsibility to route requests to different executors and manage <code class="docutils literal notranslate"><span class="pre">requestId</span></code>.</p>
<p><em>Q. Can an executor handle both context-only requests and generation-only requests?</em></p>
<p>A. Yes, but it’s not recommended, TRT-LLM does not implement proper scheduling for the case where the executor handles mixed context-only requests and generation-only requests, it’s better to run context-only requests and generation-only requests on different executors.</p>
<p><em>Q. Does disaggregated-service in TRT-LLM support multi-gpu and multi-node?</em></p>
<p>A. Yes, it’s recommended that different executor use different GPUs . We support context-only executor and genertion-only executor run on same node or different nodes. The <code class="docutils literal notranslate"><span class="pre">participantIds</span></code> and <code class="docutils literal notranslate"><span class="pre">deviceIds</span></code> used by each executor need to be explicitly set by the user, and the <code class="docutils literal notranslate"><span class="pre">participantIds</span></code> of each executor must not be intersecting.</p>
</section>
<section id="debugging-faqs">
<h3>Debugging FAQs<a class="headerlink" href="#debugging-faqs" title="Link to this heading">#</a></h3>
<p><em>Q. Does TRT-LLM support using GPU direct RDMA for inter-node KV Cache transfer?</em></p>
<p>A. Yes, TRT-LLM supports using GPU direct RDMA for inter-node KV cache transfer.</p>
<p><em>Q. What causes the substantial bandwidth fluctuations in kvCache transfers, especially during the first few requests following service initialization?</em></p>
<p>A. The communication for kvCache transfer between executors are established dynamically. The connection establishment process incurs significant overhead, which explains the apparently lower kvCache transfer bandwidth observed during the initial requests after service startup. This lower bandwidth reflects the inclusion of connection establishment overhead. When conducting benchmarks, it is recommended to perform a warm-up phase to ensure accurate performance measurements.</p>
<p><em>Q. When my servers are running on different NVLink domains, some servers hang or have a lower performance. How to fix that?</em></p>
<p>A. NVLink domain can be found with <code class="docutils literal notranslate"><span class="pre">nvidia-smi</span> <span class="pre">-q</span></code> in the <code class="docutils literal notranslate"><span class="pre">Fabric.ClusterUUID</span></code> field. A few UCX environment variables can be adjusted when your servers have different NVLink domains:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">UCX_CUDA_IPC_ENABLE_MNNVL</span></code>: Set to <code class="docutils literal notranslate"><span class="pre">n</span></code>. This also can reduce UCX timeout error messages like <code class="docutils literal notranslate"><span class="pre">UCX</span>&#160; <span class="pre">ERROR</span>&#160;&#160; <span class="pre">cuMemImportFromShareableHandle</span> <span class="pre">failed:</span> <span class="pre">invalid</span> <span class="pre">resource</span> <span class="pre">handle</span></code>, although these errors don’t necessarily cause your trtllm-serve to fail.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">UCX_NET_DEVICES</span></code>: Check if this is set correctly, or unset this variable to allow UCX to use all possible devices.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">UCX_RNDV_SCHEME</span></code>: Set to <code class="docutils literal notranslate"><span class="pre">get_zcopy</span></code> or <code class="docutils literal notranslate"><span class="pre">put_zcopy</span></code> on GB200 for better performance. The default value is <code class="docutils literal notranslate"><span class="pre">auto</span></code>.</p></li>
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