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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"><a class="reference internal" href="../key-features.html">Key Features</a></li>
<li class="toctree-l1"><a class="reference internal" href="../torch.html">PyTorch Backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../release-notes.html">Release Notes</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../installation/grace-hopper.html">Installing on Grace Hopper</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../llm-api/reference.html">API Reference</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API Examples</span></p>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../llm-api-examples/index.html">LLM Examples Introduction</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="../llm-api-examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference.html">Generate text</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_quantization.html">Generation with Quantization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_logits_processor.html">Control generated text using logits post processor</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_customize.html">Generate text with customization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../llm-api-examples/customization.html">Common Customizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../llm-api-examples/llm_api_examples.html">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="../llm-api-examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference.html">Generate text</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_quantization.html">Generation with Quantization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_logits_processor.html">Control generated text using logits post processor</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_customize.html">Generate text with customization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/checkpoint.html">TensorRT-LLM Checkpoint</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-runtime.html">C++ GPT Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/graph-rewriting.html">Graph Rewriting Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/inference-request.html">Inference Request</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/lora.html">Run gpt-2b + LoRA using GptManager / cpp runtime</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/kv-cache-reuse.html">KV cache reuse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/speculative-decoding.html">Speculative Sampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/disaggregated-service.html">Disaggregated-Service (experimental)</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Performance</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../performance/perf-benchmarking.html">Benchmarking</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../performance/performance-tuning-guide/index.html">Performance Tuning Guide</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="../performance/performance-tuning-guide/benchmarking-default-performance.html">Benchmarking Default Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-build-time-flags.html">Useful Build-Time Flags</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/tuning-max-batch-size-and-max-num-tokens.html">Tuning Max Batch Size and Max Num Tokens</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/deciding-model-sharding-strategy.html">Deciding Model Sharding Strategy</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/fp8-quantization.html">FP8 Quantization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-runtime-flags.html">Useful Runtime Options</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../reference/precision.html">Numerical Precision</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/memory.html">Memory Usage of TensorRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</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="Falcon180B-H200.html">Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100</a></li>
<li class="toctree-l1"><a class="reference internal" href="quantization-in-TRT-LLM.html">Speed up inference with SOTA quantization techniques in TRT-LLM</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</span></li>
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<section id="new-xqa-kernel-provides-2-4x-more-llama-70b-throughput-within-the-same-latency-budget">
<h1>New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget<a class="headerlink" href="#new-xqa-kernel-provides-2-4x-more-llama-70b-throughput-within-the-same-latency-budget" title="Link to this heading">#</a></h1>
<p>XQA kernel provides optimization for <a class="reference external" href="https://arxiv.org/abs/1911.02150">MQA</a> and <a class="reference external" href="https://arxiv.org/abs/2305.13245v3">GQA</a> during generation phase. It also provides optimization for beam search. Using tensor cores for acceleration, reducing data loading and conversion, it delivers increased throughput within the same latency budget. Increased throughput allows serving greater number of user requests while providing the same experience.</p>
<p>Support matrix and usage flags are described in <a class="reference internal" href="#/docs/source/advanced/gpt-attention.md#xqa-optimization"><span class="xref myst">docs/source/advanced/gpt_attention</span></a>.</p>
<p><strong>Increased Throughput:</strong>
Looking at the Throughput-Latency curves below, we see that the enabling of XQA optimization increases throughput. Higher throughput equates to serving more users, and we can see that TPOT on the Y-axis flattens out when XQA gets enabled.</p>
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/XQA_ThroughputvsLatency.png?raw=true" alt="XQA increased throughput within same latency budget" width="950" height="auto">
<p><sub>Preliminary measured Performance, subject to change. TPOT lower is better. FP8, 8xH100 GPUs, Single Engine, ISL/OSL: 512/2048, BS: 1 - 256, TensorRT-LLM v0.8a</sub></p>
<section id="llama-70b-on-h200-up-to-2-4x-increased-throughput-with-xqa-within-same-latency-budget">
<h2>Llama-70B on H200 up to 2.4x increased throughput with XQA within same latency budget<a class="headerlink" href="#llama-70b-on-h200-up-to-2-4x-increased-throughput-with-xqa-within-same-latency-budget" title="Link to this heading">#</a></h2>
<p><strong>H200 2.4x with XQA</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>GPUs</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-left"><p>Throughput w/o XQA (tok/s/GPU)</p></th>
<th class="head text-left"><p>Throughput w/ XQA (tok/s/GPU)</p></th>
<th class="head text-left"><p>Speedup</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td class="text-left"><p>Llama-70B</p></td>
<td class="text-left"><p>1</p></td>
<td class="text-left"><p>128</p></td>
<td class="text-left"><p>2048</p></td>
<td class="text-left"><p>1,227</p></td>
<td class="text-left"><p>2,941</p></td>
<td class="text-left"><p>2.4x</p></td>
</tr>
<tr class="row-odd"><td class="text-left"><p></p></td>
<td class="text-left"><p>8</p></td>
<td class="text-left"><p>128</p></td>
<td class="text-left"><p>2048</p></td>
<td class="text-left"><p>13,232</p></td>
<td class="text-left"><p>25,300</p></td>
<td class="text-left"><p>1.9x</p></td>
</tr>
</tbody>
</table>
</div>
<section id="closing">
<h3>Closing<a class="headerlink" href="#closing" title="Link to this heading">#</a></h3>
<p>These improvements will be published in the <code class="docutils literal notranslate"><span class="pre">main</span></code> branch soon, and will be
included in the v0.8 releases.</p>
<p>For more information about H200, please see the <a class="reference internal" href="H200launch.html"><span class="std std-doc">H200 announcement blog</span></a>.</p>
<p>Throughput is calculated as output tokens per second per gpu.
<code class="docutils literal notranslate"><span class="pre">out_tps=output_seqlen*batch_size/total_latency/tp</span></code></p>
<p><sub> <strong>Glossary:</strong>
| DP = Data Parallel
ISL = Input Sequence Length
| PP = Pipeline Parallel
| OSL = Output Sequence Length
| OOM = Out of Memory
| TP = Tensor Parallel <sub/></p>
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