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5e2cf02f46
TensorRT-LLMs/blogs/Falcon180B-H200.html
Shi Xiaowei 5e2cf02f46
Update gh-pages (#4284) 
update docs for 0.20.0rc2

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
2025-05-14 11:12:52 +08:00

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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"><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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<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API</span></p>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Examples</span></p>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../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="../examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_kv_events.html">Get KV Cache Events</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_customize.html">Generate text with customization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</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_quantization.html">Generation with Quantization</a></li>
<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_multilora.html">Generate text with multiple LoRA adapters</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_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../examples/customization.html">LLM Common Customizations</a></li>
<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_async.html">Generate Text Asynchronously</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_customize.html">Generate text with customization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</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_quantization.html">Generation with Quantization</a></li>
<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_multilora.html">Generate text with multiple LoRA adapters</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_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
</ul>
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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>
<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_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>
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<li class="toctree-l2"><a class="reference internal" href="../examples/openai_completion_client.html">OpenAI Completion Client</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Model Definition API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.runtime.html">Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../_cpp_gen/runtime.html">Runtime</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Command-Line Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-build.html">trtllm-build</a></li>
<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-serve.html">trtllm-serve</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Architecture</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/overview.html">TensorRT-LLM Architecture</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/checkpoint.html">TensorRT-LLM Checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architecture/workflow.html">TensorRT-LLM Build Workflow</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architecture/add-model.html">Adding a Model</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>
<li class="toctree-l1"><a class="reference internal" href="../advanced/executor.html">Executor API</a></li>
<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/lora.html">Run gpt-2b + LoRA using Executor / 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>
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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 current active"><a class="current reference internal" href="#">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"><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>
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100</span></li>
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<section id="falcon-180b-on-a-single-h200-gpu-with-int4-awq-and-6-7x-faster-llama-70b-over-a100">
<h1>Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100<a class="headerlink" href="#falcon-180b-on-a-single-h200-gpu-with-int4-awq-and-6-7x-faster-llama-70b-over-a100" title="Link to this heading">#</a></h1>
<p>H200’s large capacity &amp; high memory bandwidth, paired with TensorRT-LLM’s
optimizations, maximizes inference performance.</p>
<section id="falcon-180b-on-a-single-h200-with-int4-awq">
<h2>Falcon-180B on a single H200 with INT4 AWQ<a class="headerlink" href="#falcon-180b-on-a-single-h200-with-int4-awq" title="Link to this heading">#</a></h2>
<p><a class="reference external" href="https://huggingface.co/tiiuae/falcon-180B">Falcon-180B</a>, one of the largest &amp;
most accurate open source models available, can run on a <em>single</em> H200 GPU.</p>
<p>The 141GB of memory on H200, paired with TensorRT-LLM running INT4 AWQ with
FP8, allows for the entire large language model to fit on a single GPU, where
previously eight A100s were required. H200 Falcon-180B provides up to <strong>800</strong>
tok/s and retains high accuracy.</p>
<p><strong>Model Performance:</strong>
H200’s large capacity &amp; high memory bandwidth, utilizing INT4 AWQ to reduce
memory footprint, allows for great performance on Falcon-180B on a single GPU.</p>
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/Falcon180B-H200_tps.png?raw=true" alt="Falcon-180B performance comparison" width="450" height="auto">
<p><sup>Preliminary measured Performance, subject to change. TP1 does not represent peak performance on H200. </sup>
<sup>
TensorRT-LLM v0.7a |
Falcon-180B |
1xH200 TP1 |
INT4 AWQ |
BS: (in order) 256, 128 </sup></p>
<p><strong>Model Accuracy:</strong>
Often quantization can have adverse impacts on the accuracy of the model,
however, TensorRT-LLM’s AWQ decreases memory footprint of the model by <strong>4x</strong>
while maintaining high accuracy.</p>
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/Falcon180B-H200_acc.png?raw=true" alt="Falcon-180B accuracy comparison" width="600" height="auto">
<p><sup>Preliminary measured accuracy, subject to change. </sup>
<sup>
TensorRT-LLM v0.7a |
Falcon-180B |
1xH200 TP1 |
INT4 AWQ
</sup></p>
<p><a class="reference external" href="https://arxiv.org/abs/2306.00978"><strong>INT4 Activation-aware Weight Quantization
(AWQ)</strong></a> (Lin et al., 2023) is a quantization
technique which compresses the weights of an LLM down to 4bits based on their
relative importance, and performs computation in FP16. This allows for AWQ to
retain higher accuracy than other 4bit methods and reduce memory usage, but
requires special kernels capable of handling the change in precision
performantly.</p>
<p>TensorRT-LLM has implemented custom kernels for AWQ, and taken the technique a
step further by performing FP8 computation on Hopper GPUs instead of the
standard FP16.</p>
<p>Similar examples running Falcon-180B with quantization in TensorRT-LLM are
available in <a class="reference internal" href="#/examples/models/contrib/falcon"><span class="xref myst">examples/models/contrib/falcon</span></a>.</p>
</section>
<section id="llama-70b-on-h200-up-to-6-7x-a100">
<h2>Llama-70B on H200 up to 6.7x A100<a class="headerlink" href="#llama-70b-on-h200-up-to-6-7x-a100" title="Link to this heading">#</a></h2>
<p>TensorRT-LLM has improved its Group Query Attention (GQA) kernels, in the
generation phase, providing up to 2.4x improvement on Llama-70B over
TensorRT-LLM v0.5, achieving over <strong>3,800</strong> tok/s/gpu at up to <strong>6.7x</strong> faster
than A100.</p>
<p><strong>H200 6.7x A100</strong></p>
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/Falcon180B-H200_H200vA100.png?raw=true" alt="Llama-70B H200 vs A100 comparison" width="600" height="auto">
<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 (out tok/s/GPU)</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>128</p></td>
<td class="text-left"><p>3,803</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></p></td>
<td class="text-left"><p></p></td>
<td class="text-left"><p>3,803</p></td>
</tr>
<tr class="row-even"><td class="text-left"><p></p></td>
<td class="text-left"><p>1</p></td>
<td class="text-left"><p></p></td>
<td class="text-left"><p>2048</p></td>
<td class="text-left"><p>2,941</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></p></td>
<td class="text-left"><p></p></td>
<td class="text-left"><p>3,163</p></td>
</tr>
<tr class="row-even"><td class="text-left"><p></p></td>
<td class="text-left"><p>1</p></td>
<td class="text-left"><p></p></td>
<td class="text-left"><p>4096</p></td>
<td class="text-left"><p>1,946</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></p></td>
<td class="text-left"><p></p></td>
<td class="text-left"><p>2,263</p></td>
</tr>
</tbody>
</table>
</div>
<p><sup>Preliminary measured performance, subject to change. </sup>
<sup>
TensorRT-LLM v0.7a |
Llama2-70B |
1xH200 = TP1, 8xH200 = max TP/PP/DP config |
FP8 |
BS: (in order) 960, 960, 192, 560, 96, 640 </sup></p>
<p><strong>TensorRT-LLM GQA now 2.4x faster on H200</strong></p>
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/Falcon180B-H200_DecvOct.png?raw=true" alt="Llama-70B H200 December vs Oct." width="400" height="auto">
<p><sup>Preliminary measured performance, subject to change.</sup>
<sup>
TensorRT-LLM v0.7a vs TensorRT-LLM v0.6a |
Llama2-70B |
1xH200 TP1 |
FP8 |
BS 192 </sup></p>
<p><a class="reference external" href="https://arxiv.org/abs/2305.13245v2"><strong>Grouped Query Attention (GQA)</strong></a>
(Ainslie et al., 2023), used in Llama-70B, is a variant of Multihead Attention
(MHA) which groups key-value (KV) heads together, resulting in fewer KV heads
than query (Q) heads. TensorRT-LLM has a custom implementation of MHA which
supports GQA, multi-query attention (MQA) and standard MHA. It leverages Tensor
Cores, including in the generation phase, and delivers great performance on
NVIDIA GPUs.</p>
<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 v0.7 &amp; v0.8 releases.</p>
<p>Similar examples running Llama-70B in TensorRT-LLM are published in
<a class="reference internal" href="#/examples/models/core/llama"><span class="xref myst">examples/models/core/llama</span></a>.</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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