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<h1 id="gpu-support">GPU support</h1>
<h2 id="achieving-optimal-gpu-performance-with-docling">Achieving Optimal GPU Performance with Docling</h2>
<p>This guide describes how to maximize GPU performance for Docling pipelines. It covers device selection, pipeline differences, and provides example snippets for configuring batch size and concurrency in the VLM pipeline for both Linux and Windows.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Improvements and optimizations strategies for maximizing the GPU performance is an
active topic. Regularly check these guidelines for updates.</p>
</div>
<h3 id="standard-pipeline">Standard Pipeline</h3>
<p>Enable GPU acceleration by configuring the accelerator device and concurrency options using Docling's API:</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.accelerator_options</span><span class="w"> </span><span class="kn">import</span> <span class="n">AcceleratorDevice</span><span class="p">,</span> <span class="n">AcceleratorOptions</span>
<span class="c1"># Configure accelerator options for GPU</span>
<span class="n">accelerator_options</span> <span class="o">=</span> <span class="n">AcceleratorOptions</span><span class="p">(</span>
<span class="n">device</span><span class="o">=</span><span class="n">AcceleratorDevice</span><span class="o">.</span><span class="n">CUDA</span><span class="p">,</span> <span class="c1"># or AcceleratorDevice.AUTO</span>
<span class="p">)</span>
</code></pre></div>
<p>Batch size and concurrency for document processing are controlled for each stage of the pipeline as:</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.pipeline_options</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">ThreadedPdfPipelineOptions</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">pipeline_options</span> <span class="o">=</span> <span class="n">ThreadedPdfPipelineOptions</span><span class="p">(</span>
<span class="n">ocr_batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="c1"># default 4</span>
<span class="n">layout_batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="c1"># default 4</span>
<span class="n">table_batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="c1"># currently not using GPU batching</span>
<span class="p">)</span>
</code></pre></div>
<p>Setting a higher <code>page_batch_size</code> will run the Docling models (in particular the layout detection stage) with a GPU batch inference mode.</p>
<h4 id="complete-example">Complete example</h4>
<p>For a complete example see <a href="../../examples/gpu_standard_pipeline/">gpu_standard_pipeline.py</a>.</p>
<h4 id="ocr-engines">OCR engines</h4>
<p>The current Docling OCR engines rely on third-party libraries, hence GPU support depends on the availability in the respective engines.</p>
<p>The only setup which is known to work at the moment is RapidOCR with the torch backend, which can be enabled via</p>
<div class="highlight"><pre><span></span><code><span class="n">pipeline_options</span> <span class="o">=</span> <span class="n">PdfPipelineOptions</span><span class="p">()</span>
<span class="n">pipeline_options</span><span class="o">.</span><span class="n">ocr_options</span> <span class="o">=</span> <span class="n">RapidOcrOptions</span><span class="p">(</span>
<span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch&quot;</span><span class="p">,</span>
<span class="p">)</span>
</code></pre></div>
<p>More details in the GitHub discussion <a href="https://github.com/docling-project/docling/discussions/2451">#2451</a>.</p>
<h3 id="vlm-pipeline">VLM Pipeline</h3>
<p>For best GPU utilization, use a local inference server. Docling supports inference servers which exposes the OpenAI-compatible chat completion endpoints. For example:</p>
<ul>
<li>vllm: <code>http://localhost:8000/v1/chat/completions</code> (available only on Linux)</li>
<li>LM Studio: <code>http://localhost:1234/v1/chat/completions</code> (available both on Linux and Windows)</li>
<li>Ollama: <code>http://localhost:11434/v1/chat/completions</code> (available both on Linux and Windows)</li>
</ul>
<h4 id="start-the-inference-server">Start the inference server</h4>
<p>Here is an example on how to start the <a href="https://docs.vllm.ai/">vllm</a> inference server with optimum parameters for Granite Docling.</p>
<div class="highlight"><pre><span></span><code>vllm<span class="w"> </span>serve<span class="w"> </span>ibm-granite/granite-docling-258M<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--host<span class="w"> </span><span class="m">127</span>.0.0.1<span class="w"> </span>--port<span class="w"> </span><span class="m">8000</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--max-num-seqs<span class="w"> </span><span class="m">512</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--max-num-batched-tokens<span class="w"> </span><span class="m">8192</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--enable-chunked-prefill<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--gpu-memory-utilization<span class="w"> </span><span class="m">0</span>.9
</code></pre></div>
<h4 id="configure-docling">Configure Docling</h4>
<p>Configure the VLM pipeline using Docling's VLM options:</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.pipeline_options</span><span class="w"> </span><span class="kn">import</span> <span class="n">VlmPipelineOptions</span>
<span class="n">vlm_options</span> <span class="o">=</span> <span class="n">VlmPipelineOptions</span><span class="p">(</span>
<span class="n">enable_remote_services</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">vlm_options</span><span class="o">=</span><span class="p">{</span>
<span class="s2">&quot;url&quot;</span><span class="p">:</span> <span class="s2">&quot;http://localhost:8000/v1/chat/completions&quot;</span><span class="p">,</span> <span class="c1"># or any other compatible endpoint</span>
<span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="p">{</span>
<span class="s2">&quot;model&quot;</span><span class="p">:</span> <span class="s2">&quot;ibm-granite/granite-docling-258M&quot;</span><span class="p">,</span>
<span class="s2">&quot;max_tokens&quot;</span><span class="p">:</span> <span class="mi">4096</span><span class="p">,</span>
<span class="p">},</span>
<span class="s2">&quot;concurrency&quot;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="c1"># default is 1</span>
<span class="s2">&quot;prompt&quot;</span><span class="p">:</span> <span class="s2">&quot;Convert this page to docling.&quot;</span><span class="p">,</span>
<span class="s2">&quot;timeout&quot;</span><span class="p">:</span> <span class="mi">90</span><span class="p">,</span>
<span class="p">}</span>
<span class="p">)</span>
</code></pre></div>
<p>Additionally to the concurrency, we also have to set the <code>page_batch_size</code> Docling parameter. Make sure to set <code>settings.perf.page_batch_size &gt;= vlm_options.concurrency</code>.</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.settings</span><span class="w"> </span><span class="kn">import</span> <span class="n">settings</span>
<span class="n">settings</span><span class="o">.</span><span class="n">perf</span><span class="o">.</span><span class="n">page_batch_size</span> <span class="o">=</span> <span class="mi">64</span> <span class="c1"># default is 4</span>
</code></pre></div>
<h4 id="complete-example_1">Complete example</h4>
<p>For a complete example see <a href="../../examples/gpu_vlm_pipeline/">gpu_vlm_pipeline.py</a>.</p>
<h4 id="available-models">Available models</h4>
<p>Both LM Studio and Ollama rely on llama.cpp as runtime engine. For using this engine, models have to be converted to the gguf format.</p>
<p>Here is a list of known models which are available in gguf format and how to use them.</p>
<p>TBA.</p>
<h2 id="performance-results">Performance results</h2>
<h3 id="test-data">Test data</h3>
<table>
<thead>
<tr>
<th></th>
<th>PDF doc</th>
<th><a href="https://huggingface.co/datasets/vidore/vidore_v3_hr">ViDoRe V3 HR</a></th>
</tr>
</thead>
<tbody>
<tr>
<td>Num docs</td>
<td>1</td>
<td>14</td>
</tr>
<tr>
<td>Num pages</td>
<td>192</td>
<td>1110</td>
</tr>
<tr>
<td>Num tables</td>
<td>95</td>
<td>258</td>
</tr>
<tr>
<td>Format type</td>
<td>PDF</td>
<td>Parquet of images</td>
</tr>
</tbody>
</table>
<h3 id="test-infrastructure">Test infrastructure</h3>
<table>
<thead>
<tr>
<th></th>
<th>g6e.2xlarge</th>
<th>RTX 5090</th>
<th>RTX 5070</th>
</tr>
</thead>
<tbody>
<tr>
<td>Description</td>
<td>AWS instance <code>g6e.2xlarge</code></td>
<td>Linux bare metal machine</td>
<td>Windows 11 bare metal machine</td>
</tr>
<tr>
<td>CPU</td>
<td>8 vCPUs, AMD EPYC 7R13</td>
<td>16 vCPU, AMD Ryzen 7 9800</td>
<td>16 vCPU, AMD Ryzen 7 9800</td>
</tr>
<tr>
<td>RAM</td>
<td>64GB</td>
<td>128GB</td>
<td>64GB</td>
</tr>
<tr>
<td>GPU</td>
<td>NVIDIA L40S 48GB</td>
<td>NVIDIA GeForce RTX 5090</td>
<td>NVIDIA GeForce RTX 5070</td>
</tr>
<tr>
<td>CUDA Version</td>
<td>13.0, driver 580.95.05</td>
<td>13.0, driver 580.105.08</td>
<td>13.0, driver 581.57</td>
</tr>
</tbody>
</table>
<h3 id="results">Results</h3>
<table>
<thead>
<tr><th rowspan="2">Pipeline</th><th colspan="2">g6e.2xlarge</th><th colspan="2">RTX 5090</th><th colspan="2">RTX 5070</th></tr>
<tr><th>PDF doc</th><th>ViDoRe V3 HR</th><th>PDF doc</th><th>ViDoRe V3 HR</th><th>PDF doc</th><th>ViDoRe V3 HR</th></tr>
</thead>
<tbody>
<tr><td>Standard - Inline (no OCR)</td><td>3.1 pages/second</td><td>-</td><td>7.9 pages/second<br /><small><em>[cpu-only]* 1.5 pages/second</em></small></td><td>-</td><td>4.2 pages/second<br /><small><em>[cpu-only]* 1.2 pages/second</em></small></td><td>-</td></tr>
<tr><td>VLM - Inference server (GraniteDocling)</td><td>2.4 pages/second</td><td>-</td><td>3.8 pages/second</td><td>3.6-4.5 pages/second</td><td>-</td><td>-</td></tr>
</tbody>
</table>
<p><em>* cpu-only timing computed with 16 pytorch threads.</em></p>
</article>
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