Update README.md

README.md

@@ -769,6 +769,104 @@ The following performance benchmarks were conducted with [vLLM](https://docs.vll
 
 ### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)
 
+<table border="1" class="dataframe">
+  <thead>
+    <tr>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
+      <th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
+      <th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
+    </tr>
+    <tr>
+      <th>Hardware</th>
+      <th>Model</th>
+      <th>Average Cost Reduction</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>Queries Per Dollar</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>Queries Per Dollar</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>Queries Per Dollar</th>
+    </tr>
+  </thead>
+  <tbody style="text-align: center">
+   <tr>
+      <th rowspan="3" valign="top">A100x4</th>
+      <td>neuralmagic/Pixtral-Large-Instruct-2411-hf</td>
+      <td></td>
+      <td>0.4</td>
+      <td>222</td>
+      <td>0.7</td>
+      <td>341</td>
+      <td>0.8</td>
+      <td>399</td>
+    </tr>
+    <tr>
+      <td>neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w8a8</td>
+      <td>1.70</td>
+      <td>0.8</td>
+      <td>383</td>
+      <td>1.1</td>
+      <td>571</td>
+      <td>1.3</td>
+      <td>674</td>
+    </tr>
+    <tr>
+      <td>neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16</td>
+      <td>1.48</td>
+      <td>0.5</td>
+      <td>276</td>
+      <td>1.0</td>
+      <td>505</td>
+      <td>1.4</td>
+      <td>680</td>
+    </tr>
+    <tr>
+      <<th rowspan="3" valign="top">H100x4</th>
+      <td>neuralmagic/Pixtral-Large-Instruct-2411-hf</td>
+      <td></td>
+      <td>1.0</td>
+      <td>284</td>
+      <td>1.6</td>
+      <td>465</td>
+      <td>1.8</td>
+      <td>511</td>
+    </tr>
+    <tr>
+      <td>neuralmagic/Pixtral-Large-Instruct-2411-hf-FP8-Dynamic</td>
+      <td>1.61</td>
+      <td>1.7</td>
+      <td>467</td>
+      <td>2.6</td>
+      <td>726</td>
+      <td>3.2</td>
+      <td>908</td>
+    </tr>
+    <tr>
+      <td>neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16</td>
+      <td>1.33</td>
+      <td>1.4</td>
+      <td>393</td>
+      <td>2.2</td>
+      <td>726</td>
+      <td>2.7</td>
+      <td>764</td>
+    </tr>
+  </tbody>
+</table>
+
+**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens
+
+**QPS: Queries per second.
+
+**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).
+
+## The Mistral AI Team
+
+Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Diogo Costa, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)
+
 <table border="1" class="dataframe">
   <thead>
     <tr>