Update README.md

README.md

@@ -235,7 +235,7 @@ lm_eval \
 ## Inference Performance
 
 
-This model achieves up to xxx speedup in single-stream deployment and up to xxx speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
+This model achieves up to 1.84x speedup in single-stream deployment and up to 1.85x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
 The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).
 
 <details>
@@ -387,22 +387,22 @@ The following performance benchmarks were conducted with [vLLM](https://docs.vll
     <tr>
       <td>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w8a8</td>
       <td>1.84</td>
-      <td>1.2</td>
-      <td>586</td>
-      <td>4.0</td>
-      <td>2042</td>
-      <td>4.6</td>
-      <td>2270</td>
+      <td>0.6</td>
+      <td>293</td>
+      <td>2.0</td>
+      <td>1021</td>
+      <td>2.3</td>
+      <td>1135</td>
     </tr>
     <tr>
       <td>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16</td>
       <td>2.73</td>
-      <td>2.4</td>
-      <td>1256</td>
-      <td>12.8</td>
-      <td>6364</td>
-      <td>16.0</td>
-      <td>8076</td>
+      <td>0.6</td>
+      <td>314</td>
+      <td>3.2</td>
+      <td>1591</td>
+      <td>4.0</td>
+      <td>2019</td>
     </tr>
     <tr>
       <th rowspan="3" valign="top">H100x4</td>
@@ -418,22 +418,22 @@ The following performance benchmarks were conducted with [vLLM](https://docs.vll
     <tr>
       <td>neuralmagic/Qwen2-VL-72B-Instruct-FP8-Dynamic</td>
       <td>1.70</td>
-      <td>1.6</td>
-      <td>457</td>
-      <td>4.4</td>
-      <td>1207</td>
-      <td>4.8</td>
-      <td>1296</td>
+      <td>0.8</td>
+      <td>236</td>
+      <td>2.2</td>
+      <td>623</td>
+      <td>2.4</td>
+      <td>669</td>
     </tr>
     <tr>
       <td>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16</td>
       <td>2.35</td>
-      <td>5.2</td>
-      <td>1400</td>
-      <td>13.2</td>
-      <td>3640</td>
-      <td>14.4</td>
-      <td>3976</td>
+      <td>1.3</td>
+      <td>350</td>
+      <td>3.3</td>
+      <td>910</td>
+      <td>3.6</td>
+      <td>994</td>
     </tr>
   </tbody>
 </table>