Update README.md

README.md

@@ -25,7 +25,7 @@ library_name: transformers
 - **Model Developers:** Neural Magic
 
 Quantized version of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct).
-It achieves an average score of xxxx on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves xxxx.
+It achieves an average score of 70.26 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 70.30.
 
 ### Model Optimizations
 
@@ -224,3 +224,258 @@ evalplus.evaluate \
 |-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
 | HumanEval Pass@1                        | 71.00                            | 70.50                                       |
 
+
+## Inference Performance
+
+
+This model achieves up to 1.6x speedup in single-stream deployment and up to 1.7x 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 0.6.6.post1, and [GuideLLM](https://github.com/neuralmagic/guidellm).
+
+### Single-stream performance (measured with vLLM version 0.6.6.post1)
+<table>
+  <tr>
+    <td></td>
+    <td></td>
+    <td></td>
+    <th style="text-align: center;" colspan="7" >Latency (s)</th>
+  </tr>
+  <tr>
+    <th>GPU class</th>
+    <th>Model</th>
+    <th>Speedup</th>
+    <th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
+    <th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
+    <th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
+    <th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
+    <th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
+    <th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
+    <th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
+  </tr>
+  <tr>
+    <td style="vertical-align: middle;" rowspan="3" >A5000</td>
+    <td>granite-3.1-8b-instruct</td>
+    <td></td>
+    <td>28.3</td>
+    <td>3.7</td>
+    <td>28.8</td>
+    <td>3.8</td>
+    <td>3.6</td>
+    <td>7.2</td>
+    <td>15.7</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td>
+    <td>1.60</td>
+    <td>17.7</td>
+    <td>2.3</td>
+    <td>18.0</td>
+    <td>2.4</td>
+    <td>2.2</td>
+    <td>4.5</td>
+    <td>10.0</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w4a16</td>
+    <td>2.61</td>
+    <td>10.3</td>
+    <td>1.5</td>
+    <td>10.7</td>
+    <td>1.5</td>
+    <td>1.3</td>
+    <td>2.7</td>
+    <td>6.6</td>
+  </tr>
+  <tr>
+    <td style="vertical-align: middle;" rowspan="3" >A6000</td>
+    <td>granite-3.1-8b-instruct</td>
+    <td></td>
+    <td>25.8</td>
+    <td>3.4</td>
+    <td>26.2</td>
+    <td>3.4</td>
+    <td>3.3</td>
+    <td>6.5</td>
+    <td>14.2</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td>
+    <td>1.50</td>
+    <td>17.4</td>
+    <td>2.3</td>
+    <td>16.9</td>
+    <td>2.2</td>
+    <td>2.2</td>
+    <td>4.4</td>
+    <td>9.8</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w4a16</td>
+    <td>2.48</td>
+    <td>10.0</td>
+    <td>1.4</td>
+    <td>10.4</td>
+    <td>1.5</td>
+    <td>1.3</td>
+    <td>2.5</td>
+    <td>6.2</td>
+  </tr>
+  <tr>
+    <td style="vertical-align: middle;" rowspan="3" >A100</td>
+    <td>granite-3.1-8b-instruct</td>
+    <td></td>
+    <td>13.6</td>
+    <td>1.8</td>
+    <td>13.7</td>
+    <td>1.8</td>
+    <td>1.7</td>
+    <td>3.4</td>
+    <td>7.3</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td>
+    <td>1.31</td>
+    <td>10.4</td>
+    <td>1.3</td>
+    <td>10.5</td>
+    <td>1.4</td>
+    <td>1.3</td>
+    <td>2.6</td>
+    <td>5.6</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w4a16</td>
+    <td>1.80</td>
+    <td>7.3</td>
+    <td>1.0</td>
+    <td>7.4</td>
+    <td>1.0</td>
+    <td>0.9</td>
+    <td>1.9</td>
+    <td>4.3</td>
+  </tr>
+</table>
+
+
+### Multi-stream asynchronous performance (measured with vLLM version 0.6.6.post1)
+<table>
+  <tr>
+    <td></td>
+    <td></td>
+    <td></td>
+    <th style="text-align: center;" colspan="7" >Maximum Throughput (Queries per Second)</th>
+  </tr>
+  <tr>
+    <th>GPU class</th>
+    <th>Model</th>
+    <th>Speedup</th>
+    <th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
+    <th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
+    <th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
+    <th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
+    <th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
+    <th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
+    <th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
+  </tr>
+  <tr>
+    <td style="vertical-align: middle;" rowspan="3" >A5000</td>
+    <td>granite-3.1-8b-instruct</td>
+    <td></td>
+    <td>0.8</td>
+    <td>3.1</td>
+    <td>0.4</td>
+    <td>2.5</td>
+    <td>6.7</td>
+    <td>2.7</td>
+    <td>0.3</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td>
+    <td>1.71</td>
+    <td>1.3</td>
+    <td>5.2</td>
+    <td>0.9</td>
+    <td>4.0</td>
+    <td>10.5</td>
+    <td>4.4</td>
+    <td>0.5</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w4a16</td>
+    <td>1.46</td>
+    <td>1.3</td>
+    <td>3.9</td>
+    <td>0.8</td>
+    <td>2.9</td>
+    <td>8.2</td>
+    <td>3.6</td>
+    <td>0.5</td>
+  </tr>
+  <tr>
+    <td style="vertical-align: middle;" rowspan="3" >A6000</td>
+    <td>granite-3.1-8b-instruct</td>
+    <td></td>
+    <td>1.3</td>
+    <td>5.1</td>
+    <td>0.9</td>
+    <td>4.0</td>
+    <td>0.3</td>
+    <td>4.3</td>
+    <td>0.6</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td>
+    <td>1.39</td>
+    <td>1.8</td>
+    <td>7.0</td>
+    <td>1.3</td>
+    <td>5.6</td>
+    <td>14.0</td>
+    <td>6.3</td>
+    <td>0.8</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w4a16</td>
+    <td>1.09</td>
+    <td>1.9</td>
+    <td>4.8</td>
+    <td>1.0</td>
+    <td>3.8</td>
+    <td>10.0</td>
+    <td>5.0</td>
+    <td>0.6</td>
+  </tr>
+  <tr>
+    <td style="vertical-align: middle;" rowspan="3" >A100</td>
+    <td>granite-3.1-8b-instruct</td>
+    <td></td>
+    <td>3.1</td>
+    <td>10.7</td>
+    <td>2.1</td>
+    <td>8.5</td>
+    <td>20.6</td>
+    <td>9.6</td>
+    <td>1.4</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td>
+    <td>1.23</td>
+    <td>3.8</td>
+    <td>14.2</td>
+    <td>2.1</td>
+    <td>11.4</td>
+    <td>25.9</td>
+    <td>12.1</td>
+    <td>1.7</td>
+  </tr>
+  <tr>
+    <td>granite-3.1-8b-instruct-quantized.w4a16</td>
+    <td>0.96</td>
+    <td>3.4</td>
+    <td>9.0</td>
+    <td>2.6</td>
+    <td>7.2</td>
+    <td>18.0</td>
+    <td>8.8</td>
+    <td>1.3</td>
+  </tr>
+</table>