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@@ -25,7 +25,7 @@ library_name: transformers
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  - **Model Developers:** Neural Magic
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  Quantized version of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct).
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- 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.
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  ### Model Optimizations
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@@ -220,3 +220,326 @@ evalplus.evaluate \
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  | HumanEval Pass@1 | 71.00 | 70.50 |
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  - **Model Developers:** Neural Magic
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  Quantized version of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct).
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+ It achieves an average score of 69.81 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 70.30.
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  ### Model Optimizations
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  | HumanEval Pass@1 | 71.00 | 70.50 |
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+ ## Inference Performance
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+
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+
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+ 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.
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+ 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).
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+
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+ ### Single-stream performance (measured with vLLM version 0.6.6.post1)
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+ <table>
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+ <tr>
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+ <td></td>
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+ <td></td>
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+ <td></td>
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+ <th style="text-align: center;" colspan="7" >Latency (s)</th>
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+ </tr>
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+ <tr>
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+ <th>GPU class</th>
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+ <th>Model</th>
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+ <th>Speedup</th>
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+ <th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
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+ <th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
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+ <th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
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+ <th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
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+ <th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
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+ <th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
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+ <th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
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+ </tr>
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+ <tr>
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+ <td style="vertical-align: middle;" rowspan="3" >A5000</td>
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+ <td>granite-3.1-8b-instruct</td>
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+ <td></td>
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+ <td>28.3</td>
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+ <td>3.7</td>
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+ <td>28.8</td>
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+ <td>3.8</td>
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+ <td>3.6</td>
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+ <td>7.2</td>
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+ <td>15.7</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w8a8</td>
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+ <td>1.60</td>
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+ <td>17.7</td>
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+ <td>2.3</td>
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+ <td>18.0</td>
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+ <td>2.4</td>
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+ <td>2.2</td>
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+ <td>4.5</td>
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+ <td>10.0</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
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+ <td>2.61</td>
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+ <td>10.3</td>
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+ <td>1.5</td>
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+ <td>10.7</td>
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+ <td>1.5</td>
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+ <td>1.3</td>
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+ <td>2.7</td>
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+ <td>6.6</td>
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+ </tr>
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+ <tr>
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+ <td style="vertical-align: middle;" rowspan="3" >A6000</td>
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+ <td>granite-3.1-8b-instruct</td>
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+ <td></td>
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+ <td>25.8</td>
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+ <td>3.4</td>
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+ <td>26.2</td>
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+ <td>3.4</td>
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+ <td>3.3</td>
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+ <td>6.5</td>
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+ <td>14.2</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w8a8</td>
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+ <td>1.50</td>
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+ <td>17.4</td>
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+ <td>2.3</td>
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+ <td>16.9</td>
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+ <td>2.2</td>
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+ <td>2.2</td>
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+ <td>4.4</td>
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+ <td>9.8</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
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+ <td>2.48</td>
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+ <td>10.0</td>
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+ <td>1.4</td>
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+ <td>10.4</td>
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+ <td>1.5</td>
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+ <td>1.3</td>
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+ <td>2.5</td>
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+ <td>6.2</td>
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+ </tr>
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+ <tr>
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+ <td style="vertical-align: middle;" rowspan="3" >A100</td>
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+ <td>granite-3.1-8b-instruct</td>
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+ <td></td>
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+ <td>13.6</td>
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+ <td>1.8</td>
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+ <td>13.7</td>
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+ <td>1.8</td>
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+ <td>1.7</td>
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+ <td>3.4</td>
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+ <td>7.3</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w8a8</td>
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+ <td>1.31</td>
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+ <td>10.4</td>
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+ <td>1.3</td>
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+ <td>10.5</td>
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+ <td>1.4</td>
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+ <td>1.3</td>
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+ <td>2.6</td>
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+ <td>5.6</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
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+ <td>1.80</td>
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+ <td>7.3</td>
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+ <td>1.0</td>
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+ <td>7.4</td>
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+ <td>1.0</td>
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+ <td>0.9</td>
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+ <td>1.9</td>
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+ <td>4.3</td>
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+ </tr>
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+ <tr>
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+ <td style="vertical-align: middle;" rowspan="3" >L40</td>
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+ <td>granite-3.1-8b-instruct</td>
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+ <td></td>
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+ <td>25.1</td>
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+ <td>3.2</td>
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+ <td>25.3</td>
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+ <td>3.2</td>
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+ <td>3.2</td>
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+ <td>6.3</td>
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+ <td>13.4</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-FP8-dynamic</td>
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+ <td>1.47</td>
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+ <td>16.8</td>
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+ <td>2.2</td>
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+ <td>17.1</td>
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+ <td>2.2</td>
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+ <td>2.1</td>
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+ <td>4.2</td>
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+ <td>9.3</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
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+ <td>2.72</td>
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+ <td>8.9</td>
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+ <td>1.2</td>
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+ <td>9.2</td>
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+ <td>1.2</td>
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+ <td>1.1</td>
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+ <td>2.3</td>
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+ <td>5.3</td>
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+ </tr>
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+ </table>
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+
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+
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+ ### Multi-stream asynchronous performance (measured with vLLM version 0.6.6.post1)
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+ <table>
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+ <tr>
391
+ <td></td>
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+ <td></td>
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+ <td></td>
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+ <th style="text-align: center;" colspan="7" >Maximum Throughput (Queries per Second)</th>
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+ </tr>
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+ <tr>
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+ <th>GPU class</th>
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+ <th>Model</th>
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+ <th>Speedup</th>
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+ <th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
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+ <th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
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+ <th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
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+ <th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
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+ <th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
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+ <th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
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+ <th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
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+ </tr>
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+ <tr>
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+ <td style="vertical-align: middle;" rowspan="3" >A5000</td>
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+ <td>granite-3.1-8b-instruct</td>
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+ <td></td>
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+ <td>0.8</td>
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+ <td>3.1</td>
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+ <td>0.4</td>
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+ <td>2.5</td>
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+ <td>6.7</td>
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+ <td>2.7</td>
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+ <td>0.3</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w8a8</td>
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+ <td>1.71</td>
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+ <td>1.3</td>
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+ <td>5.2</td>
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+ <td>0.9</td>
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+ <td>4.0</td>
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+ <td>10.5</td>
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+ <td>4.4</td>
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+ <td>0.5</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
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+ <td>1.46</td>
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+ <td>1.3</td>
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+ <td>3.9</td>
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+ <td>0.8</td>
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+ <td>2.9</td>
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+ <td>8.2</td>
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+ <td>3.6</td>
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+ <td>0.5</td>
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+ </tr>
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+ <tr>
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+ <td style="vertical-align: middle;" rowspan="3" >A6000</td>
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+ <td>granite-3.1-8b-instruct</td>
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+ <td></td>
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+ <td>1.3</td>
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+ <td>5.1</td>
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+ <td>0.9</td>
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+ <td>4.0</td>
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+ <td>0.3</td>
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+ <td>4.3</td>
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+ <td>0.6</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w8a8</td>
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+ <td>1.39</td>
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+ <td>1.8</td>
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+ <td>7.0</td>
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+ <td>1.3</td>
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+ <td>5.6</td>
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+ <td>14.0</td>
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+ <td>6.3</td>
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+ <td>0.8</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
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+ <td>1.09</td>
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+ <td>1.9</td>
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+ <td>4.8</td>
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+ <td>1.0</td>
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+ <td>3.8</td>
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+ <td>10.0</td>
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+ <td>5.0</td>
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+ <td>0.6</td>
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+ </tr>
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+ <tr>
477
+ <td style="vertical-align: middle;" rowspan="3" >A100</td>
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+ <td>granite-3.1-8b-instruct</td>
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+ <td></td>
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+ <td>3.1</td>
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+ <td>10.7</td>
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+ <td>2.1</td>
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+ <td>8.5</td>
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+ <td>20.6</td>
485
+ <td>9.6</td>
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+ <td>1.4</td>
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+ </tr>
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+ <tr>
489
+ <td>granite-3.1-8b-instruct-quantized.w8a8</td>
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+ <td>1.23</td>
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+ <td>3.8</td>
492
+ <td>14.2</td>
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+ <td>2.1</td>
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+ <td>11.4</td>
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+ <td>25.9</td>
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+ <td>12.1</td>
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+ <td>1.7</td>
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+ </tr>
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+ <tr>
500
+ <td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
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+ <td>0.96</td>
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+ <td>3.4</td>
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+ <td>9.0</td>
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+ <td>2.6</td>
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+ <td>7.2</td>
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+ <td>18.0</td>
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+ <td>8.8</td>
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+ <td>1.3</td>
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+ </tr>
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+ <tr>
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+ <td style="vertical-align: middle;" rowspan="3" >L40</td>
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+ <td>granite-3.1-8b-instruct</td>
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+ <td></td>
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+ <td>1.4</td>
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+ <td>7.8</td>
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+ <td>1.1</td>
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+ <td>6.2</td>
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+ <td>15.5</td>
519
+ <td>6.0</td>
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+ <td>0.7</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-8b-instruct-FP8-dynamic</td>
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+ <td>1.12</td>
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+ <td>2.1</td>
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+ <td>7.4</td>
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+ <td>1.3</td>
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+ <td>5.9</td>
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+ <td>15.3</td>
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+ <td>6.9</td>
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+ <td>0.8</td>
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+ </tr>
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+ <tr>
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+ <td>granite-3.1-2b-instruct-quantized.w4a16<br>(this model)</td>
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+ <td>1.29</td>
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+ <td>2.4</td>
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+ <td>8.9</td>
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+ <td>1.4</td>
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+ <td>7.1</td>
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+ <td>17.8</td>
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+ <td>7.8</td>
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+ <td>1.0</td>
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+ </tr>
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+ </table>
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+