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Model: RedHatAI/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8
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README.md

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+---
+license: mit
+tags:
+- deepseek
+- int8
+- vllm
+- llmcompressor
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
+library_name: transformers
+---
+
+# DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** Qwen2ForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+  - **Activation quantization:** INT8
+- **Release Date:** 2/4/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B).
+
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) to INT8 data type.
+This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
+Weight quantization also reduces disk size requirements by approximately 50%.
+
+Only the weights and activations of the linear operators within transformers blocks are quantized.
+Weights are quantized using a symmetric per-channel scheme, whereas quantizations are quantized using a symmetric per-token scheme.
+The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
+
+
+## Use with vLLM
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+number_gpus = 1
+model_name = "neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+sampling_params = SamplingParams(temperature=0.6, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
+llm = LLM(model=model_name, tensor_parallel_size=number_gpus, trust_remote_code=True)
+
+messages_list = [
+    [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
+]
+
+prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
+
+outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
+
+generated_text = [output.outputs[0].text for output in outputs]
+print(generated_text)
+```
+
+vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+## Creation
+
+This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+from llmcompressor.transformers import oneshot
+
+# Load model
+model_stub = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
+model_name = model_stub.split("/")[-1]
+
+num_samples = 1024
+max_seq_len = 8192
+
+tokenizer = AutoTokenizer.from_pretrained(model_stub)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_stub,
+    device_map="auto",
+    torch_dtype="auto",
+)
+
+def preprocess_fn(example):
+  return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
+
+ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
+ds = ds.map(preprocess_fn)
+
+# Configure the quantization algorithm and scheme
+recipe = [
+    SmoothQuantModifier(smoothing_strength=0.8),
+    QuantizationModifier(
+        targets="Linear",
+        scheme="W8A8",
+        ignore=["lm_head"],
+        dampening_frac=0.1,
+    ),
+]
+
+# Apply quantization
+oneshot(
+    model=model,
+    dataset=ds, 
+    recipe=recipe,
+    max_seq_length=max_seq_len,
+    num_calibration_samples=num_samples,
+)
+
+# Save to disk in compressed-tensors format
+save_path = model_name + "-quantized.w8a8
+model.save_pretrained(save_path)
+tokenizer.save_pretrained(save_path)
+print(f"Model and tokenizer saved to: {save_path}")
+```
+
+## Evaluation
+
+The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands:
+
+OpenLLM Leaderboard V1:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \
+  --tasks openllm \
+  --write_out \
+  --batch_size auto \
+  --output_path output_dir \
+  --show_config
+```
+
+OpenLLM Leaderboard V2:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \
+  --apply_chat_template \
+  --fewshot_as_multiturn \
+  --tasks leaderboard \
+  --write_out \
+  --batch_size auto \
+  --output_path output_dir \
+  --show_config
+```
+
+### Accuracy
+
+<table>
+  <thead>
+    <tr>
+      <th>Category</th>
+      <th>Metric</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-14B</th>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8</th>
+      <th>Recovery</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+<td rowspan="4"><b>Reasoning</b></td>
+<td>AIME 2024 (pass@1)</td>
+<td>66.67</td>
+<td>66.31</td>
+<td>99.46%</td>
+</tr>
+<tr>
+<td>MATH-500 (pass@1)</td>
+<td>94.66</td>
+<td>94.68</td>
+<td>100.02%</td>
+</tr>
+<tr>
+<td>GPQA Diamond (pass@1)</td>
+<td>59.35</td>
+<td>58.32</td>
+<td>98.26%</td>
+</tr>
+<tr>
+<td><b>Average Score</b></td>
+<td><b>73.56</b></td>
+<td><b>73.1</b></td>
+<td><b>99.37%</b></td>
+</tr>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V1</b></td>
+      <td>ARC-Challenge (Acc-Norm, 25-shot)</td>
+      <td>58.79</td>
+      <td>57.85</td>
+      <td>98.4%</td>
+    </tr>
+    <tr>
+      <td>GSM8K (Strict-Match, 5-shot)</td>
+      <td>87.04</td>
+      <td>87.79</td>
+      <td>100.9%</td>
+    </tr>
+    <tr>
+      <td>HellaSwag (Acc-Norm, 10-shot)</td>
+      <td>81.51</td>
+      <td>81.04</td>
+      <td>99.4%</td>
+    </tr>
+    <tr>
+      <td>MMLU (Acc, 5-shot)</td>
+      <td>74.46</td>
+      <td>74.26</td>
+      <td>99.7%</td>
+    </tr>
+    <tr>
+      <td>TruthfulQA (MC2, 0-shot)</td>
+      <td>54.77</td>
+      <td>54.94</td>
+      <td>100.3%</td>
+    </tr>
+    <tr>
+      <td>Winogrande (Acc, 5-shot)</td>
+      <td>69.38</td>
+      <td>70.48</td>
+      <td>101.6%</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>70.99</b></td>
+      <td><b>71.06</b></td>
+      <td><b>100.1%</b></td>
+    </tr>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V2</b></td>
+      <td>IFEval (Inst Level Strict Acc, 0-shot)</td>
+      <td>42.11</td>
+      <td>41.62</td>
+      <td>98.6%</td>
+    </tr>
+    <tr>
+      <td>BBH (Acc-Norm, 3-shot)</td>
+      <td>13.73</td>
+      <td>14.29</td>
+      <td>---</td>
+    </tr>
+    <tr>
+      <td>Math-Hard (Exact-Match, 4-shot)</td>
+      <td>0.00</td>
+      <td>0.00</td>
+      <td>---</td>
+    </tr>
+    <tr>
+      <td>GPQA (Acc-Norm, 0-shot)</td>
+      <td>35.07</td>
+      <td>37.22</td>
+      <td>106.2%</td>
+    </tr>
+    <tr>
+      <td>MUSR (Acc-Norm, 0-shot)</td>
+      <td>45.14</td>
+      <td>43.56</td>
+      <td>96.5%</td>
+    </tr>
+    <tr>
+      <td>MMLU-Pro (Acc, 5-shot)</td>
+      <td>34.86</td>
+      <td>33.63</td>
+      <td>96.5%</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>34.21</b></td>
+      <td><b>34.12</b></td>
+      <td><b>99.7%</b></td>
+    </tr>
+    <tr>
+      <td rowspan="4"><b>Coding</b></td>
+      <td>HumanEval (pass@1)</td>
+      <td>78.90</td>
+      <td>78.40</td>
+      <td><b>99.4%</b></td>
+    </tr>
+    <tr>
+      <td>HumanEval (pass@10)</td>
+      <td>89.80</td>
+      <td>90.10</td>
+      <td>100.3%</td>
+    </tr>
+    <tr>
+      <td>HumanEval+ (pass@10)</td>
+      <td>72.60</td>
+      <td>72.40</td>
+      <td>99.7%</td>
+    </tr>
+    <tr>
+      <td>HumanEval+ (pass@10)</td>
+      <td>84.90</td>
+      <td>84.90</td>
+      <td>100.0%</td>
+    </tr>
+  </tbody>
+</table>
+
+## Inference Performance
+
+
+This model achieves up to 1.6x speedup in both single-stream and 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>
+<summary>Benchmarking Command</summary>
+
+```
+guidellm --model neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8 --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server
+```
+</details>
+
+### Single-stream performance (measured with vLLM version 0.7.2)
+<table>
+  <thead>
+    <tr>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th>
+      <th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th>
+      <th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th>
+      <th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th>
+      <th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th>
+      <th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th>
+      <th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th>
+      <th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th>
+    </tr>
+    <tr>
+      <th>Hardware</th>
+      <th>Model</th>
+      <th>Average cost reduction</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+    </tr>
+  </thead>
+  <tbody style="text-align: center" >
+    <tr>
+      <th rowspan="3" valign="top">A6000x1</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-14B</th>
+      <td>---</td>
+      <td>5.4</td>
+      <td>837</td>
+      <td>10.7</td>
+      <td>419</td>
+      <td>5.5</td>
+      <td>813</td>
+      <td>5.6</td>
+      <td>805</td>
+      <td>42.2</td>
+      <td>107</td>
+      <td>42.8</td>
+      <td>105</td>
+      <td>22.9</td>
+      <td>197</td>
+      <td>71.7</td>
+      <td>63</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8</th>
+      <td>1.59</td>
+      <td>3.3</td>
+      <td>1345</td>
+      <td>6.7</td>
+      <td>673</td>
+      <td>3.4</td>
+      <td>1315</td>
+      <td>3.5</td>
+      <td>1296</td>
+      <td>26.5</td>
+      <td>170</td>
+      <td>26.8</td>
+      <td>168</td>
+      <td>14.5</td>
+      <td>310</td>
+      <td>48.3</td>
+      <td>93</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16</th>
+      <td>2.51</td>
+      <td>2.0</td>
+      <td>2275</td>
+      <td>4.0</td>
+      <td>1127</td>
+      <td>2.2</td>
+      <td>2072</td>
+      <td>2.3</td>
+      <td>1945</td>
+      <td>15.3</td>
+      <td>294</td>
+      <td>15.9</td>
+      <td>283</td>
+      <td>9.9</td>
+      <td>456</td>
+      <td>36.6</td>
+      <td>123</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">A100x1</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-14B</th>
+      <td>---</td>
+      <td>2.6</td>
+      <td>765</td>
+      <td>5.2</td>
+      <td>383</td>
+      <td>2.7</td>
+      <td>746</td>
+      <td>2.7</td>
+      <td>732</td>
+      <td>20.8</td>
+      <td>97</td>
+      <td>21.2</td>
+      <td>95</td>
+      <td>11.3</td>
+      <td>179</td>
+      <td>36.7</td>
+      <td>55</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8</th>
+      <td>1.34</td>
+      <td>1.9</td>
+      <td>1072</td>
+      <td>3.8</td>
+      <td>533</td>
+      <td>1.9</td>
+      <td>1045</td>
+      <td>1.9</td>
+      <td>1032</td>
+      <td>14.8</td>
+      <td>136</td>
+      <td>15.2</td>
+      <td>132</td>
+      <td>8.1</td>
+      <td>248</td>
+      <td>39.6</td>
+      <td>51</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16</th>
+      <td>1.93</td>
+      <td>1.2</td>
+      <td>1627</td>
+      <td>2.5</td>
+      <td>810</td>
+      <td>1.3</td>
+      <td>1530</td>
+      <td>1.4</td>
+      <td>1474</td>
+      <td>9.7</td>
+      <td>208</td>
+      <td>10.2</td>
+      <td>197</td>
+      <td>5.8</td>
+      <td>348</td>
+      <td>37.6</td>
+      <td>53</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">H100x1</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-14B</th>
+      <td>---</td>
+      <td>1.6</td>
+      <td>672</td>
+      <td>3.3</td>
+      <td>334</td>
+      <td>1.7</td>
+      <td>662</td>
+      <td>1.7</td>
+      <td>652</td>
+      <td>12.8</td>
+      <td>85</td>
+      <td>13.0</td>
+      <td>84</td>
+      <td>7.0</td>
+      <td>155</td>
+      <td>25.2</td>
+      <td>43</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-FP8-dynamic</th>
+      <td>1.33</td>
+      <td>1.2</td>
+      <td>925</td>
+      <td>2.3</td>
+      <td>467</td>
+      <td>1.2</td>
+      <td>908</td>
+      <td>1.2</td>
+      <td>896</td>
+      <td>9.3</td>
+      <td>118</td>
+      <td>9.5</td>
+      <td>115</td>
+      <td>5.2</td>
+      <td>210</td>
+      <td>23.9</td>
+      <td>46</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16</th>
+      <td>1.37</td>
+      <td>1.2</td>
+      <td>944</td>
+      <td>2.3</td>
+      <td>474</td>
+      <td>1.2</td>
+      <td>931</td>
+      <td>1.2</td>
+      <td>907</td>
+      <td>9.1</td>
+      <td>121</td>
+      <td>9.2</td>
+      <td>119</td>
+      <td>5.1</td>
+      <td>214</td>
+      <td>22.5</td>
+      <td>49</td>
+    </tr>
+  </tbody>
+</table>
+
+**Use case profiles: prompt tokens / generation tokens
+
+**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).
+
+
+### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)
+<table>
+  <thead>
+    <tr>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th>
+      <th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th>
+      <th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th>
+      <th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th>
+      <th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th>
+      <th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th>
+      <th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th>
+      <th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th>
+    </tr>
+    <tr>
+      <th>Hardware</th>
+      <th>Model</th>
+      <th>Average cost reduction</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+    </tr>
+  </thead>
+  <tbody style="text-align: center" >
+    <tr>
+      <th rowspan="3" valign="top">A6000x1</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-14B</th>
+      <td>---</td>
+      <td>13.7</td>
+      <td>30785</td>
+      <td>5.5</td>
+      <td>12327</td>
+      <td>6.5</td>
+      <td>14517</td>
+      <td>5.1</td>
+      <td>11439</td>
+      <td>2.0</td>
+      <td>4434</td>
+      <td>1.3</td>
+      <td>2982</td>
+      <td>0.6</td>
+      <td>1462</td>
+      <td>0.2</td>
+      <td>371</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8</th>
+      <td>1.44</td>
+      <td>21.4</td>
+      <td>48181</td>
+      <td>8.2</td>
+      <td>18421</td>
+      <td>9.8</td>
+      <td>22051</td>
+      <td>7.8</td>
+      <td>17462</td>
+      <td>2.8</td>
+      <td>6281</td>
+      <td>1.7</td>
+      <td>3758</td>
+      <td>1.0</td>
+      <td>2335</td>
+      <td>0.2</td>
+      <td>419</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16</th>
+      <td>0.98</td>
+      <td>12.7</td>
+      <td>28540</td>
+      <td>5.7</td>
+      <td>12796</td>
+      <td>5.4</td>
+      <td>12218</td>
+      <td>3.7</td>
+      <td>8401</td>
+      <td>2.5</td>
+      <td>5583</td>
+      <td>1.3</td>
+      <td>2987</td>
+      <td>0.7</td>
+      <td>1489</td>
+      <td>0.2</td>
+      <td>368</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">A100x1</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-14B</th>
+      <td>---</td>
+      <td>15.6</td>
+      <td>31306</td>
+      <td>7.1</td>
+      <td>14192</td>
+      <td>7.7</td>
+      <td>15435</td>
+      <td>6.0</td>
+      <td>11971</td>
+      <td>2.4</td>
+      <td>4878</td>
+      <td>1.6</td>
+      <td>3298</td>
+      <td>0.9</td>
+      <td>1862</td>
+      <td>0.2</td>
+      <td>355</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8</th>
+      <td>1.31</td>
+      <td>20.8</td>
+      <td>41907</td>
+      <td>9.3</td>
+      <td>18724</td>
+      <td>10.5</td>
+      <td>21043</td>
+      <td>8.4</td>
+      <td>16886</td>
+      <td>3.0</td>
+      <td>5975</td>
+      <td>1.9</td>
+      <td>3917</td>
+      <td>1.2</td>
+      <td>2481</td>
+      <td>0.2</td>
+      <td>464</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16</th>
+      <td>0.94</td>
+      <td>14.0</td>
+      <td>28146</td>
+      <td>6.5</td>
+      <td>13042</td>
+      <td>6.5</td>
+      <td>12987</td>
+      <td>5.1</td>
+      <td>10194</td>
+      <td>2.6</td>
+      <td>5269</td>
+      <td>1.5</td>
+      <td>2925</td>
+      <td>0.9</td>
+      <td>1849</td>
+      <td>0.2</td>
+      <td>382</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">H100x1</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-14B</th>
+      <td>---</td>
+      <td>31.4</td>
+      <td>34404</td>
+      <td>14.1</td>
+      <td>15482</td>
+      <td>16.6</td>
+      <td>18149</td>
+      <td>13.3</td>
+      <td>14572</td>
+      <td>4.7</td>
+      <td>5099</td>
+      <td>2.6</td>
+      <td>2849</td>
+      <td>1.9</td>
+      <td>2060</td>
+      <td>0.3</td>
+      <td>347</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-FP8-dynamic</th>
+      <td>1.31</td>
+      <td>40.9</td>
+      <td>44729</td>
+      <td>18.5</td>
+      <td>20260</td>
+      <td>22.1</td>
+      <td>24165</td>
+      <td>18.1</td>
+      <td>19779</td>
+      <td>5.7</td>
+      <td>6246</td>
+      <td>3.4</td>
+      <td>3681</td>
+      <td>2.5</td>
+      <td>2746</td>
+      <td>0.4</td>
+      <td>474</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16</th>
+      <td>1.12</td>
+      <td>33.3</td>
+      <td>36387</td>
+      <td>15.0</td>
+      <td>16453</td>
+      <td>17.6</td>
+      <td>19241</td>
+      <td>14.2</td>
+      <td>15576</td>
+      <td>4.6</td>
+      <td>5034</td>
+      <td>3.0</td>
+      <td>3292</td>
+      <td>2.2</td>
+      <td>2412</td>
+      <td>0.4</td>
+      <td>481</td>
+    </tr>
+  </tbody>
+</table>
+
+**Use case profiles: 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).

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+      - - ['re:.*q_proj', 're:.*k_proj', 're:.*v_proj']
+        - re:.*input_layernorm
+      - - ['re:.*gate_proj', 're:.*up_proj']
+        - re:.*post_attention_layernorm
+      - - ['re:.*down_proj']
+        - re:.*up_proj
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+      dampening_frac: 0.1
+      ignore: [lm_head]
+      config_groups:
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+          weights: {num_bits: 8, type: int, symmetric: true, strategy: channel, observer: mse}
+          input_activations: {num_bits: 8, type: int, symmetric: true, strategy: token, dynamic: true,
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