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--- |
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license: mit |
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tags: |
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- deepseek |
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- fp8 |
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- vllm |
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
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library_name: transformers |
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--- |
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# DeepSeek-R1-Distill-Qwen-7B-FP8-dynamic |
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## Model Overview |
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- **Model Architecture:** Qwen2ForCausalLM |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** FP8 |
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- **Activation quantization:** FP8 |
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- **Release Date:** 2/5/2025 |
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- **Version:** 1.0 |
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- **Model Developers:** Neural Magic |
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Quantized version of [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B). |
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### Model Optimizations |
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This model was obtained by quantizing the weights and activations of [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) to FP8 data type. |
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. |
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Only the weights and activations of the linear operators within transformers blocks are quantized. |
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Weights are quantized using a symmetric per-channel scheme, whereas quantizations are quantized using a symmetric per-token scheme. |
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[LLM Compressor](https://github.com/vllm-project/llm-compressor) is used for quantization. |
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## Use with vLLM |
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. |
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```python |
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from transformers import AutoTokenizer |
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from vllm import LLM, SamplingParams |
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number_gpus = 1 |
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model_name = "neuralmagic/DeepSeek-R1-Distill-Qwen-7B-dynamic" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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sampling_params = SamplingParams(temperature=0.6, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id]) |
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llm = LLM(model=model_name, tensor_parallel_size=number_gpus, trust_remote_code=True) |
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messages_list = [ |
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[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}], |
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] |
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prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] |
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outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) |
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generated_text = [output.outputs[0].text for output in outputs] |
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print(generated_text) |
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``` |
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vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. |
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## Creation |
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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from llmcompressor.modifiers.quantization import QuantizationModifier |
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from llmcompressor.transformers import oneshot |
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import os |
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# Load model |
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model_stub = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" |
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model_name = model_stub.split("/")[-1] |
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model = AutoModelForCausalLM.from_pretrained( |
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model_stub, |
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torch_dtype="auto", |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_stub) |
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# Configure the quantization algorithm and scheme |
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recipe = QuantizationModifier( |
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targets="Linear", |
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scheme="FP8_DYNAMIC", |
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ignore=["lm_head"], |
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) |
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# Apply quantization |
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oneshot( |
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model=model, |
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recipe=recipe, |
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) |
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# Save to disk in compressed-tensors format |
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save_path = model_name + "-FP8-dynamic |
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model.save_pretrained(save_path) |
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tokenizer.save_pretrained(save_path) |
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print(f"Model and tokenizer saved to: {save_path}") |
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``` |
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## Evaluation |
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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: |
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OpenLLM Leaderboard V1: |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Qwen-7B-FP8-dynamic",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \ |
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--tasks openllm \ |
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--write_out \ |
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--batch_size auto \ |
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--output_path output_dir \ |
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--show_config |
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``` |
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OpenLLM Leaderboard V2: |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Qwen-7B-FP8-dynamic",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \ |
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--apply_chat_template \ |
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--fewshot_as_multiturn \ |
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--tasks leaderboard \ |
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--write_out \ |
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--batch_size auto \ |
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--output_path output_dir \ |
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--show_config |
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``` |
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### Accuracy |
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<table> |
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<thead> |
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<tr> |
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<th>Category</th> |
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<th>Metric</th> |
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<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-7B</th> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-FP8-dynamic</th> |
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<th>Recovery</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V1</b></td> |
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<td>ARC-Challenge (Acc-Norm, 25-shot)</td> |
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<td>50.51</td> |
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<td>50.51</td> |
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<td>100.0%</td> |
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</tr> |
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<tr> |
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<td>GSM8K (Strict-Match, 5-shot)</td> |
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<td>78.62</td> |
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<td>79.83</td> |
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<td>101.5%</td> |
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</tr> |
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<tr> |
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<td>HellaSwag (Acc-Norm, 10-shot)</td> |
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<td>61.90</td> |
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<td>61.62</td> |
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<td>99.6%</td> |
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</tr> |
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<tr> |
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<td>MMLU (Acc, 5-shot)</td> |
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<td>54.19</td> |
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<td>53.76</td> |
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<td>99.2%</td> |
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</tr> |
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<tr> |
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<td>TruthfulQA (MC2, 0-shot)</td> |
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<td>45.55</td> |
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<td>46.14</td> |
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<td>101.3%</td> |
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</tr> |
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<tr> |
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<td>Winogrande (Acc, 5-shot)</td> |
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<td>61.56</td> |
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<td>60.54</td> |
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<td>98.3%</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>58.72</b></td> |
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<td><b>58.73</b></td> |
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<td><b>100.0%</b></td> |
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</tr> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V2</b></td> |
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<td>IFEval (Inst Level Strict Acc, 0-shot)</td> |
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<td>39.38</td> |
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<td>39.01</td> |
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<td>99.1%</td> |
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</tr> |
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<tr> |
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<td>BBH (Acc-Norm, 3-shot)</td> |
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<td>6.97</td> |
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<td>6.19</td> |
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<td>---</td> |
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</tr> |
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<tr> |
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<td>Math-Hard (Exact-Match, 4-shot)</td> |
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<td>0.00</td> |
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<td>0.00</td> |
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<td>---</td> |
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</tr> |
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<tr> |
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<td>GPQA (Acc-Norm, 0-shot)</td> |
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<td>1.81</td> |
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<td>1.63</td> |
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<td>---</td> |
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</tr> |
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<tr> |
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<td>MUSR (Acc-Norm, 0-shot)</td> |
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<td>4.68</td> |
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<td>5.08</td> |
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<td>---</td> |
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</tr> |
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<tr> |
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<td>MMLU-Pro (Acc, 5-shot)</td> |
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<td>1.66</td> |
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<td>1.76</td> |
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<td>---</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>9.08</b></td> |
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<td><b>8.94</b></td> |
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<td><b>---</b></td> |
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</tr> |
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<tr> |
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<td rowspan="4"><b>Coding</b></td> |
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<td>HumanEval (pass@1)</td> |
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<td>40.80</td> |
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<td>39.50</td> |
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<td><b>96.8%</b></td> |
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</tr> |
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<tr> |
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<td>HumanEval (pass@10)</td> |
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<td>64.40</td> |
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<td>62.10</td> |
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<td>96.4%</td> |
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</tr> |
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<tr> |
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<td>HumanEval+ (pass@10)</td> |
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<td>38.50</td> |
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<td>37.20</td> |
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<td>96.6%</td> |
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</tr> |
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<tr> |
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<td>HumanEval+ (pass@10)</td> |
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<td>60.40</td> |
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<td>59.30</td> |
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<td>98.2%</td> |
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</tr> |
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</tbody> |
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</table> |
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## Inference Performance |
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This model achieves up to 1.4x speedup in single-stream deployment and up to 1.2x 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 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm). |
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<details> |
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<summary>Benchmarking Command</summary> |
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``` |
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guidellm --model neuralmagic/DeepSeek-R1-Distill-Qwen-7B-FP8-dynamic --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server |
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``` |
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</details> |
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### Single-stream performance (measured with vLLM version 0.7.2) |
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<table> |
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<thead> |
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<tr> |
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<th></th> |
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<th></th> |
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<th></th> |
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<th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th> |
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<th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th> |
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<th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th> |
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<th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th> |
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<th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th> |
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<th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th> |
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<th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th> |
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<th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th> |
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</tr> |
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<tr> |
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<th>Hardware</th> |
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<th>Model</th> |
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<th>Average cost reduction</th> |
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<th>Latency (s)</th> |
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<th>QPD</th> |
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<th>Latency (s)</th> |
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<th>QPD</th> |
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<th>Latency (s)</th> |
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<th>QPD</th> |
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<th>Latency (s)</th> |
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<th>QPD</th> |
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<th>Latency (s)</th> |
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<th>QPD</th> |
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<th>Latency (s)</th> |
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<th>QPD</th> |
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<th>Latency (s)</th> |
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<th>QPD</th> |
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<th>Latency (s)</th> |
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<th>QPD</th> |
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</tr> |
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</thead> |
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<tbody style="text-align: center" > |
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<tr> |
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<th rowspan="3" valign="top">A6000x1</th> |
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<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-7B</th> |
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<td>---</td> |
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<td>2.9</td> |
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<td>1576</td> |
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<td>5.7</td> |
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<td>788</td> |
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<td>2.9</td> |
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<td>1535</td> |
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<td>3.0</td> |
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<td>1496</td> |
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<td>22.6</td> |
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<td>199</td> |
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<td>23.2</td> |
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<td>194</td> |
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<td>12.1</td> |
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<td>370</td> |
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<td>38.5</td> |
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<td>117</td> |
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</tr> |
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<tr> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w8a8</th> |
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<td>1.56</td> |
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<td>1.8</td> |
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<td>2495</td> |
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<td>3.7</td> |
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<td>1223</td> |
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<td>1.9</td> |
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<td>2384</td> |
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<td>1.9</td> |
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<td>2393</td> |
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<td>14.3</td> |
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<td>315</td> |
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<td>14.8</td> |
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<td>304</td> |
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<td>7.9</td> |
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<td>572</td> |
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<td>25.3</td> |
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<td>178</td> |
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</tr> |
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<tr> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16</th> |
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<td>2.41</td> |
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<td>1.1</td> |
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<td>4086</td> |
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<td>2.3</td> |
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<td>1998</td> |
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<td>1.2</td> |
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<td>3783</td> |
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<td>1.3</td> |
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<td>3527</td> |
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<td>8.6</td> |
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<td>526</td> |
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<td>8.8</td> |
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<td>512</td> |
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<td>5.2</td> |
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<td>860</td> |
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<td>22.7</td> |
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<td>198</td> |
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</tr> |
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<tr> |
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<th rowspan="3" valign="top">A100x1</th> |
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<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-7B</th> |
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<td>---</td> |
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<td>1.4</td> |
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<td>1389</td> |
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<td>2.9</td> |
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<td>691</td> |
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<td>1.5</td> |
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<td>1358</td> |
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<td>1.5</td> |
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<td>1329</td> |
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<td>11.5</td> |
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<td>175</td> |
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<td>11.6</td> |
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<td>174</td> |
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<td>6.2</td> |
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<td>326</td> |
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<td>21.5</td> |
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<td>93</td> |
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</tr> |
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<tr> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w8a8</th> |
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<td>1.28</td> |
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<td>1.1</td> |
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<td>1850</td> |
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<td>2.2</td> |
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<td>905</td> |
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<td>1.1</td> |
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<td>1807</td> |
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<td>1.1</td> |
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<td>1750</td> |
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<td>8.6</td> |
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<td>233</td> |
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<td>8.7</td> |
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<td>230</td> |
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<td>4.7</td> |
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<td>431</td> |
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<td>23.1</td> |
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<td>87</td> |
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</tr> |
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<tr> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16</th> |
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<td>1.72</td> |
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<td>0.8</td> |
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<td>2575</td> |
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<td>1.5</td> |
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<td>1298</td> |
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<td>0.8</td> |
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<td>2461</td> |
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<td>0.8</td> |
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<td>2382</td> |
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<td>6.1</td> |
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<td>331</td> |
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<td>6.2</td> |
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<td>323</td> |
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<td>3.6</td> |
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<td>566</td> |
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<td>22.7</td> |
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<td>89</td> |
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</tr> |
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<tr> |
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<th rowspan="3" valign="top">H100x1</th> |
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<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-7B</th> |
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<td>---</td> |
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<td>0.9</td> |
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<td>1161</td> |
|
<td>1.9</td> |
|
<td>579</td> |
|
<td>1.0</td> |
|
<td>1138</td> |
|
<td>1.0</td> |
|
<td>1121</td> |
|
<td>7.5</td> |
|
<td>146</td> |
|
<td>7.6</td> |
|
<td>145</td> |
|
<td>3.9</td> |
|
<td>279</td> |
|
<td>15.4</td> |
|
<td>71</td> |
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</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-FP8-dynamic</th> |
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<td>1.34</td> |
|
<td>0.7</td> |
|
<td>1585</td> |
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<td>1.4</td> |
|
<td>786</td> |
|
<td>0.7</td> |
|
<td>1577</td> |
|
<td>0.7</td> |
|
<td>1524</td> |
|
<td>5.3</td> |
|
<td>207</td> |
|
<td>5.5</td> |
|
<td>197</td> |
|
<td>2.9</td> |
|
<td>382</td> |
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<td>14.3</td> |
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<td>77</td> |
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</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16</th> |
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<td>1.33</td> |
|
<td>0.7</td> |
|
<td>1590</td> |
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<td>1.4</td> |
|
<td>793</td> |
|
<td>0.7</td> |
|
<td>1549</td> |
|
<td>0.7</td> |
|
<td>1509</td> |
|
<td>5.4</td> |
|
<td>201</td> |
|
<td>5.5</td> |
|
<td>198</td> |
|
<td>2.9</td> |
|
<td>381</td> |
|
<td>14.0</td> |
|
<td>78</td> |
|
</tr> |
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</tbody> |
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</table> |
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|
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**Use case profiles: prompt tokens / generation tokens |
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|
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**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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|
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|
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### Multi-stream asynchronous performance (measured with vLLM version 0.7.2) |
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<table> |
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<thead> |
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<tr> |
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<th></th> |
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<th></th> |
|
<th></th> |
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<th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th> |
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<th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th> |
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<th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th> |
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<th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th> |
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<th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th> |
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<th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th> |
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<th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th> |
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<th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th> |
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</tr> |
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<tr> |
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<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-7B</th> |
|
<td>---</td> |
|
<td>14.9</td> |
|
<td>67138</td> |
|
<td>7.1</td> |
|
<td>32094</td> |
|
<td>7.4</td> |
|
<td>33096</td> |
|
<td>5.9</td> |
|
<td>26480</td> |
|
<td>2.0</td> |
|
<td>9004</td> |
|
<td>1.5</td> |
|
<td>6639</td> |
|
<td>1.1</td> |
|
<td>4938</td> |
|
<td>0.3</td> |
|
<td>1151</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w8a8</th> |
|
<td>1.36</td> |
|
<td>20.2</td> |
|
<td>90956</td> |
|
<td>8.8</td> |
|
<td>39786</td> |
|
<td>10.2</td> |
|
<td>45963</td> |
|
<td>8.1</td> |
|
<td>36596</td> |
|
<td>3.1</td> |
|
<td>13968</td> |
|
<td>2.1</td> |
|
<td>9629</td> |
|
<td>1.4</td> |
|
<td>6374</td> |
|
<td>0.3</td> |
|
<td>1429</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16</th> |
|
<td>1.00</td> |
|
<td>13.3</td> |
|
<td>59681</td> |
|
<td>6.1</td> |
|
<td>27633</td> |
|
<td>5.9</td> |
|
<td>26689</td> |
|
<td>4.7</td> |
|
<td>20944</td> |
|
<td>2.9</td> |
|
<td>13108</td> |
|
<td>1.9</td> |
|
<td>8355</td> |
|
<td>1.0</td> |
|
<td>4362</td> |
|
<td>0.3</td> |
|
<td>1170</td> |
|
</tr> |
|
<tr> |
|
<th rowspan="3" valign="top">A100x1</th> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-7B</th> |
|
<td>---</td> |
|
<td>26.4</td> |
|
<td>53073</td> |
|
<td>13.0</td> |
|
<td>26213</td> |
|
<td>14.5</td> |
|
<td>29110</td> |
|
<td>11.4</td> |
|
<td>22936</td> |
|
<td>4.4</td> |
|
<td>8749</td> |
|
<td>3.3</td> |
|
<td>6680</td> |
|
<td>2.3</td> |
|
<td>4634</td> |
|
<td>0.5</td> |
|
<td>1105</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w8a8</th> |
|
<td>1.27</td> |
|
<td>34.3</td> |
|
<td>69009</td> |
|
<td>14.8</td> |
|
<td>29791</td> |
|
<td>19.0</td> |
|
<td>38214</td> |
|
<td>15.7</td> |
|
<td>31598</td> |
|
<td>5.6</td> |
|
<td>11186</td> |
|
<td>4.2</td> |
|
<td>8350</td> |
|
<td>3.0</td> |
|
<td>6020</td> |
|
<td>0.7</td> |
|
<td>1328</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16</th> |
|
<td>0.93</td> |
|
<td>23.9</td> |
|
<td>47993</td> |
|
<td>12.0</td> |
|
<td>24194</td> |
|
<td>12.5</td> |
|
<td>25239</td> |
|
<td>10.0</td> |
|
<td>20029</td> |
|
<td>4.5</td> |
|
<td>9055</td> |
|
<td>3.3</td> |
|
<td>6681</td> |
|
<td>2.1</td> |
|
<td>4156</td> |
|
<td>0.5</td> |
|
<td>1043</td> |
|
</tr> |
|
<tr> |
|
<th rowspan="3" valign="top">H100x1</th> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-7B</th> |
|
<td>---</td> |
|
<td>54.3</td> |
|
<td>59410</td> |
|
<td>26.0</td> |
|
<td>28440</td> |
|
<td>32.1</td> |
|
<td>35154</td> |
|
<td>26.7</td> |
|
<td>29190</td> |
|
<td>8.0</td> |
|
<td>8700</td> |
|
<td>6.6</td> |
|
<td>7275</td> |
|
<td>5.2</td> |
|
<td>5669</td> |
|
<td>1.2</td> |
|
<td>1266</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-FP8-dynamic</th> |
|
<td>1.16</td> |
|
<td>62.9</td> |
|
<td>68818</td> |
|
<td>30.3</td> |
|
<td>33196</td> |
|
<td>39.4</td> |
|
<td>43132</td> |
|
<td>31.1</td> |
|
<td>34073</td> |
|
<td>9.2</td> |
|
<td>10058</td> |
|
<td>7.1</td> |
|
<td>7748</td> |
|
<td>6.1</td> |
|
<td>6714</td> |
|
<td>1.3</td> |
|
<td>1415</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16</th> |
|
<td>1.02</td> |
|
<td>56.2</td> |
|
<td>61483</td> |
|
<td>26.7</td> |
|
<td>29243</td> |
|
<td>32.5</td> |
|
<td>35592</td> |
|
<td>26.9</td> |
|
<td>29461</td> |
|
<td>8.3</td> |
|
<td>9072</td> |
|
<td>6.4</td> |
|
<td>7027</td> |
|
<td>5.2</td> |
|
<td>5731</td> |
|
<td>1.2</td> |
|
<td>1291</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). |
|
|
