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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-32B |
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library_name: transformers |
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--- |
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# DeepSeek-R1-Distill-Qwen-32B-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-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B). |
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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-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) 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-32B-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-32B" |
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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-32B-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-32B-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-32B</th> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-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="4"><b>Reasoning</b></td> |
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<td>AIME 2024 (pass@1)</td> |
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<td>69.75</td> |
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<td>68.5</td> |
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<td>98.21%</td> |
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</tr> |
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<tr> |
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<td>MATH-500 (pass@1)</td> |
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<td>95.09</td> |
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<td>95.26</td> |
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<td>100.18%</td> |
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</tr> |
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<tr> |
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<td>GPQA Diamond (pass@1)</td> |
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<td>64.05</td> |
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<td>62.88</td> |
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<td>98.17%</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>76.3</b></td> |
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<td><b>75.55</b></td> |
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<td><b>99.02%</b></td> |
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</tr> |
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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>64.59</td> |
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<td>64.42</td> |
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<td>99.7%</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>82.71</td> |
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<td>82.64</td> |
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<td>99.9%</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>83.80</td> |
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<td>83.77</td> |
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<td>100.0%</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>81.12</td> |
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<td>80.98</td> |
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<td>99.8%</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>58.41</td> |
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<td>58.30</td> |
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<td>99.8%</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>76.40</td> |
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<td>76.09</td> |
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<td>99.6%</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>74.51</b></td> |
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<td><b>74.36</b></td> |
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<td><b>99.8%</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>42.87</td> |
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<td>49.43</td> |
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<td>99.2%</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>57.96</td> |
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<td>58.38</td> |
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<td>100.7%</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>26.95</td> |
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<td>26.86</td> |
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<td>99.7%</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>43.95</td> |
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<td>44.22</td> |
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<td>100.6%</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>49.82</td> |
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<td>49.43</td> |
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<td>99.2%</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>36.92</b></td> |
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<td><b>36.86</b></td> |
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<td><b>99.8%</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>86.00</td> |
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<td>85.20</td> |
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<td><b>99.1%</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>92.50</td> |
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<td>92.20</td> |
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<td>99.7%</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>82.00</td> |
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<td>80.90</td> |
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<td>98.7%</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>88.70</td> |
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<td>88.70</td> |
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<td>100.0%</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 1.5x 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 0.7.2, 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-32B-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></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>GPU class</th> |
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<th>Number of GPUs</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">A6000</th> |
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<td>2</td> |
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<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th> |
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<td>---</td> |
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<td>6.3</td> |
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<td>359</td> |
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<td>12.8</td> |
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<td>176</td> |
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<td>6.5</td> |
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<td>347</td> |
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<td>6.6</td> |
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<td>342</td> |
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<td>49.9</td> |
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<td>45</td> |
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<td>50.8</td> |
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<td>44</td> |
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<td>26.6</td> |
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<td>85</td> |
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<td>83.4</td> |
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<td>27</td> |
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</tr> |
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<tr> |
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<td>1</td> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w8a8</th> |
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<td>1.81</td> |
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<td>6.9</td> |
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<td>648</td> |
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<td>13.8</td> |
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<td>325</td> |
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<td>7.2</td> |
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<td>629</td> |
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<td>7.2</td> |
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<td>622</td> |
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<td>54.8</td> |
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<td>82</td> |
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<td>55.6</td> |
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<td>81</td> |
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<td>30.0</td> |
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<td>150</td> |
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<td>94.8</td> |
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<td>47</td> |
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</tr> |
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<tr> |
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<td>1</td> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th> |
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<td>3.07</td> |
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<td>3.9</td> |
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<td>1168</td> |
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<td>7.8</td> |
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<td>580</td> |
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<td>4.3</td> |
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<td>1041</td> |
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<td>4.6</td> |
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<td>975</td> |
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<td>29.7</td> |
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<td>151</td> |
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<td>30.9</td> |
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<td>146</td> |
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<td>19.3</td> |
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<td>233</td> |
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<td>61.4</td> |
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<td>73</td> |
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</tr> |
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<tr> |
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<th rowspan="3" valign="top">A100</th> |
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<td>1</td> |
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<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th> |
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<td>---</td> |
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<td>5.6</td> |
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<td>361</td> |
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<td>11.1</td> |
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<td>180</td> |
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<td>5.7</td> |
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<td>350</td> |
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<td>5.8</td> |
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<td>347</td> |
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<td>44.0</td> |
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<td>46</td> |
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<td>44.7</td> |
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<td>45</td> |
|
<td>23.6</td> |
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<td>85</td> |
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<td>73.7</td> |
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<td>27</td> |
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</tr> |
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<tr> |
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<td>1</td> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w8a8</th> |
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<td>1.50</td> |
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<td>3.7</td> |
|
<td>547</td> |
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<td>7.3</td> |
|
<td>275</td> |
|
<td>3.8</td> |
|
<td>536</td> |
|
<td>3.8</td> |
|
<td>528</td> |
|
<td>29.0</td> |
|
<td>69</td> |
|
<td>29.5</td> |
|
<td>68</td> |
|
<td>15.7</td> |
|
<td>128</td> |
|
<td>53.1</td> |
|
<td>38</td> |
|
</tr> |
|
<tr> |
|
<td>1</td> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th> |
|
<td>2.30</td> |
|
<td>2.2</td> |
|
<td>894</td> |
|
<td>4.5</td> |
|
<td>449</td> |
|
<td>2.4</td> |
|
<td>831</td> |
|
<td>2.5</td> |
|
<td>798</td> |
|
<td>17.4</td> |
|
<td>116</td> |
|
<td>18.0</td> |
|
<td>112</td> |
|
<td>10.5</td> |
|
<td>191</td> |
|
<td>49.5</td> |
|
<td>41</td> |
|
</tr> |
|
<tr> |
|
<th rowspan="3" valign="top">H100</th> |
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<td>1</td> |
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<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th> |
|
<td>---</td> |
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<td>3.3</td> |
|
<td>327</td> |
|
<td>6.7</td> |
|
<td>163</td> |
|
<td>3.4</td> |
|
<td>320</td> |
|
<td>3.4</td> |
|
<td>317</td> |
|
<td>26.6</td> |
|
<td>41</td> |
|
<td>26.9</td> |
|
<td>41</td> |
|
<td>14.3</td> |
|
<td>77</td> |
|
<td>47.8</td> |
|
<td>23</td> |
|
</tr> |
|
<tr> |
|
<td>1</td> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-FP8-dynamic</th> |
|
<td>1.52</td> |
|
<td>2.2</td> |
|
<td>503</td> |
|
<td>4.3</td> |
|
<td>252</td> |
|
<td>2.2</td> |
|
<td>490</td> |
|
<td>2.3</td> |
|
<td>485</td> |
|
<td>17.3</td> |
|
<td>63</td> |
|
<td>17.5</td> |
|
<td>63</td> |
|
<td>9.5</td> |
|
<td>116</td> |
|
<td>33.4</td> |
|
<td>33</td> |
|
</tr> |
|
<tr> |
|
<td>1</td> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th> |
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<td>1.61</td> |
|
<td>2.1</td> |
|
<td>532</td> |
|
<td>4.1</td> |
|
<td>268</td> |
|
<td>2.1</td> |
|
<td>516</td> |
|
<td>2.1</td> |
|
<td>513</td> |
|
<td>16.1</td> |
|
<td>68</td> |
|
<td>16.5</td> |
|
<td>66</td> |
|
<td>9.1</td> |
|
<td>120</td> |
|
<td>31.9</td> |
|
<td>34</td> |
|
</tr> |
|
</tbody> |
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</table> |
|
|
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**Use case profiles: prompt tokens / generation tokens |
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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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### Multi-stream asynchronous performance (measured with vLLM version 0.7.2) |
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<table> |
|
<thead> |
|
<tr> |
|
<th></th> |
|
<th></th> |
|
<th></th> |
|
<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> |
|
<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> |
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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> |
|
</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">A6000x2</th> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th> |
|
<td>---</td> |
|
<td>6.2</td> |
|
<td>13940</td> |
|
<td>1.9</td> |
|
<td>4348</td> |
|
<td>2.7</td> |
|
<td>6153</td> |
|
<td>2.1</td> |
|
<td>4778</td> |
|
<td>0.6</td> |
|
<td>1382</td> |
|
<td>0.4</td> |
|
<td>930</td> |
|
<td>0.3</td> |
|
<td>685</td> |
|
<td>0.1</td> |
|
<td>124</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w8a8</th> |
|
<td>1.80</td> |
|
<td>8.7</td> |
|
<td>19492</td> |
|
<td>4.2</td> |
|
<td>9474</td> |
|
<td>4.1</td> |
|
<td>9290</td> |
|
<td>3.0</td> |
|
<td>6802</td> |
|
<td>1.2</td> |
|
<td>2734</td> |
|
<td>0.9</td> |
|
<td>1962</td> |
|
<td>0.5</td> |
|
<td>1177</td> |
|
<td>0.1</td> |
|
<td>254</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th> |
|
<td>1.30</td> |
|
<td>5.9</td> |
|
<td>13366</td> |
|
<td>2.5</td> |
|
<td>5733</td> |
|
<td>2.4</td> |
|
<td>5409</td> |
|
<td>1.6</td> |
|
<td>3525</td> |
|
<td>1.2</td> |
|
<td>2757</td> |
|
<td>0.7</td> |
|
<td>1663</td> |
|
<td>0.3</td> |
|
<td>676</td> |
|
<td>0.1</td> |
|
<td>214</td> |
|
</tr> |
|
<tr> |
|
<th rowspan="3" valign="top">A100x2</th> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th> |
|
<td>---</td> |
|
<td>12.9</td> |
|
<td>13016</td> |
|
<td>5.8</td> |
|
<td>5848</td> |
|
<td>6.3</td> |
|
<td>6348</td> |
|
<td>5.1</td> |
|
<td>5146</td> |
|
<td>2.0</td> |
|
<td>1988</td> |
|
<td>1.5</td> |
|
<td>1463</td> |
|
<td>0.9</td> |
|
<td>869</td> |
|
<td>0.2</td> |
|
<td>192</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w8a8</th> |
|
<td>1.52</td> |
|
<td>21.4</td> |
|
<td>21479</td> |
|
<td>8.9</td> |
|
<td>8948</td> |
|
<td>10.6</td> |
|
<td>10611</td> |
|
<td>8.2</td> |
|
<td>8197</td> |
|
<td>3.0</td> |
|
<td>3018</td> |
|
<td>2.0</td> |
|
<td>2054</td> |
|
<td>1.2</td> |
|
<td>1241</td> |
|
<td>0.3</td> |
|
<td>264</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th> |
|
<td>1.09</td> |
|
<td>13.5</td> |
|
<td>13568</td> |
|
<td>6.5</td> |
|
<td>6509</td> |
|
<td>6.0</td> |
|
<td>6075</td> |
|
<td>4.7</td> |
|
<td>4754</td> |
|
<td>2.8</td> |
|
<td>2790</td> |
|
<td>1.6</td> |
|
<td>1651</td> |
|
<td>0.9</td> |
|
<td>862</td> |
|
<td>0.2</td> |
|
<td>225</td> |
|
</tr> |
|
<tr> |
|
<th rowspan="3" valign="top">H100x2</th> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th> |
|
<td>---</td> |
|
<td>25.5</td> |
|
<td>14392</td> |
|
<td>12.5</td> |
|
<td>7035</td> |
|
<td>14.0</td> |
|
<td>7877</td> |
|
<td>11.3</td> |
|
<td>6364</td> |
|
<td>3.6</td> |
|
<td>2041</td> |
|
<td>2.7</td> |
|
<td>1549</td> |
|
<td>1.9</td> |
|
<td>1057</td> |
|
<td>0.4</td> |
|
<td>200</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-FP8-dynamic</th> |
|
<td>1.46</td> |
|
<td>46.7</td> |
|
<td>25538</td> |
|
<td>20.3</td> |
|
<td>11082</td> |
|
<td>23.3</td> |
|
<td>12728</td> |
|
<td>18.4</td> |
|
<td>10049</td> |
|
<td>5.3</td> |
|
<td>2881</td> |
|
<td>3.7</td> |
|
<td>2097</td> |
|
<td>2.6</td> |
|
<td>1445</td> |
|
<td>0.5</td> |
|
<td>256</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th> |
|
<td>1.23</td> |
|
<td>36.9</td> |
|
<td>20172</td> |
|
<td>17.4</td> |
|
<td>9500</td> |
|
<td>18.0</td> |
|
<td>9822</td> |
|
<td>14.2</td> |
|
<td>7755</td> |
|
<td>5.3</td> |
|
<td>2900</td> |
|
<td>3.3</td> |
|
<td>1867</td> |
|
<td>2.3</td> |
|
<td>1265</td> |
|
<td>0.4</td> |
|
<td>241</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). |
