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--- |
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license: mit |
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tags: |
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- deepseek |
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- int4 |
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- vllm |
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- llmcompressor |
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B |
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library_name: transformers |
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--- |
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# DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 |
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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:** INT4 |
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- **Release Date:** 2/4/2025 |
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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-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B). |
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### Model Optimizations |
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This model was obtained by quantizing the weights of [DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) to INT4 data type. |
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This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. |
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Only the weights of the linear operators within transformers blocks are quantized. |
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Weights are quantized using a symmetric per-group scheme, with group size 128. |
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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. |
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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-14B-quantized.w4a16" |
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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.modifiers.smoothquant import SmoothQuantModifier |
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from llmcompressor.transformers import oneshot |
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# Load model |
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model_stub = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B" |
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model_name = model_stub.split("/")[-1] |
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num_samples = 2048 |
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max_seq_len = 8192 |
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tokenizer = AutoTokenizer.from_pretrained(model_stub) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_stub, |
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device_map="auto", |
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torch_dtype="auto", |
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) |
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def preprocess_fn(example): |
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return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)} |
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ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train") |
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ds = ds.map(preprocess_fn) |
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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="W4A16", |
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ignore=["lm_head"], |
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dampening_frac=0.01, |
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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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dataset=ds, |
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recipe=recipe, |
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max_seq_length=max_seq_len, |
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num_calibration_samples=num_samples, |
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) |
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# Save to disk in compressed-tensors format |
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save_path = model_name + "-quantized.w4a16 |
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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-14B-quantized.w4a16",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-14B-quantized.w4a16",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-14B</th> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16</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>58.79</td> |
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<td>58.28</td> |
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<td>99.1%</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>87.04</td> |
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<td>87.34</td> |
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<td>100.4%</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>81.51</td> |
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<td>80.42</td> |
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<td>98.7%</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>74.46</td> |
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<td>73.32</td> |
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<td>98.5%</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>54.77</td> |
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<td>55.29</td> |
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<td>101.0%</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>69.38</td> |
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<td>70.48</td> |
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<td>101.6%</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>70.99</b></td> |
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<td><b>70.85</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>43.05</td> |
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<td>34.90</td> |
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<td>81.1%</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>47.16</td> |
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<td>45.36</td> |
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<td>96.2%</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>35.07</td> |
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<td>34.90</td> |
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<td>99.5%</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>45.14</td> |
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<td>44.20</td> |
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<td>97.9%</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>34.86</td> |
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<td>35.09</td> |
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<td>100.7%</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>34.21</b></td> |
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<td><b>32.41</b></td> |
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<td><b>94.7%</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>78.90</td> |
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<td>79.00</td> |
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<td><b>100.1%</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>89.80</td> |
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<td>89.70</td> |
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<td>99.9%</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>72.60</td> |
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<td>72.80</td> |
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<td>100.3%</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>84.90</td> |
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<td>84.00</td> |
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<td>98.8%</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 2.8x speedup in single-stream deployment and up to 1.4x 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-14B-quantized.w4a16 --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-14B</th> |
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<td>---</td> |
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<td>5.4</td> |
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<td>837</td> |
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<td>10.7</td> |
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<td>419</td> |
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<td>5.5</td> |
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<td>813</td> |
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<td>5.6</td> |
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<td>805</td> |
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<td>42.2</td> |
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<td>107</td> |
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<td>42.8</td> |
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<td>105</td> |
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<td>22.9</td> |
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<td>197</td> |
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<td>71.7</td> |
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<td>63</td> |
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</tr> |
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<tr> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8</th> |
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<td>1.59</td> |
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<td>3.3</td> |
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<td>1345</td> |
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<td>6.7</td> |
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<td>673</td> |
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<td>3.4</td> |
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<td>1315</td> |
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<td>3.5</td> |
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<td>1296</td> |
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<td>26.5</td> |
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<td>170</td> |
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<td>26.8</td> |
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<td>168</td> |
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<td>14.5</td> |
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<td>310</td> |
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<td>48.3</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-14B-quantized.w4a16</th> |
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<td>2.51</td> |
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<td>2.0</td> |
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<td>2275</td> |
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<td>4.0</td> |
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<td>1127</td> |
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<td>2.2</td> |
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<td>2072</td> |
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<td>2.3</td> |
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<td>1945</td> |
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<td>15.3</td> |
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<td>294</td> |
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<td>15.9</td> |
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<td>283</td> |
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<td>9.9</td> |
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<td>456</td> |
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<td>36.6</td> |
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<td>123</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-14B</th> |
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<td>---</td> |
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<td>2.6</td> |
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<td>765</td> |
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<td>5.2</td> |
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<td>383</td> |
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<td>2.7</td> |
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<td>746</td> |
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<td>2.7</td> |
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<td>732</td> |
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<td>20.8</td> |
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<td>97</td> |
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<td>21.2</td> |
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<td>95</td> |
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<td>11.3</td> |
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<td>179</td> |
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<td>36.7</td> |
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<td>55</td> |
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</tr> |
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<tr> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8</th> |
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<td>1.34</td> |
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<td>1.9</td> |
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<td>1072</td> |
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<td>3.8</td> |
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<td>533</td> |
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<td>1.9</td> |
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<td>1045</td> |
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<td>1.9</td> |
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<td>1032</td> |
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<td>14.8</td> |
|
<td>136</td> |
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<td>15.2</td> |
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<td>132</td> |
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<td>8.1</td> |
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<td>248</td> |
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<td>39.6</td> |
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<td>51</td> |
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</tr> |
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<tr> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16</th> |
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<td>1.93</td> |
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<td>1.2</td> |
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<td>1627</td> |
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<td>2.5</td> |
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<td>810</td> |
|
<td>1.3</td> |
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<td>1530</td> |
|
<td>1.4</td> |
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<td>1474</td> |
|
<td>9.7</td> |
|
<td>208</td> |
|
<td>10.2</td> |
|
<td>197</td> |
|
<td>5.8</td> |
|
<td>348</td> |
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<td>37.6</td> |
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<td>53</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-14B</th> |
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<td>---</td> |
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<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> |
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</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-FP8-dynamic</th> |
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<td>1.33</td> |
|
<td>1.2</td> |
|
<td>925</td> |
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<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> |
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</tr> |
|
<tr> |
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<th>neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16</th> |
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<td>1.37</td> |
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<td>1.2</td> |
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<td>944</td> |
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<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> |
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</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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**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> |
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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> |
|
<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). |
