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It achieves an average score of 88.98 on the HumanEval+ benchmark, whereas the unquantized model achieves 87.63. This model was obtained by quantizing the weights and activations of DeepSeek-Coder-V2-Instruct to FP8 data type, ready for inference with vLLM >= 0.5.2. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. In particular, this model can now be loaded and evaluated with only 4xH100 GPUs, as opposed to 8. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. AutoFP8 is used for quantization with 512 sequences of UltraChat.

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- **Model Architecture:** DeepSeek-Coder-V2-Lite-Instruct - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to Meta-Llama-3-7B-Instruct, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 7/18/2024 - **Version:** 1.0 - **License(s):** deepseek-license - **Model Developers:** Neural Magic Quantized version of DeepSeek-Coder-V2-Lite-Instruct. It achieves an average score of 79.60 on the HumanEval+ benchmark, whereas the unquantized model achieves 79.33.

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This model was obtained by quantizing the weights of DeepSeek-R1-Distill-Qwen-32B to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. Category Metric deepseek-ai/DeepSeek-R1-Distill-Qwen-32B neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16 Recovery

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- **Model Architecture:** DeepSeek-V2.5-1210 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 3/1/2025 - **Version:** 1.0 - **Model Developers:** Neural Magic Quantized version of DeepSeek-V2.5-1210. It achieves an average score of 77.8 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 77.82. This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM >= 0.5.2. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. The weights and activations of the linear operators within transformers blocks are quantized, except the MLP routers.

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This model is an FP8 quantized version of google/diffusiongemma-26B-A4B-it. The model has both weights and activations quantized to FP8 using vllm/llm-compressor and in the compressed-tensors format. It was evaluated on several tasks to assess its quality in comparison to the unquantized model using vLLM. The following metrics were generated when serving the quantized model with vLLM on a single B200 GPU.

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This model is an NVFP4 quantized version of google/diffusiongemma-26B-A4B-it. The model has both weights and activations quantized to NVFP4 using vllm/llm-compressor and in the compressed-tensors format. It was evaluated on several tasks to assess its quality in comparison to the unquantized model using vLLM. The following metrics were generated when serving the quantized model with vLLM on a single B200 GPU.

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

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选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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