Qwen3.8-2.4T 人工智能模型
Qwen3.8-2.4T 是一个具有竞争力的性能和多种量化选项以实现高效部署的大型语言模型，支持多种量化选项以实现高效部署。 该项目因其558个星标和127条评论的高人气而具有重要意义，表明社区兴趣浓厚。它解决了高效大型语言模型部署的需求，并具有明确的盈利潜力。 该模型在许可证方面与Kimi k3相似，适用于内部使用或年收入<50M美元的情况。它提供多种量化选项，但q4上缺乏QAT，需要额外的校准
项目链接：https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B
作者：Philpax
发布时间：2026-08-12T15:01:17Z
挖掘日期：2026-08-13
AI 评分：8.0/10
来源：hackernews
标签：LLM, Quantization, Performance, AI, HuggingFace
📌 项目详解
Qwen3.8-2.4T 是一个具有竞争力的性能和多种量化选项以实现高效部署的大型语言模型，支持多种量化选项以实现高效部署。 该项目因其558个星标和127条评论的高人气而具有重要意义，表明社区兴趣浓厚。它解决了高效大型语言模型部署的需求，并具有明确的盈利潜力。 该模型在许可证方面与Kimi k3相似，适用于内部使用或年收入<50M美元的情况。它提供多种量化选项，但q4上缺乏QAT，需要额外的校准。
🌐 背景与生态
大型语言模型（LLMs）正在迅速发展，量化已成为高效部署的关键技术。Qwen3.8-2.4T 在这个领域竞争，提供与其他领先模型相当的性能。
💬 社区讨论
社区评论强调了其与其他模型的竞争力，讨论了量化选项，并指出了在未经额外校准的情况下部署的限制。
🚀 应用前景
Qwen3.8-2.4T 可用于需要大型语言处理行业的客户服务、内容生成和研究。其量化选项使其适用于边缘设备和云平台。
🔧 技术栈
该模型使用PyTorch构建，并支持FP8和BF16等多种量化格式。它与HuggingFace生态系统集成，并需要特定硬件以实现最佳性能。
🎯 上手难度
入门评级为进阶。前提条件包括Python 3.8+、GPU和API密钥。步骤包括克隆仓库、安装依赖项和运行示例脚本。
👥 目标用户
目标用户包括从事大型语言任务的后端工程师、ML实践者和研究人员。它特别适用于需要可扩展和高效语言模型的组织。
⚖️ 类似项目对比
竞品包括DeepSeek V4-Pro和Kimi k3，它们提供类似的性能和量化选项。Qwen3.8-2.4T 通过其开源许可证和更广泛的社区支持脱颖而出。
📚 参考链接
📄 查看原文内容
https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B-FP8
--- Top Comments ---
[NitpickLawyer]: Supposedly this is a Kimi k3 rival. Bit of a chonker, especially since they only released bf16 and fp8. So at launch this will be harder to serve than k3. No QAT on q4 means that someone with deep pockets (nvda?) will have to quant it, with plenty of calibration data. Should bring it ~1.3TB, so around k3 size. License pretty similar to k3 with some caveats. Free to use for internal or <50M$ revenue / year. Limitations above that threshold for serving the model or services targeting co...
[dhx]: Also of interest: DeepSeek V4-Pro-0813 (1.6T-A49B) benchmark scores have apparently just been announced on the DeepSeek WeChat channel and they're sitting about Fable 5 level.[1] [1] https://www.reddit.com/r/LocalLLaMA/comments/1vmi0fg/deepsee...
[guardiangod]: https://unsloth.ai/docs/models/qwen3.8 The 1bit quant model is at an astonishing 397GB with 95B active per MOE. This literally puts Opus 4.5 performance level into a machine a normal person could buy, and still gets usable tokens/second. The full lossless model BF16 is clocking at 4.9TB. The model card claims the model to be between Opus 4.8 and Fable 5. Again that's astonishing as getting a machine with 7TB RAM (with context + KV cache) is still within the...
[edg5000]: Shall we bet on when the hardware needed for this (without quantizing and at good speed) will reach < 10k USD? I'm betting 2040. I can download it now, and then get the hardware later. Eventually we can all have these things running 24/7 in our home if we wanted to. I currently would not have any task for it that would really utilize the hardware 24/7, but maybe in 20 years I will.
[l72]: > In particular, Qwen3.8-Max is the official version based on Qwen3.8-2.4T-A95B with more features, such as vision input & non-thinking support, 1M context length by default, official built-in tools, etc. That is unfortunate, that the open weight model doesn't have vision support or the 1M context length...