马斯克大赞阿里 AI，9B 参数硬刚 120B，海外网友：这叫小模型？

爱范儿03-03

马斯克又双叒叕下场点赞中国 AI 了。昨天深夜，阿里通义千问团队在 X 平台正式发布了 Qwen3.5 小模型系列，覆盖 0.8B、2B、4B 和 9B 四个参数规格。甫一发布，便在海外科技圈引发强烈反响。马斯克也在该推文下评论称：“Impressive intelligence density”（令人印象深刻的智能密度）。这股热度的背后，APPSO 也好奇，为什么这几款小模型能够激起如此大的波澜...

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HuggingFace 地址：https://huggingface.co/Qwen/Qwen3.5-2B多模态这块也有讲究。Qwen3.5 采用“早期融合”训练机制，文本、图像、视频在底层就一起处理，不是在文本模型上事后挂个视觉编码器。这个差异直接决定了小参数模型在视觉问答、OCR 文档理解等任务上，能打出远超同量级传统轻量多模态模型的表现。全系列同时支持长达 26 万 Token 的上下文窗口，并引入“思考”与“非思考”双模式，可在深度逻辑推理与快速响应之间灵活切换。0.8B 与 2B 模型均为 24 层结构，隐藏维度分别为 1024 和 2048，专为物联网设备等极端边缘场景设计，也可直接在主流手机上原生运行。尽管 0.8B 在语言基准 MMLU-Pro 上得分 29.7，表现平平，但受益于早期融合架构，其在视觉任务 MathVista 上达到 62.2，OCRBench 达到 74.5，展现出与参数量不相称的视觉理解能力。2B 模型的 OCRBench 进一步提升至 84.5，表现亮眼。上下滑动查看更多内容4B 模型为 32 层结构，隐藏维度 2560，能够流畅运行于消费级移动硬件。官方将其定性为“出乎意料强大的多模态轻量智能体底座”。9B 则再进一步，同为 32 层结构，但隐藏维度提升至 4096，FFN 维度扩展至 12288，可以在 Mac 上流畅运行。其 MMLU-Pro 得分达到 82.5，不仅超越了参数量为其三倍的上一代 Qwen3-30B，在视觉任务上更以两位数优势超过 GPT-5-Nano 与 Gemini 2.5 Flash Lite。MMMU-Pro 得分高达 70.1，MathVision 达到 78.9，证明了其在复杂物理和数学图像解析上的实力。官方将 9B 的目标明确为“缩小与前沿大模型的能力差距”，从基准数据来看，这一目标已初步实现。海外开发者对这批模型的实际表现给出了高度正面的评价。有开发者直言，称其为“小模型”不过是低估了它，就好比把飓风叫做微风。另有观点认为，真正值得关注的指标是每十亿参数所能释放的智能密度。如今只需 3 万美元的硬件，就能跑出一年前需要 20 万美元 GPU 配置才有的推理能力。已有开发者演示了在 iPhone 17 Pro 上通过针对 Apple Silicon 优化的 MLX 框架本地运行 Qwen3.5-2B 6-bit 版本，模型可实时完成视觉理解与问答任务。网友在本地 Vision Agents 应用中实时识别手写内容并即时回答问题，并感叹这在几个月前根本无法实现。还有用户这样总结道，9B 模型性能与规模达 120B 的 ChatGPT 开源模型相当，体积却只有其十三分之一，且完全免费，可在任何笔记本电脑乃至手机上运行。知名开源推理工具 Ollama 也迅速跟进，官宣支持 Qwen3.5 全系四个尺寸，并配套提供原生工具调用、思维链推理与多模态功能，只需一行命令即可拉取运行。附上地址：https://ollama.com/library/qwen3.5跑分是起点，AI+硬件才是终局在谈及阿里为何坚持追求开源与发布全尺寸模型时，千问技术负责人林俊旸曾在清华 AGI-Next 峰会上转述其师弟的观点，给出了一个朴素的答案。他表示，小模型起源于内部实验需求，是为了让资源有限的学生也能参与研究。他回忆道，7B 规模的模型已让很多硕士博士生无力承担实验成本，若将 1.8B 的模型开源出去，很多同学就有机会顺利毕业，“这是很好的初心”。平心而论，Qwen 系列一路开源走下来，客观上确实让很多人用上了本来用不起的 AI 模型。而对于普通用户而言，想亲身体验这批最新的小模型其实也不复杂。目前，你可以通过 PocketPal AI 这款手机应用，直接下载并在本地运行 Qwen3.5 系列模型，无需任何 API，模型推理全程在设备端完成。如果不习惯英文界面，可以在设置 (Setting) 里找到语言 (Language) 选项，切换成中文。（具体教程可参考 APPSO 此前的文章：《我在 iPhone 17 Pro Max 上跑 AI 模型，体验了一把十年前“越狱”的快乐》）。不过有一个细节，比选哪个模型更重要：选对量化版本。BF16 是接近原始精度的半精度权重，回答稳定性最好，推理细节与对齐效果最接近原版，但 2B 的 BF16 权重文件就要 4.45GB，运行时还需额外占用内存用于 KV cache 和运行缓冲区，极易触发系统杀后台或直接加载失败。因此手机端的选择逻辑应该是：可用内存长期能剩 6GB 以上，优先选 IQ4_NL；可用内存常在 3GB 至 5GB 之间，优先选 Q3_K_M；可用内存更低，才考虑 Q3_K_S 等等。说到底，一个无法独立运行的模型，不过是一堆权重文件。真正有价值的，是与正确硬件深度绑定、以正确量化格式部署的小模型，那才能成为真正的产品。智能手机时代本质上是“单向输入”的范式，而即将到来的 AI 硬件浪潮，是要以更碎片化、更有粘性的方式接管人类的记忆与生活。小模型，正是给这些硬件注入灵魂的关键。阿里已将 Qwen 小模型嵌入 AI 眼镜等可穿戴硬件，实现毫秒级端侧视觉解析。在真实物理场景中，向眼镜询问前方障碍物时哪怕延迟三秒也会失去全部意义，而这恰恰是云端大模型无法克服的物理瓶颈。你向眼镜询问前方障碍物的时候，哪怕延迟三秒也会失去全部意义，遇到需要深度推理的复杂问题，再作为路由器把请求交给云端的大模型处理。是的，端云协同架构，才是接下来几年计算平台的基本形态。包括在 iPhone 的“视觉智能”中，当用户把摄像头对准餐厅或商品，端侧实时完成场景解析、文本提取，甚至直接唤起购买流程，全程在设备上完成。此外有媒体报道称，苹果下一步还在开发带摄像头的 AirPods 和智能眼镜，这些设备会变成用户的“第二双眼睛和耳朵”。工业方面，IoT 设备、工厂传感器、医疗监测终端，这些场景里数据隐私更敏感，本地推理同样是硬需求。而端侧小模型实时处理第一视角多模态数据，则是绕不过去的基础设施。就像今天没有人会专门写一篇文章夸手机能打电话一样。AI 眼镜、AI 手表、AI 耳机等可穿戴设备，现在听起来还有点新鲜，但在未来，它们也会变得稀松平常。而让这件事成为可能的，恰恰是一批又一批看似没什么存在感的小模型。它们很小，但如无意外，它们将无处不在。附上 HuggingFace 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