#端侧

我们承诺过开源 Qwen3.8 的模型权重。现在，是时候见面了！🎉 ⚡ Qwen3.8-27B： - 一款原生多模态稠密模型。仅凭 27B 参数，综合表现超越 Qwen3.7-Plus，在真实世界的编码与办公工作流中尤为亮眼。 - 原生支持 262K 上下文，可通过 YaRN 轻松扩展至 1M tokens。 - 为开发者而生。高效、高质量，并采用 Apache 2.0 许可证。 🚀 Qwen3.8-2.4T-A95B（Max 级别）的开放权重也已于近日发布。 无论你是想用 Qwen3.8-27B 在本地部署轻量级应用，还是用 Qwen3.8-2.4T-A95B 构建智能体，它们现在都属于你了！ 下载、部署，去构建我们尚未想象到的东西吧。👀👇 - Hugging Face： https://huggingface.co/collections/Qwen/qwen38 - ModelScope： https://www.modelscope.cn/collections/Qwen/Qwen38

ChatGPT can now remember your activity across the apps and websites on your computer. With Computer History in the desktop app, future interactions feel more personalized and require less explanation.

Introducing NVIDIA Nemotron 3.5 Lightning⚡ An open 30B MoE model with 3B active parameters, built for always-on agents to complete high-volume, specialized tasks faster. It delivers up to 4x the output speed of similar-sized models.

The best of Gemini is right at the heart of the new Pixel 11 phones and the new Pixel Watch 5. We call it Gemini Intelligence. It’s no longer just an operating system that helps you use your phone, it’s an intelligence system that gets things done for you.

Introducing NVIDIA Nemotron 3.5 Lightning⚡ An open 30B MoE model with 3B active parameters, built for always-on agents to complete high-volume, specialized tasks faster. It delivers up to 4x the output speed of similar-sized models.

Now in preview: The ChatGPT desktop app for Linux. Use ChatGPT, ChatGPT Work, and Codex where you already work and build, with your projects and browser workflows on supported Linux systems.

ive been playing with @AntLingAGI's ling 3.0 flash 124b for a week now, and i need to publish some numbers and performance i found valuable from this lab. a week ago the weights dropped. since then, on one dgx spark, i benched the community quant ladder, ran every official quant to ground, took the official int4 from doesn't run to the fastest single dgx spark path, watched the model write working software in one shot through an agent loop, and served its full 256K context. everything measured the same way and public, nothing tuned to flatter anyone. the verdict: ling 3.0 flash earned a permanent seat on my box. it's the fastest 124B class path i've measured on a single spark, 38.7 tok/s on the official int4 recipe, 35.2 on the community gguf, 2.4x what deepseek v4 flash does on the same hardware, and it writes correct code on the first pass often enough to feel like a different class of local model. dive with me into the full week of receipts, quant by quant, curve by curve 👇

Today, we’re excited to open-source TwiL-LM3, the first formal reasoning model from the webAI Intelligence Lab. At just 3 billion parameters, TwiL-LM3 outperforms OpenAI’s GPT-OSS-120B on 4 of 5 formal reasoning benchmarks while running efficiently on consumer hardware. That’s 40× fewer parameters, 2.6× faster inference, and state-of-the-art performance in the reasoning tasks that power reliable tool calling, code generation, structured outputs, and AI agents. TwiL-LM3 was trained using webAI’s proprietary reasoning pipeline on webAI-owned, verified datasets—not scraped internet data. We believe better reasoning comes from better training pipelines and higher-quality data, not simply larger models. Our approach demonstrates that efficient models can rival—and in many cases surpass—models dozens of times their size. Designed for the edge, TwiL-LM3 runs on hardware people already own—from a Raspberry Pi to an iPhone—bringing advanced reasoning to millions of devices without relying on the cloud. This is our first open-source release from the webAI Intelligence Lab, and it’s only the beginning. Proudly built in Austin, Texas. Article: https://www.webai.com/blog/webai-releases-twil-lm-a-family-of-formal-logic-models-that-outreason-a-120b-model-and-run-on-an-iphone