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想买Mac运行大模型? 这是劝退贴 其实估算方法很简单, 现在买 MacStudio 哪怕运行 Qwen3.6-27B 4bit 量化版本, 然后开 DFlash 使用Qwen的内置投机解码, 也就飙到 65token/s. 而现在普遍大模型都能跑到 40 token/s. 如果专门买 MacStudio M3 Ultra 96G 运行大模型, 如果把设备售价 (32999) 换算成使用API, 以 GLM-5.2 为例, 每百万token 28块, 一台 MacStudio 的价格大概能买到 32999/28 = 1178M token. 而为了输出这些token, 买到的 MacStudio 运行 Qwen3.6-27B 要持续运行 209天. 也就是说回本周期至少是200天不间断运行. 然后运行模型才是纯赚. 这还是没算电费和不直接买API而是买套餐的情况.而且, 最重要的是这还是在运行一个只有27B的小模型. 如果真的买512G的 MacStudio (108749, 而且好像已经断货了), 然后运行量化版本的 GLM-5.2, 速度就会跌到只有 17 token/s, 回本周期大概在 7 年左右... 对于现在1.5个月模型就发新版本的情况下, 普通用户自用是绝对不划算的. 所以大部分用户买 coding plan 会更划算, 如果像我一样要测新模型, 直接租卡也会比直接买划算很多. 当然, 如果你本身就有Mac或者显卡, 那么空闲的时候(比如睡觉的时候)让它跑大模型运行任务, 反而是划算的. #本地大模型# #mac# #qwen36# #glm52#

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

08-04 AI日报🍁｜阿里 Qwen3.8-Max 重磅发布 今日 AI 圈 5 条要闻，重点看这几条： 1. 阿里发布 Qwen3.8-Max（2.4T 参数旗舰，下周开源权重）； 2. DeepSeek V4 Flash 正式版上线，Agent 能力大幅增强； 3. OpenAI 未发布模型（Astra 相关）在数学上取得 10 项突破； 4. NVIDIA 开源 Nemotron VoiceChat 全双工实时语音模型； 5. 英国光子芯片初创 OLIX 融资 3.12 亿美元。

🔥 Qwen3.8-Max 重磅登陆 作为备受期待的 @Alibaba_Qwen 最新一代 2.4 万亿参数旗舰模型，Qwen3.8-Max 现已无缝接入 0 成本体验顶尖模型，解锁极致生产力！🚀 🎁 专属福利 Buff 叠满，多重好礼一次领够： 1️⃣ 新用户专享：使用 @BinanceWallet、@BitgetWallet 或 @imTokenOfficial 登录，直接领 100 万免费 Credits！ 2️⃣ 充值超级大赠送：笔笔充值享积分返赠（BNB Chain 享 1:1 等额赠送，其他方式 1:0.5），单用户最高可拿 $100 额外奖励！ 3️⃣ 邀请连环赠：好友通过你的链接注册，即领 30 万 Credits（可与钱包登录礼叠加，累计最高可得 130 万 Credits）；邀请人享其充值及订阅返利！ 👉 先到先得，立即来 免费使用：

RAG system that skips HTML parsing entirely! PixelRAG is an open-source visual RAG framework that renders documents as screenshots instead of parsing them into text. Most RAG pipelines start by converting HTML to text. Tables flatten into unstructured rows. Charts disappear. Layout context is gone before the LLM ever sees it. The paper measured this directly: HTML-to-text conversion accounts for 36.6% of retrieval failures on SimpleQA. PixelRAG skips that step entirely. It renders pages as screenshot tiles using Playwright, embeds those tiles with a fine-tuned Qwen3-VL-Embedding model, builds a FAISS index, and passes retrieved images directly to a VLM reader. No text abstraction in between. Benchmarked across six datasets against the strongest text-based baselines: - SimpleQA: 78.8% vs 71.6% (+7.1 points) - NQ-Tables: 48.8% vs 42.5% (+6.3 points) - EVQA: +15.5 points - LiveVQA: +11.3 points One honest caveat from the authors: this requires Qwen3-VL-4B class models or larger to see the benefit. Smaller models trail text retrieval. The authors also recommend using PixelRAG as an enhancement layer alongside existing text systems rather than a full replacement. Ships with a pre-built Wikipedia index covering 8.28M articles across 28.1M screenshot tiles. A Claude Code plugin lets Claude take screenshots of any URL and reason over the visual content directly. Key capabilities: • Renders web pages, PDFs, and images as screenshot tiles via Playwright • Fine-tuned Qwen3-VL-Embedding model for visual retrieval • FAISS index for fast vector search • Pre-built Wikipedia index: 8.28M articles, 28.1M tiles • 3x token cost reduction via image compression • Claude Code plugin for direct URL screenshot and visual reasoning • LoRA fine-tuning support via pixelrag-train 100% open source. I've shared the link in the replies!

好家伙，qwen3.8-max 正式版来了，2.4T 参数、1M 上下文，下周 Max 和 27B 的权重都开源(听说 27B 在本地 17GB 内存就能跑)！！！ 它绝对是非常被低估的模型，视觉理解、前端、复杂任务都是全球第一梯队，API 价格也是旗舰里最便宜的一档。 两周前我复刻 macOS、植物大战僵尸和黄金矿工，当时已经觉得前端能力挺牛逼，，今天我又拿正式版整了个大活： 第一个，我扔给它一张 2D 户型图，让它把房子装修后 3D 展示： 它先把图重画成带尺寸标注的标准户型图，再 1:1 盖成 3D，家具全配好，可以第一人称走进去逛，切到俯视图和图纸完全对得上，连每扇门往哪边开都没错。 做装修、中介的兄弟们想想这个场景，可以玩起来了。 第二个，我让它做一个能开车、逛街、有昼夜循环的 3D 开放世界城市游戏： 它自己选型、写代码，第一版帧率只有 6 帧，它自己查出来是灯光加太多，改了渲染方式拉回 50 帧。 最后交给我的网页版小 GTA，完全达到了我预期的效果，而且细节做的非常完善。 模型越来越强，Coding的想象空间太大了，等后续再给大家很多的测评和反馈，下面的视频展示效果👇：

Minimax开源Minimax H3 33B的视频Transformer + 32B的Qwen3-VL-32B文本与视频编码器，模型文件大小就65G，一般家庭跑不起来，生成768p视频至少A100起。 模型：

Meet Qwen3.8-Max. Your always-on workmate for • Coding • Reasoning • Research • Writing • AI Agents You live your life. It does the work. Try it & API on • Model Studio: • Qwen Cloud: #BuildWithQwen# #AlibabaCloud# #QwenCloud# #ModelStudio#