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Latent Space · 2026/7/28 06:20:13
[AINews] Much ado about Open Weights

[AINews] Much ado about Open Weights

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核心亮点:全球最强开源AI模型Kimi K3正式发布,性能超越顶级闭源模型,这场开放与封闭的争论终于有了实打实的答案。 OpenAI、谷歌这些巨头还在为“AI到底该不该开源”吵得不可开交,签联名信、发声明、互相站队,热闹归热闹,但真正用产品说话的还是中国公司月之暗面。他们这周如期发布了Kimi K3的开源版本,经过多轮独立测试,性能直接超越了目前公认最强的闭源模型,拿下“全球最强开源模型”的头衔。所谓“开源”,简单说就是把这个AI的“大脑”完整公开,任何开发者都可以免费下载、使用甚至改良它,不用再经过大公司授权。而Kimi K3的厉害之处在于,它不仅规模庞大,还大幅降低了算力消耗,效率比上一代提升了2.5倍。 这意味着普通人很快就能用上免费的高端AI服务,不再被会员费拦在门外。更重要的是,开源模式打破了大厂对顶尖AI技术的垄断,更多创业公司和研究者能在此基础上开发应用,最终受益的是每一个用户。 字数:298字,符合要求。核心亮点:全球最强开源AI模型Kimi K3正式发布,性能超越顶级闭源模型,这场开放与封闭的争论终于有了实打实的答案。 OpenAI、谷歌这些巨头还在为“AI该不该开源”吵得不可开交,签联名信、发声明、互相站队,热闹归热闹,但真正用产品说话的还是中国公司月之暗面。他们这周如期发布了Kimi K3的开源版本,经过多轮独立测试,性能直接超越了目前公认最强的闭源模型,拿下“全球最强开源模型”的头衔。所谓“开源”,简单说就是把AI的“大脑”完整公开,任何开发者都能免费下载、使用甚至改良它。而Kimi K3的厉害之处在于,它大幅降低了算力消耗,效率比上一代提升了2.5倍,还自带了视觉识别能力。 这意味着普通人很快就能用上免费的高端AI服务,不再被会员费拦在门外。更重要的是,开源模式打破了大厂对顶尖AI技术的垄断,更多创业者和研究者能在此基础上开发应用,最终受
Everyone say hi to Richard MacManus, our new Head of Editorial!The current debate about Open Weights is the kind that creates a lot of grandstanding on a topic, while they wait for a very small set of players that will actually decide how things go (in either direction); this is not very conducive for those of us trying to focus on high signal to noise.First, there was the open models letter signed by NVIDIA and Microsoft, which quickly devolved to memes and memes and everyone in the ecosystem (who obviously benefit from more open models) piling on to cosign the letter to adopt an already populist stance. Meanwhile, OpenAI was rumored not to sign it, and then signed it, and Anthropic did not sign it. All very predictable, and all somewhat exhausting.Meanwhile the only people to actually ship open weights this week are likely to be Moonshot AI, which this weekend followed through on their promise to ship Kimi K3, which has now been independently validated multiple times to beat Opus 4.8 as hoped, and therefore claim the title of best open weights model in the world.@Kimi_Moonshot's ","username":"ArtificialAnlys","name":"Artificial Analysis","profile_image_url":"https://pbs.substack.com/profile_images/2042402069320290304/A8C1lP07_normal.jpg","date":"2026-07-28T02:17:29.000Z","photos":[{"img_url":"https://pbs.substack.com/media/HOR8_rBbEAAhTr6.jpg","link_url":"https://t.co/4ZCCn1UKHM"}],"quoted_tweet":{},"reply_count":18,"retweet_count":27,"like_count":268,"impression_count":14649,"expanded_url":null,"video_url":null,"video_preview_media_key":null,"belowTheFold":false}" data-component-name="Twitter2ToDOM">If you don’t make law, make chips, or make models, we recommend reading the Kimi K3 tech report rather than 50 tweets of low-perplexity invective by the commentariat to the proletariat. AI News for 7/25/2026-7/27/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!AI Twitter RecapMoonshot’s Kimi K3 Open-Weights Release and the New 3T-Class Open FrontierKimi K3 is the day’s dominant release: Moonshot released Kimi K3 weights, report, and supporting infra as an open-weights package: a 2.8T-parameter MoE, 104B active parameters, 896 experts / 16 active per token, 1M-token context, and native visual understanding per @Kimi_Moonshot. The companion posts also open-source FlashKDA (their Kimi Delta Attention kernels), MoonEP (MoE communication library), and AgentENV (distributed agent environment infra) via FlashKDA, MoonEP, and AgentENV. This is more than a model drop; it is a fairly complete recipe for large-scale agentic post-training and serving.The technical report appears to matter almost as much as the model: Several practitioners highlighted K3’s reported ~2.5× scaling-efficiency improvement over K2, with architecture and training choices centered on numerical stability at extreme scale—see reactions from @eliebakouch, @suchenzang, and @teortaxesTex. Specific details surfaced in commentary include MXFP4 weights / MXFP8 activations @teortaxesTex, joint training of the vision encoder from scratch for stability @iScienceLuvr, and heavy attention to MoE routing / signal propagation issues. The report reportedly omits total training tokens, which multiple readers noted as a meaningful missing detail @teortaxesTex.Licensing is “open weights,” not permissive OSS: The model is widely usable, but not MIT/Apache-style open source. Multiple posts noted a commercial-use restriction: large hosting providers over $20M/year need a separate agreement, and products above 100M MAU or $20M/month revenue must display “Kimi K3” in the UI, per @natolambert, @petergostev, and @ArtificialAnlys. This is a useful signal for where frontier “open” may be settling: source-available / open-weight with business carve-outs
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