美团开源 LongCat-2.5:1.6 万亿参数智能体模型,全程未用 Nvidia GPU 训练
Meituan releases a 1.6-trillion-parameter agent model that never touched an Nvidia GPU
美团把 1.6 万亿参数的 LongCat-2.5 权重直接放出来了,全程用华为昇腾芯片训练,Terminal Bench 拿了 88.4 分,只比 Claude Opus 5 低一点。想知道不用 Nvidia 能不能训出顶级智能体模型的,看看这套真实方案。
美团发布并开源了 LongCat-2.5 的模型权重,这是一款专为 AI 智能体设计的大模型,总参数量达 1.6 万亿,训练全程只使用中国国产算力,未提及任何 Nvidia GPU。在智能体任务基准 Terminal Bench 上,LongCat-2.5 取得 88.4 分,仅落后于 Claude Opus 5。
在部署层面,示例方案运行在 194 颗华为昇腾(Ascend)芯片上,说明该模型从训练到推理都可以完全脱离美系 GPU 生态。这对受出口管制影响、难以采购 Nvidia 高端芯片的中国团队来说,是一条可复制的路线:用国产芯片完成万亿参数模型的训练,同时保持智能体能力处于第一梯队。
从行业格局看,LongCat-2.5 把国产算力的上限推到了与 Claude Opus 5 同一基准量级附近,开发者可以直接下载权重自行部署验证。对于需要在国内合规环境下搭建智能体系统的企业,以及关注芯片替代路线的工程团队,这套完全基于华为昇腾的训练与部署方案具有直接的参考价值。
Meituan releases a 1.6-trillion-parameter agent model that never touched an Nvidia GPU
Meituan has released the weights for LongCat-2.5, a 1.6-trillion-parameter model built for AI agents and trained entirely on Chinese hardware. On Terminal Bench, it scores 88.4 points, landing just behind Claude Opus 5. The sample deployment runs on 194 Huawei Ascend chips, and Meituan makes no mention of Nvidia GPUs. The article Meituan releases a 1.6-trillion-parameter agent model that never touched an Nvidia GPU appeared first on The Decoder .
This report is categorized under AITOP's "Models" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to US-China foundation model benchmarks and commercialization comparison and AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include LongCat-2.5, Meituan, Huawei Ascend, Claude Opus 5.
Source attribution: curated from Decoder (@Jonathan Kemper). Cross-source corroboration: 11 independent sources have covered this development (including rohanpaul_ai, Jerry Liu, TestingCatalog), confirming industry-wide signal strength.