DeepSeekV4 与美团 LongCat 同时「破万亿」，释放了哪些信号？

marsbitPublished on 2026-04-30Last updated on 2026-04-30

国内 AI 企业开始尝试铺设自己的轨道。

今年伊始,海外科技圈都在关注中国的算力问题。

1 月,马斯克在播客中称,中国在 AI 算力上「将远超世界其他地区」。2 月,OpenAI 首席执行官奥特曼说,中国在人工智能领域的技术进步 「快得惊人」。英伟达 CEO 黄仁勋也多次公开表示:「限制中国的 AI 技术,反而会加速其自主研发」。

2025 年可以说是供给端的集结之年。摩尔线程、沐曦股份等国产 GPU 接连登陆资本市场,国产大模型的产业基础进一步加深。2026年,变化向产业链下游传导,4 月下旬,多款国产大模型发布新版本。

4 月 20 日,月之暗面推出擅长长程代码编写的 Kimi K2.6 模型;4月 24 日,DeepSeek V4 发布;随后美团 LongCat-2.0-Preview 开放测试,两者总参数规模均突破万亿,且均支持 1M 超长上下文 。

值得一提的是,DeepSeek V4 完成了从英伟达体系向华为昇腾平台的迁移与适配;而美团 LongCat2.0 则是训练推理全程基于国产算力的万亿参数大模型,使用了 5 万至 6 万张国产算力芯片。

长久以来,中国 AI 从业者,普遍策略是搭上已有的成熟方案。现在,国内 AI 企业开始尝试铺设自己的轨道。

在荒野修路

你该如何完成一次艰难的任务呢?

科幻作家阿瑟·克拉克的答案是:「唯一的办法是让不可能本身,成为前进的起点。」

DeepSeek V4 从最初定档到最终发布,时间调整了多次。外部普遍推测,原因之一,就是需要将核心代码从英伟达的 CUDA 迁移出来。

CUDA 生态经过十几年的打磨,已是一个功能强大、工具完备的开发平台。国产算力生态尚在构建初期。迁移代码的过程,意味着开发团队需要做大量底层框架的重构工作。

最终 DeepSeek 做到了,V4 发布两日后,摩根大通在报告中指出,V4 成功适配华为昇腾芯片,验证了国产算力在前沿 AI 推理上的可行性;且 DeepSeek 通过混合注意力架构等底层技术创新,显著降低了推理成本。

DeepSeek 用技术极客的方式降本增效,通过重写半个大模型的工作量完成硬核迁移。同日开放测试的美团 LongCat-2.0-Preview,则是直接跑在国产算力之上。

国产算力在工程层面,有哪些难点?不妨以 LongCat-2.0-Preview 为例看看。

第一个难点,是物理层面的。国产硬件底座的显存容量和带宽与英伟达芯片有差异,训练部署万亿参数模型时,美团团队在工程方面有不小的挑战,需要用更多精力去调试并行策略、优化显存 。

第二难点,是软件生态的成熟度,针对国产芯片的特性,确保训练全程的精确可复现,团队需要重写和优化核心算子,以及自研全确定性的算子。

第三个难点,是万卡集群的稳定性,在动用 5 万-6 万张国产算力卡的超大规模集群上,硬件故障难以避免。为此,团队构建了一套完整的容错与自动恢复体系。

最后,针对国产硬件的特点,团队在训练框架和模型结构进行针对性的亲和设计,打破了通用框架的适配局限,提升了计算性能。

DeepSeek 的算法优化降低了算力的门槛,把模型的价格打了下来;美团的工程实践则证明国产芯片的可行性。这些探索,也给国产芯片生态沉淀出工程能力和经验。

梁文锋曾说:「我们不是有意成为一条鲶鱼,只是不小心成了一条鲶鱼」,而今「鲶鱼效应」已经显现,DeepSeek 并不独行。

从单点到系统

腾讯云的汤道生曾有这样一个比喻:「大模型是发动机,使用者是驾驶员」。使用者很容易注意到发动机的性能,但优秀的驾驶员,会意识到燃料与底盘同样重要。

中国算力的发展,依赖的是整条产业链的协同进步。各个环节的核心企业,都在持续补足短板。

在制造端,公开数据表明,中国芯片产量节节攀升,但却是「哑铃型」结构, 28nm 以上成熟制程占绝对主力,14nm 及以下先进制程产能依然稀缺。

面对 EUV 光刻机缺位的现实,中芯国际、华虹半导体等企业正推进多重曝光等工艺攻关,试图在物理极限中寻找平衡点。多方报道显示,中芯国际的 N+2 工艺(等效7nm)良率已经突破 80%,这意味着已经跨过了商业化量产的门槛。

在算力端,国产芯片在单卡算力上与英伟达仍存在差距。华为昇腾 910C 等产品的实践表明,通过极致的集群线性加速比,也能跑通体量巨大的模型训练。

「得生态者得天下」。英伟达 CUDA 构建的护城河之所以深厚,一个重要原因是形成了普适性的软硬件兼容标准。

行业从业者也意识到这一点。比如寒武纪推出基础软件平台,兼容主流框架,降低开发者的迁移门槛。智源人工智能研究院牵头的开源系统,构建了统一的底层接口,让上层模型可以运行在多种不同的国产芯片上。

国内互联网大厂也有很多动作,百度的双轨战略,字节跳动的千亿投入,都在为算力底座寻找更优解。

据公开数据梳理,在过去几年中,美团至少布局了 21 家覆盖半导体/智能硬件和通用大模型领域的相关公司。其中,既包括芯片算力层的摩尔线程、沐曦股份,以及视觉芯片领域的爱芯元智;也包括新材料等细分赛道的广州众山、东方算芯等多家企业。

在技术长期保持跟进的同时,产业资本也在做算力的投资人和共建者,逐渐形成正向循环。

从数字世界,到现实任务

「当下人工智能正处于第三次浪潮的重要拐点,大模型正推动其从弱人工智能迈向通用人工智能,更关键的是,推动机器人从 1.0 专用机器人时代进入 2.0 通用具身智能时代。」

北京智源人工智能研究院院长王仲远的话,点出 AI 能力的重要落点,是物理世界。

一方面,众多国产厂商正致力于让大模型在云端「读万卷书」,提升模型的智慧、逻辑推理的严密性。另一方面,也要让大模型「行万里路」,比如文心大模型被植入到自动驾驶的决策系统中;混元大模型的工业质检方案,已出现在多个流水线场景。

美团的外卖、到店、酒旅等业务,构成了日常生活中最复杂的任务执行网络。这里有海量的的真实场景:从商家后厨的出餐速度,到骑手在暴雨中的配送路径,再到用户深夜的一句「想吃火锅」。

王兴曾明确提出,要将美团 App 率先升级成「AI-powered App」。这意味着,LongCat 的训练目标不仅是回答「哪家的小炒肉好吃」,更要「找到这家店,选出最佳的团购券,然后预定 2 个周五晚上 7 点钟的座位」。

这意味着任务交付的效果尤为重要,也解释了美团为何强调要打造物理世界的 AI 底座。

从参数提升到算力跑通,国产大模型正在完成从「能用」到「好用」的进阶。

这条路没有捷径。未来,当算法、算力、资金与场景持续产生化学反应,中国 AI 的故事,也将从「单点突破」翻到「系统进化」这一页。

本文来自微信公众号“蓝洞商业”,作者:于玮琳

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