英伟达成美国大模型开源标杆：Nemotron 3连训练配方都公开，10万亿token数据全放出

量子位2025-12-27

英伟达在开源模型上玩的很激进：“最高效的开放模型家族”Nemotron 3，混合Mamba-Transformer MoE架构、NVFP4低精度训练全用上。而且开放得很彻底：不仅开放模型权重，还要把超过10万亿token的训练数据、预训练和后训练软件、训练配方全部公开。与其他开源模型相比性能有竞争力，且速度快1.5-3.3倍。把Mamba和Transformer混着用Nemotron 3在架构层面...

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released","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"SNVD.UK":0.6,"NVDU":0.6,"NVIW.SI":0.6,"NVDS":0.6,"NVD2.UK":0.6,"2NVD.UK":0.6,"NVDY":0.6,"NVDS.UK":0.6,"NVD":0.6,"NVD3.UK":0.6,"3NVD.UK":0.6,"NVDD":0.6,"NVDA":1.97},"content_text":"英伟达在开源模型上玩的很激进：“最高效的开放模型家族”Nemotron 3，混合Mamba-Transformer MoE架构、NVFP4低精度训练全用上。而且开放得很彻底：不仅开放模型权重，还要把超过10万亿token的训练数据、预训练和后训练软件、训练配方全部公开。与其他开源模型相比性能有竞争力，且速度快1.5-3.3倍。把Mamba和Transformer混着用Nemotron 3在架构层面追求推理效率的最大化。传统Transformer的自注意力机制需要对不断增长的KV Cache做线性扫描，序列越长，计算开销越大。英伟达的解决方案是大量使用Mamba-2层替代自注意力层——Mamba层在生成时只需要存储固定大小的状态，不受序列长度影响。以Nano型号为例，整个模型主要由交替堆叠的Mamba-2层和MoE层构成，自注意力层只保留了少数几个。论文给出的层排布模式是：5个Mamba-2+MoE的重复单元，接3个同样结构的单元，再来1个包含注意力层的单元，最后是4个Mamba-2+MoE单元。在8k输入、16k输出的典型推理场景下，Nemotron 3 Nano 30B-A3B的吞吐量是Qwen3-30B-A3B的3.3倍。序列越长，优势越明显。与此同时，模型在长上下文任务上的表现并没有打折扣。论文展示了一组RULER基准测试的结果：在100万token输入长度下，Nemotron 3 Nano基座模型拿到了68.2分，而在同样条件下训练的Nemotron 2 Nano 12B只有23.43分，出现了断崖式下跌。MoE混合架构在长度外推上的鲁棒性明显更好。LatentMoE：在潜空间里做专家路由针对Super和Ultra这两个更大的模型，英伟达提出了LatentMoE架构，在潜在空间中进行专家计算。MoE层在实际部署时会遇到两类瓶颈：低延迟场景下，每次只处理几十到几百个token，此时从显存读取专家权重成为主要开销。高吞吐场景下，一次处理数千token，此时专家间的all-to-all通信成为瓶颈。两种情况下，开销都与隐藏维度d线性相关。LatentMoE的做法是：先把token从原始隐藏维度d投影到一个更小的潜在维度ℓ（通常是d的四分之一），在这个低维空间里完成专家路由和计算，最后再投影回原始维度。这样一来，每个专家的权重加载量和通信量都降低了d/ℓ倍。省下来的计算预算被用于增加专家数量和每个token激活的专家数。标准MoE用128个专家、激活6个；LatentMoE用512个专家、激活22个。两者的总参数量和激活参数量几乎相同（都是8B激活、73B总参），但LatentMoE在所有下游任务上都取得了更好的成绩——MMLU-Pro从48.30提升到52.87，代码任务从51.95提升到55.14，数学任务从78.32提升到80.19。需要注意的是，路由门控网络、共享专家计算以及非专家层仍然保留在原始维度，因为这些部分对瓶颈的贡献很小。用NVFP4训练250亿tokenSuper和Ultra还采用了NVFP4格式进行训练，这是英伟达在低精度训练上的又一次探索。NVFP4是一种4位浮点格式，采用E2M1的元素格式（2位指数、1位尾数），配合16元素的微块缩放和E4M3格式的块缩放因子。在GB300上，FP4的峰值吞吐量是FP8的3倍。论文显示，团队已经用NVFP4格式稳定训练了高达25万亿token。与BF16训练相比，Nano模型的损失差距控制在1%以内，8B激活参数的更大模型差距进一步缩小到0.6%以内。在MMLU、GSM8K、Humaneval等下游任务上，NVFP4训练的模型与BF16版本的准确率曲线几乎完全重合。不过并非所有层都适合量化到NVFP4。团队发现Mamba输出投影层在量化后会出现高达40%的flush-to-zero现象，因此保留在MXFP8精度；QKV投影和注意力投影保留在BF16以维持少量注意力层的保真度；网络最后15%的层也保持高精度以确保稳定性。MTP层和潜在投影由于对推理时间影响很小，同样保留在BF16。多环境强化学习一把训到底Nemotron 3的后训练采用了多环境强化学习，覆盖数学推理、竞赛编程、指令遵循、软件工程、搜索、对话、通用工具使用、长上下文等多种任务。与之前分阶段训练不同能力的做法不同，这次英伟达选择同时训练所有任务。论文指出，这种同步训练方式更稳定，更不容易出现reward hacking，也避免了分阶段训练常见的能力退化问题。AIME25数学分数从80提升到90，LiveCodeBench从65提升到72，τ²-Bench工具使用从40提升到50左右，全程呈稳定上升趋势。高效的推理吞吐量在这里发挥了重要作用。大规模RL需要生成海量rollout样本，Nemotron 3的混合架构相比其他开源模型有显著优势。团队还采用了异步RL架构来解耦训练和推理，并利用多token预测加速rollout生成。训练算法方面使用GRPO配合masked importance sampling来处理训练策略和rollout策略之间的差异。整个后训练软件栈以Apache 2.0协议开源，包括NeMo-RL（可扩展RL训练）和NeMo-Gym（RL环境集合）两个仓库。Nemotron 3还支持推理时的思维预算控制。用户可以指定思维链的最大token数，当模型达到预算时，追加一个标记即可让模型基于部分思维链生成最终回答。论文给出了准确率与平均生成token数之间的权衡曲线，这为实际部署中的效率-精度平衡提供了细粒度控制。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"2","news_tag":"dataReport","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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