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I see many of the improvements made by DeepSeek as "obvious in retrospect": they're the sort of innovations that, had somebody requested me upfront about them, I might have said had been good concepts. But the fact that the export controls haven't had all of their supposed effects is just not the identical thing as the export controls having failed. The o1 methods are built on the same model as gpt4o but benefit from considering time. The fundamental problem with strategies resembling grouped-query consideration or KV cache quantization is that they involve compromising on mannequin high quality in order to reduce the scale of the KV cache. But defenders will benefit only if they recognize the magnitude of the problem and act accordingly. Around the time that the first paper was released in December, Altman posted that "it is (comparatively) simple to copy something that you realize works" and "it is extraordinarily hard to do one thing new, risky, and difficult if you don’t know if it will work." So the claim is that DeepSeek isn’t going to create new frontier fashions; it’s simply going to replicate outdated models. Within two weeks of the release of its first free chatbot app, the cell app skyrocketed to the top of the app store charts within the United States.

RMSNorm 和 MLA 上投影的重计算 (Recomputation): 在反向传播过程中，DeepSeek-V3 会重新计算 RMSNorm 和 MLA 上投影的输出，而不是将这些中间结果存储在显存中。提高累加精度： 为了减少 FP8 计算过程中的精度损失，DeepSeek-V3 将 MMA (Matrix Multiply-Accumulate) 操作的中间结果累加到 FP32 寄存器中。选择性高精度： 对于模型中对精度较为敏感的组件 (例如 Embedding、Output Head、MoE Gating、Normalization、Attention 等)，DeepSeek-V3 仍然采用 BF16 或 FP32 进行计算，以保证模型的性能。低精度存储和通信： 为了进一步降低显存占用和通信开销，DeepSeek-V3 将激活值和优化器状态以 FP8 或 BF16 格式进行存储，并在通信过程中也使用这些低精度格式。

这种设计减少了模型的参数量和内存占用。 DeepSeek-V3 的训练策略涵盖了数据构建、分词其、超参数设置、长上下文扩展和多 Token 预测等多个方面。这种策略可以更好地适应数据的分布，减少量化误差。 DeepSeek-V3 在内存管理方面也做到了极致，通过多种策略最大程度地减少了内存占用。首先，大幅提升了数学和编程相关数据在整体数据中的占比，这直接增强了模型在相关领域的推理能力，使其在 MATH 500、AIME 2024 等数学基准测试和 HumanEval、LiveCodeBench 等代码基准测试中表现突出。 DeepSeek-V3 通过 FP8 混合精度训练，在保证模型精度的同时，大幅降低显存占用并提升训练速度。

这种策略避免了在 GPU 上存储 EMA 参数带来的额外显存开销。通过在 eight 个 PP rank 上，20 个 micro-batch 的 DualPipe 调度情况，可以看到，通过双向流水线的设计，以及计算和通信的重叠，流水线气泡被显著减少，GPU 利用率得到了极大提升。 Warp 专业化 (Warp Specialization): 将不同的通信任务 (例如 IB 发送、IB-to-NVLink 转发、NVLink 接收等) 分配给不同的 Warp，并根据实际负载情况动态调整每个任务的 Warp 数量，实现了通信任务的精细化管理和优化。节点限制路由 (Node-Limited Routing): 将每个 Token 最多路由到 four 个节点，有效限制了跨节点通信的范围和规模。 DeepSeek online-V3 通过一系列精细的优化策略，有效地缓解了这一瓶颈。

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