English(EN) A 671B model reads 36GB per token and still needs 671GB of HBM

DeepSeek-V3 的 MoE 架构节省带宽但需要更高容量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 15:44

对 DeepSeek-V3 模型进行的最新分析显示，尽管它拥有6710亿个参数，但其混合专家（MoE）架构意味着每个token仅激活约360亿个参数。存储参数与激活参数之间的这种显著差异使得模型能够实现高吞吐量，在读取带宽方面表现得像一个规模小得多的密集模型。然而，这种效率是以增加容量需求为代价的，因为所有激活的参数都必须驻留在内存中，这与密集模型不同，密集模型中存储和读取带宽是直接成比例的。 AI

影响 MoE 模型提供了显著的带宽优势，但需要仔细的容量规划，影响部署策略。

排序理由模型架构和性能特征分析。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 2 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 模型架构和性能特征分析。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: model release, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

dev.to — LLM tag TIER_1 English(EN) · Jax Sinclair · 2026-11-02 15:44

A 671B model reads 36GB per token and still needs 671GB of HBM

<p>Two numbers describe DeepSeek-V3 and they look like a contradiction. Decoding one token reads about 36 GB of weights. Storing the model takes 671 GB. If you have only ever sized dense models, those two figures should be the same number, because a dense model reads all of its w…

本文如何被排名

报道来源 [1]

A 671B model reads 36GB per token and still needs 671GB of HBM

相关实体

相关话题