NVIDIA 解析 Dense 与 MoE 模型:活跃参数、吞吐量与选型指南
NVIDIA 技术博客对比 Dense 与 MoE 架构:Nemotron 3.5 Lightning 采用 MoE,每 token 仅激活 30B 总参数中的 3B,而 Gemma 4 31B 等 Dense 模型每次前向传播激活全部参数。相同总参数量下 MoE 吞吐更高,但高并发时延迟优势收窄;MoE 将内存成本与计算成本解耦,微调需防路由失衡,量化对路由层影响也不同。选型应看内存预算、并发需求、微调计划与量化行为,而非仅看参数量。
her than raw parameter count alone. Next Steps Try Nemotron 3.5 Lightning on build.nvidia.com to evaluate the model interactively. Download the Nemotron 3.5 Lightning weights from Hugging Face for local deployment. Access the model via OpenRouter for API-based inference. Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more How can a 30B-parameter model activate only 3B parameters per token, and still use the capacity of
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