Unverified50% confidenceFactExact time
MoE架构相较于同等能力的稠密模型，在推理阶段显存带宽压力更低、tokens/s更高
1
Sources
50%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
Real-World Comparison of 6 Open-Source LLMs for On-Premise Deployment: A Selection Guide to Avoid Pitfalls
bilibili懂算力的Strong哥7/8/2026
Related Claims
UnverifiedThe advantage of Dense models lies in their simple architecture, more controllable inference latency, and lower deployment threshold, making them especially suitable for local deployment, while MoE requires more VRAM but delivers stronger capabilities under equivalent computational overhead.73% similarUnverifiedThe MoE architecture routes and activates only a small number of experts per pass (typically 2–8), enabling the total parameter scale to far exceed that of compute-dense models of equivalent computational cost.73% similarUnverifiedLow-bit quantization of MoE models can easily cause routing bias, which in turn leads to sudden degradation in certain capability dimensions.68% similarUnverifiedMoE模型在低并发场景下的推理延迟往往不如同等激活参数量的密集模型，更适合高吞吐量云端批处理场景67% similarUnverifiedK2.5的MoE架构单次推理实际激活的参数量可能只有总参数的1/8甚至更少65% similar
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