Unverified50% confidenceFactExact time
The download size of an MoE model is far larger than a dense model with equivalent inference cost, because all expert weights need to be stored locally.
1
Sources
50%
Confidence
Long-term
Relevance
9/28/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe trade-off of MoE is higher demand on VRAM capacity, because all expert parameters need to be loaded simultaneously, along with more complex load-balancing engineering.80% similarUnverifiedThe file size and VRAM usage of a MoE model depend on the total parameter count rather than the activated parameter count, because all expert weights need to be stored.78% similarUnverifiedAlthough the MoE architecture reduces computational costs, its large total parameter count still places high demands on VRAM and storage, posing greater challenges for local deployment.72% similarUnverifiedThe weights of all experts in a MoE model still need to reside in memory, so the demand for memory capacity does not decrease with the activation ratio69% similarUnverifiedThe download size of a model does not equal the memory required to run it, because model weights, conversation context, temporary visualizations, the operating system, and applications in use all consume memory.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/956791API
curl https://kongchang.com/api/v1/knowledge/claims/956791MCP
get_claim(id=956791)