Expired50% confidenceFactExact time
MiniMax M3 adopts an MSA (Multi-head Shared Attention) architecture, which reduces memory usage and computational cost during long-sequence inference by sharing part of the KV cache across multiple attention heads.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
5 Models Coding Showdown: Claude, GPT, DeepSeek, M3 — Who's the Best Engineering Tool?
bilibili不正经的前端啊6/8/2026
Related Claims
UnverifiedDeepSeek V4's DSA (Differential Sparse Attention) mechanism reduces computation to 27% of V3.2 and KV cache to 10% in million-token context scenarios.65% similarUnverifiednum_ctx 参数直接影响内存占用,KV缓存需要为每个上下文token存储注意力计算的中间状态62% similarVerifiedMulti-head Latent Attention (MLA) compresses storage by projecting KV into a low-dimensional latent space, reducing KV Cache memory usage to 5% to 13% of that of traditional methods.62% similarUnverifiedMLA(Multi-head Latent Attention)通过低秩压缩将 KV 缓存的显存占用降低至传统 MHA 的约 1/859% similarUnverifiedMavis将记忆分为全局记忆、智能体记忆和会话记忆三个层级59% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/48722API
curl https://kongchang.com/api/v1/knowledge/claims/48722MCP
get_claim(id=48722)