Unverified50% confidenceBenchmarkExact time
MiniMax's new architecture reduces prefill compute consumption by approximately 5x and shrinks the KV cache by approximately 4.5x under million-level context by sharing KV cache across layers and using an intelligent filtering attention mechanism.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/28/2026
First Seen
Valid until: 12/27/2026
Sources
Related Entities
Related Claims
VerifiedMulti-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.75% similarUnverifiedMiniMax 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.75% similarUnverifiedDeepSeek's prefix caching implementation is based on the MLA (Multi-head Latent Attention) architecture, which reduces the storage requirements of the KV Cache by tens of times through low-rank compression.73% similarUnverified随着模型上下文窗口从4K到128K再到未来可能的数百万token持续扩大，记忆管理和状态管理的实际效果差距会逐渐缩小73% similarUnverifiedTechnologies that compress the KV cache directly translate into longer context windows and lower per-unit inference costs.71% similar
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