Unverified50% confidenceFactExact time
Transformer's KV cache size grows linearly with sequence length, whereas Mamba2's recurrent state cache size is independent of context length and depends only on the state dimension and the number of layers.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Mamba2-8B Compression Research: 27MB Cache and 3.14GB Model
redditr/deeplearning10/16/2026
Related Entities
Related Claims
UnverifiedThe KV cache stores key-value pairs from Transformer attention computations and grows linearly with sequence length.66% similarUnverified在 Transformer 推理中，KV 缓存占用的显存可达总显存的 30%-60%66% similarVerifiedThe size of the KV cache grows linearly with context length; the KV cache for Qwen 2.5 32B at a 64K context may consume several additional gigabytes of memory.63% similarUnverifiedBecause expert pruning preserves the attention architecture and KV cache format, the KV cache handoff requires no cache conversion, and the runtime only needs to switch weight masks between the two stages.62% similarUnverifiedLFM2.5-24B-A2B's KV cache grows far more slowly than a pure Transformer of the same depth, because only 10 layers are attention layers, making the memory overhead of long contexts significantly lower.61% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1048600API
curl https://kongchang.com/api/v1/knowledge/claims/1048600MCP
get_claim(id=1048600)