Unverified50% confidenceFactExact time
The Mistral model family uses sliding window attention with a window size of 4,096 tokens at each layer, with a constant KV Cache size, achieving O(1) memory usage.
1
Sources
50%
Confidence
Long-term
Relevance
8/31/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe KV Cache size of sliding window attention is constant, achieving O(1) memory usage rather than O(n).80% similarUnverifiedThe context window is the maximum amount of information a large model can process in a single session, measured in tokens; modern top-tier models' context windows have expanded from the early 4K tokens to 200K tokens or even higher.70% similarUnverifiedIn long-context scenarios (such as models with a 128K token window), the GPU memory occupied by the KV Cache may exceed that of the model weights themselves.67% similarUnverifiedThe context window of modern large models has expanded from the early 4K tokens to 128K or even longer, and the computational complexity of the attention mechanism grows quadratically with context length.66% similarUnverifiedA model's context window is measured in tokens, and information exceeding the window length is forgotten; for example, a model supporting 8,000 tokens can only process that amount of information at one time.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/830108API
curl https://kongchang.com/api/v1/knowledge/claims/830108MCP
get_claim(id=830108)