Radar ·
Radar: cache and attention techniques in LLMs
A post dated October 1, 2026 reports 890 bytes of global KV cache per token and a persistent cache eight times smaller in a language model.
A post dated October 1, 2026 claims that a language model uses 890 bytes of global KV cache per token and a persistent cache eight times smaller. It cites Causal Encoder-Decoder, Compressed Sparse Attention, and SWA Bounded Replay as related techniques. These are claims made by the post, not independently verified findings in this summary.
The reported cache reductions and attention techniques may interest engineers optimizing inference costs. Consult the original post for its methods and context; before applying its claims, look for evaluation details and compare them with technical documentation or your own tests. If using AI to analyze internal material on the topic, avoid submitting personal or confidential data without authorization and follow your organization’s policy.