Precomputing KV Caches Could Dramatically Reduce AI Agent Compute Costs
By
[Submitted on 11 Jun 2026]
Crisped on the outside, thoughtful enough on the inside.
Summary
This article proposes a radical efficiency improvement for AI agents: instead of each agent recomputing the key-value (KV) cache from scratch when reading a document (the expensive "prefill" step), publishers should precompute the KV cache once and let other agents buy the right to load it. The authors demonstrate that loading a precomputed KV cache is token-exact (identical results) and 9-50x cheaper in compute than running prefill from scratch, with the gap widening for longer documents. However, shipping KV caches across networks fails due to egress costs exceeding the compute savings. The solution is provider-side hosting (similar to existing prompt-caching), which eliminates egress entirely. The economic analysis shows serving one hot document to 80M agents costs ~$1.5M to re-prefill vs ~$0.03M with reuse (49.7x less), leaving room for a 10x discount to users while still generating millions in provider margin per popular document. The paper frames this as an "agent-native prefill CDN" and identifies lossless KV compression and cross-party payment as open problems.
Key quotes
· 5 pulledRight now, across the world, AI agents are repeating the same absurd act: to read one document, they each recompute it from scratch.
We make a proposal that is almost offensively simple: compute it once.
On Qwen3-4B, reuse is 9-50x cheaper in compute than prefill, and the gap widens with length (prefill's attention scales with L^2), so a single reuse already pays it back.
Shipping it fails, because KV is nearly incompressible, so per-load egress costs more than the prefill it saves.
Serving one hot 3774-token document to 80M agents costs ~$1.5M to re-prefill but only ~$0.03M of reuse compute (49.7x less).
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