AICEMAJun 11

Can I Buy Your KV Cache?

arXiv:2606.13361v111.3
Predicted impact top 59% in AI · last 90 daysOriginality Incremental advance
AI Analysis

For AI service providers and publishers, this work addresses the massive compute waste from repeated prefill in multi-agent systems, offering a practical caching and payment framework.

The paper proposes that publishers precompute and sell KV caches for documents, allowing AI agents to skip prefill and reuse the cache, achieving token-exact results with 9-50x compute savings on Qwen3-4B. Serving a hot 3774-token document to 80M agents costs ~$1.5M in re-prefill vs ~$0.03M in reuse compute (49.7x less).

Right now, across the world, AI agents are repeating the same absurd act: to read one document, they each recompute it from scratch. Every agent re-runs prefill, the most compute-intensive step a large model takes, over identical text, only to rebuild a key-value (KV) cache identical to the one the agent before it just built. The same answer, computed a million times. We make a proposal that is almost offensively simple: compute it once. Let a publisher precompute a document's KV cache, and let every other agent buy the right to load it and skip prefill. It works, and it is token-exact: loading a precomputed KV and continuing matches prefilling from scratch (24/24 greedy tokens, and at the logits level), with no accuracy cost. 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. Then the part that matters: where the KV lives. Shipping it fails, because KV is nearly incompressible, so per-load egress costs more than the prefill it saves. Hosting it provider-side, exactly as production prompt-caching works, removes egress entirely. The size of the prize is set by our measured compute saving: 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). The 0.1x cache-read tariff APIs charge passes a 10x discount to users while sitting inside this measured envelope, so the 10x is a floor that the measured ~50x compute saving clears, and the gap to the physical ~50x is provider margin: millions of dollars per popular document. We frame the resulting agent-native prefill CDN and leave lossless KV compression and a cross-party payment layer as the open problems.

Foundations

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