Sleep Time Compute: Beyond Inference Scaling at Test Time

The concept of "Sleep Time Compute" aims to shift the trade-off between accuracy and real-time cost in AI systems by decoupling reasoning from response time. Instead of performing all reasoning during a live query, it splits tasks into two phases: offline reasoning during idle periods using a heavier model, and online response during user queries using a lighter, faster model. This approach offers several benefits, including the same accuracy with lower cost, higher accuracy with the same cost, and compute amortization when context is reused. However, there are also limitations to be aware of, such as hallucination propagation and complexity trade-offs. The implications of Sleep Time Compute extend beyond cost and performance, mirroring patterns in traditional software and contributing to more sustainable and stateful AI deployments.

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