Deep Papers Sleep-time Compute: Beyond Inference Scaling at Test-time
May 2, 2025
Imagine if your AI could anticipate your questions before you even ask! This intriguing discussion centers on sleep-time compute, a method allowing models to prepare answers during idle moments. By precomputing reasoning steps, it significantly cuts down on latency and costs while boosting accuracy. The talk dives into new benchmarks showing impressive reductions in compute use and cost. Additionally, the potential of leveraging idle GPUs for improved efficiency and the challenges of optimizing resources in AI systems make for a fascinating listen.
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Shift Heavy Compute Offline
- Sleep-time compute shifts model heavy lifting offline before user queries arrive.
- It cuts test-time costs and latency without reducing answer quality.
Token Efficiency and Accuracy Gains
- Sleep-time compute reduces tokens needed by about 80% for queries maintaining same answer quality.
- Larger token budgets then yield about 15% higher accuracy using this approach.
Use for Stable, Large Contexts
- Use sleep-time compute only when the context is stable and substantially large.
- Avoid it for small or highly dynamic context where gains won't outweigh costs.