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Trading Off Compute in Training and Inference – Epoch

epochai.org · 7,098 words · saved by 1 readers

Some techniques allow to increase the performance of machine learning models at the cost of more expensive inference, or reduce inference compute at the cost of lower performance. This possibility induces a tradeoff between spending more resources on training or on inference. We explore the characteristics of this tradeoff and outline some implications for AI governance. In current machine learning systems, the performance of a system is closely related to how much compute is spent during the training process. However, it is also possible to augment the capabilities of a trained model at the cost of increasing compute usage during inference or reduce compute usage during inference at the cost of lower performance. For example, models can be pruned to reduce their inference cost, or instructed to reason via chains of thought, which increases their inference cost. Based on evidence from five concrete techniques (model scaling, Monte Carlo Tree Search, pruning, resampling, and chain of th

Trading off compute in training and inference | Epoch AI Key takeaways In current machine learning systems, the performance of a system is closely related to how much compute is spent during the training process. However, it is also possible to augment the capabilities of a trained model at the cost of increasing compute usage during inference or reduce compute usage during inference at the cost of lower performance. For example, models can be pruned to reduce their inference cost, or instructed to reason via chains of thought, which increases their inference cost. Based on evidence from five

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