In short: Training runs of large Machine Learning systems are likely to last less than 14-15 months. This is because longer runs will be outcompeted by runs that start later and therefore use better hardware and better algorithms. [Edited 2022/09/22 to fix an error in the hardware improvements + rising investments calculation]

Scenario	Longest training run
Hardware improvements	3.55 years
Hardware improvements + Software improvements	1.22 years
Hardware improvements + Rising investments	9.12 months
Hardware improvements + Rising investments + Software improvements	2.52 months

Larger compute budgets and a better understanding of how to effectively use compute (through, for example, using scaling laws) are two major driving forces of progress in recent Machine Learning.

There are many ways to increase your effective compute budget: better hardware, rising investments in AI R&D and improvements in algorithmic efficiency. In this article we investigate one often-overlooked but plausibly important factor: how long—in terms of wall-clock time—you are willing to train your model for.

Here we explore a simple mathematical framework for estimating the optimal duration of a training run. A researcher is tasked with training a model by some deadline, and must decide when to start their training run. The researcher is faced with a key problem: by delaying the training run, they can access better hardware, but by starting the training run soon, they can train the model for longer.

Using estimates of the relevant parameters, we calculate the optimal training duration. We then explore six additional considerations, related to 1) how dollar-budgets for compute rise over time, 2) the rate at which algorithmic efficiency improves, 3) whether developers can upgrade their software over time 4) what would happen in a more realistic framework with stochastic growth, 5) whether it matters for the framework that labs are not explicitly optimizing for optimal training runs and 6) what would happen if they rent instead of buy hardware.

Our conclusion depends on whether the researcher is able to upgrade their hardware stack while training. If they aren't able to upgrade hardware, optimal training runs will likely last less than 3 months. If the researcher can upgrade their hardware stack during training, optimal training runs will last less than 1.2 years.

These numbers are likely to ...

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