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Is it practical/affordable to train an AlphaZero/MuZero engine using a residential gaming PC, or would it take thousands of years of training for the AI to learn enough to challenge humans?

I'm having trouble wrapping my head around how much computing power '4 hours of Google DeepMind training' equates to my residential computer running 24/7 trying to build a trained AI.

Basically, are AlphaZero or MuZero practical for indie board games that want a state of the art AI, or is it too expensive to train?

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The vast majority of neural networks are now trained on graphics processing units (GPUs) or specialised accelerator hardware such as tensor processing units (TPUs).

In Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, Silver et al. say that the training process involved 5,000 first-generation TPUs generating self-play games and 64 second-generation TPUs for training. This is certainly far beyond what any practical gaming computer is likely to achieve, as you'll likely only have one GPU, and that might not even rival a single TPU. Training on the CPU will be substantially slower again than either a GPU or TPU. Training would be orders of magnitude slower; you might find these benchmarks by Wang et al. of interest.

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    $\begingroup$ Not arguing with any part of this answer, and I may add that I think gcp (creator of Leela zero) estimated something like 10000 years in single GPU (estimated 3 years ago). However this training time depends on the game complexity a lot and also if the game has no professional players then a single GPU may well be the best in the world after a few months if not a few weeks or even days. $\endgroup$ – davik Feb 15 at 19:23

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