Baker proposes a new metric decomposing MFU that he developed the morning of the interview.
“MFU is the most important metric because it gives you all of these advantages and ways to differentiate yourself amongst five people, six people who've trained these GPT core class models.”
Baker explains higher MFU allows 25% faster time to market with same GPU and power spending.
“You have the same amount of GPUs and the same amount of power presumably. You could choose between faster time to market.”
Baker explains MFU runs at 35-40%, measuring the percentage of theoretical compute actually used for training.
“MFU, model flops utilization, and that generally runs around 35 to 40%. And that's literally the percentage of compute, theoretical compute flops that you're actually applying to trading.”
Baker explains that 50% MFU versus 40% MFU enables 25% faster time to market for AI models.
“You could choose between faster time to market. If you run a 50% MFU and your competitor's running 40 for an equivalent amount of trading flops, you could be in market 25% faster.”