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I later found enough optimizations to reduce the generation speed all the way down to ~16ms. Do I need a word cloud generator that fast? No. But if I have a word cloud generator it's going to be as fast as possible dammit.
[0] Yes, for LLMs we're closer to the bound than 1000x. Even there though, basic pytorch operations are often 2x slower than simple rewrites, better scheduling algorithms have a history of closer to 5x-10x gains, and all of that supposes you don't have further architectural improvements over time. Moreover, who's to say that legitimately fast tokenization doesn't unlock additional capabilities elsewhere which people have ignored because it was never close to viable?
Globally that’s ~50 GWh/yr, or ~5.7 MW continuous:
• 4,700 American homes
• a week of British tea
This is the "GPU driver optimizations don't matter because PC's sit idle at the desktop most of the time" mindset.
If this increases efficiency, it has real impact. 0.1% is A LOT at scale.
But look at e.g. Biome; optimizing the formatter didn't seem worth it for a long time because it only took <1s to format most files. But it's <1s times millions of files, billions of times a day when you add up every developer that used Prettier for their code formatting - it adds up.
And I'm convinced Biome triggered or was part of a bigger effort to convert JS based tools to native code. This saved time and energy, which in turn allows for faster and / or more feedback loops, which in turn allows faster turnaround cycles for software development (bet it human or LLM assisted), etc. It's a compound effect.
I don't know enough about tokenization or whatever to judge this one, but if it's 1000x as fast as it used to be, there will be less need to try and avoid or minimize tokenization which may lead to new applications.