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I think the question stands. You can't fit 25 4090s in a robot (unless we're talking about something massive with an equally massive battery), and even if you c
by mrtranscendence 3y ago
I think the question stands. You can't fit 25 4090s in a robot (unless we're talking about something massive with an equally massive battery), and even if you could an LLM wouldn't be appropriate for driving a robot.
Given the pace of improvements I don't see how you compress 25 4090s into a single GPU in 7 years. A 4090 isn't 25 times the power of a GTX 980, it's closer to maybe three times.
- machiaweliczny 3y agoLight or quantum could possibly deliver by that time
- LarsDu88 3y agoNeither of these are likely nor necessary to deliver the necessary results.
- LarsDu88 3y agoTeslas are already consumer items that rock massive batteries. My 25 count is off. It's probably closer to 75 GPUs right now. Let's say today's models are running vanilla transformers via pytorch without any of Deepmind's Flamingo QKV optimizations. In 10 years algorithmic optimizations, and ML platform improvements push that efficiency up 3-5 fold. We're down in the ballpark of 25 GPUs (again) Now, we ditch the general purpose GPUs entirely and go for specially built AI inference chip. The year is 2033 and specialty inference chips are better and more widespread. Jettison those ray tracing cores, and computer rendering stuff. Another 3x improvement and we're at ~8 GPUs Now you said the 4090 is about 3.5x faster than the 780 from a decade ago. We are now on the order of 2 chips to run inference. These models won't just be running in Teslas, they will be running in agricultural vehicles, military vehicles, and eventually robot baristas.
- deleted 3y ago[deleted]
- ChatGTP 3y agoI guess you’re onto something, that combined with reductive, concave, inference architecture gains and new flux capacitor designs we are on 10x performance maximum optimisation curve. I can taste that robot barista cappuccino now.