Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Thoughts on hardware / compute requirements for AGI, published by Steve Byrnes on January 24, 2023 on The AI Alignment Forum.

Let’s say I know how to build / train a human-level (more specifically, John von Neumann level) AGI. And let’s say that we (and/or the AGI itself) have already spent a few years on making the algorithm work better and more efficiently.

Question: How much compute will it take to run this AGI?

(NB: I said "running" an AGI, not training / programming an AGI. I'll talk a bit about “training compute” at the very end.)

Answer: I don’t know. But that doesn’t seem to be stopping me from writing this post. ¯\_(ツ)_/¯ My current feeling—which I can easily imagine changing after discussion (which is a major reason I'm writing this!)—seems to be:

75%: One current (Jan 2023) high-end retail gaming PC (with an Nvidia GeForce RTX 4090 GPU) will be enough (or more than enough) for human-level human-speed AGI,

85%: One future high-end retail gaming PC, that will on sale in a decade (2033), will be enough for human-level AGI, at ≥20% human speed.

This post will explain why I currently feel this way.

Table of Contents / TL;DR

In the prologue (Section 1), I’ll give three reasons that I care about this question: one related to our long-term prospects of globally monitoring and regulating human-level AGI; one related to whether an early AGI could be “self-sufficient” after wiping out humanity; and one related to whether AGI is even feasible in the first place. I’ll also respond to two counterarguments (i.e. arguments that I shouldn’t care about this question), namely: “More-scaled-up AGIs will always be smarter than less-scaled-up AGIs; that relative comparison is what we care about, not the absolute intelligence level that’s possible, on, say, a single GPU”, and “The very earliest human-level AGIs will be just barely human-level on the world’s biggest compute clusters, and that’s the thing that we should mainly care about, not how efficient they wind up later on”.

In Section 2, I’ll touch on a bit of prior discussion that I found interesting or thought-provoking, including a claim by Eliezer Yudkowsky that human-level human-speed AGI requires ridiculously little compute, and conversely a Metaculus forecast expecting that it requires orders of magnitude more compute than what I'm claiming here.

In Section 3, I’ll argue that the amount of computation used by the human brain is a good upper bound for my question. Then in Section 3.1 I’ll talk about compute requirements by starting with the “mechanistic method” in Joe Carlsmith’s report in brain computation and arguing for some modest adjustments in the “less compute” direction. Next in Section 3.2 I’ll talk about memory requirements, arguing for the (initially-surprising-to-me) conclusion that the brain has orders of magnitude fewer bits of learned information than it has synapses—100 trillion synapses versus ≲100 billion bits of incompressible information. Putting these together in Section 3.3, I reach the conclusion (mentioned at the top) that a retail gaming GPU will probably be plenty for human-level human-speed AGI. Finally I’ll talk about my lingering doubts in Section 3.3.1, by listing a few of the most plausible-to-me reasons that my conclusion might be wrong.

In Section 4, I’ll move on from running an AGI to training it (from scratch). This is a short section, where I mostly wanted to raise awareness of the funny fact that the ratio of training-compute to deployed-compute seems to be ≈7 orders of magnitude lower if you estimate it by looking at brains, versus if you estimate it by extrapolating from today’s self-supervised language models. I don’t have a great explanation why. On the other hand, perhaps surprisingly, I claim that resolving this question doesn’t seem particularly important for AGI governance q...