Sam Altman: “Local inference is a terrible allocation of scarce resources”

Reddit r/LocalLLaMA News
local-inference cloud-computing openai sam-altman open-source dram compute-allocation

Summary

Sam Altman argues that running AI models locally is an inefficient use of scarce global DRAM resources and that cloud-based, high-utilization data centers are a better allocation. The post adds a skeptical TL;DR framing the remark as a vendor pitch for renting over owning.

Sam Altman: “Local inference is a terrible allocation of scarce resources. A lot of people want local inference, and somehow they assume that the problem is solved now because we have open-source models, we're going to be able to run them on our computers, and so we're not going to need all the stuff in the data centre but from a global allocation perspective, actually it's quite terrible to have everybody, like, have a bunch of the world's limited supply of DRAM sitting in their own computer and being used five percent of the time you want maximum utilisation in the cloud if you're trying to deal with this very supply-constrained environment, somehow many people's intuition is the opposite of that” —————————————————————— TL;DR Guy selling product: "Here's why owning your own product instead of renting it from me is a terrible idea" (Credits to @RyanTStull, his TLDR made me laugh)
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@FinanceYF5: Sam Altman Argues Local Inference Is a Poor Allocation of Resources in a Constrained Environment: "Many people think that with open-source models, you can run them on your own computer and no longer need data centers. But from the perspective of global resource allocation, making everyone store large amounts of scarce DRAM in their computers while only using it 5% of the time is highly inefficient."

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Sam Altman argues that local inference is an inefficient allocation of resources in a constrained environment: equipping everyone's computer with large amounts of scarce DRAM that is only used about 5% of the time is far less effective than keeping cloud resources at high utilization.