Thread

1. dghlsakjg · 1785816328 · focus · HN ↗

   I know everyone wants to crap all over these setups that are impractical, but this is how progress happens.

   People will keep plugging away at this and figure out how to avoid wearing the hard drive, how to make it run faster, custom hardware buses etc.

   Keep going! I personally can't wait for the day when a 1t param model runs off a $200 SSD instead of a $50k rack of Nvidia chips.

   1. gpt5 · 1785824544 · focus · HN ↗

      Most people are already used to rely on the internet on basically everything. At best, they download a tiny chunk of entertainment from it when they go on a plane, and as soon as they land they immediately abandon that offline chunk.

      In addition, LLMs, small or large, are highly parallelizable. This means that running on the same machine/GPUs many requests in parallel is significantly more efficient, and the sum of tok/s will far outweight any single user use case.

      Those two combined means that unless LLMs reach the state of 'good enough' (TBD), I fully expect the economics and people's interest to align with 99%+ of LLM usage would be in centralized servers. (not dismissing the fact that there are use cases for local LLMs, and that the progress matters).

      1. anon373839 · 1785825976 · focus · HN ↗

         I suspect the economics favor centralized servers, if you only look at the aggregated cost to serve X number of users' tokens. But we could say the same thing about a lot of the computation that iPhones do locally. They could have been much thinner clients, but instead they now have more compute power than desktops had when iPhones launched.

         1. zozbot234 · 1785831394 · focus · HN ↗

            > I suspect the economics favor centralized servers, if you only look at the aggregated cost to serve X number of users' tokens.

            The economics of real-time, low-latency inference of very large near-SOTA models will heavily favor a centralized setup. But if you can afford to wait for your answer - be it a day, a week, or even more at the extreme low end (or if you just stick to leaner models for your relatively quick replies) the economics start to shift in a very clear way. A slow-going local inference setup relying on cheap SSD offload does not need the high power input of a datacenter rack, and the cooling load is outright trivial - even when working on many requests in parallel, which (in a SSD offload context) is what maximizes throughput even for local inference. These are serious problems for centralized inference that will probably limit the scale at which it can be applied.

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