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. hobofan · 1785835762 · focus · HN ↗

         > Those two combined means that unless LLMs reach the state of 'good enough'

         Once you are able to not just go on-premise, but on-device, there a boatload of other advantages than efficiency (network independence, privacy, etc.) that make it attractive.

         Tiny language models are already plenty "good enough" for a lot of on-device use-cases. E.g. there are a lot of things, that can be reformulated as ranking problems, where you can apply pairwise ranking, which even small language models are good at.

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