• ikt@aussie.zone
      link
      fedilink
      arrow-up
      4
      arrow-down
      4
      ·
      edit-2
      5 months ago

      For which you still need massive amounts of memory and compute to run reliably

      2026’s average gaming PC is massive amounts of memory and compute apparently

      The gap will take decades to close, if it ever does.

      lol there are plenty of open source models in the top 100 with multiple SOTA models released in the last few months alone

      There’s also smaller LLM’s being made like https://eurollm.io/ which excel in their own ways

      That, and the fact that chatbots and agents nowadays rely on all sorts of proprietary customizations

      Funny that just came up: https://discourse.ubuntu.com/t/the-future-of-ai-in-ubuntu/81130?=0

      Previously, to benefit from the full power of LLMs, you had to skew to higher parameter models. Recent developments in models like Gemma 4 and Qwen-3.6-35B-A3B demonstrate advanced capabilities such as tool-calling which enable LLMs to search the web, interact with external APIs and file systems, troubleshoot live systems and fundamentally reason about topics that lie outside of their initial training data.

      The gap will take decades to close, if it ever does.

      😁

        • ikt@aussie.zone
          link
          fedilink
          arrow-up
          3
          arrow-down
          4
          ·
          5 months ago

          But regardless, the main point of the gap is resources

          What makes you think we won’t have the resources in the future?

          Any model that can run on 16GB or less, is not going to be any close in real world tasks, to any other cloud based model. It just cannot be.

          Well you can compare Gemma 4 running in LM Studio on an average gaming PC to ChatGPT3.5 and you tell me? Or is your benchmark purely based on right at this very moment between open source models today vs cloud today?

          For reference Gemma 4 is 26 billion parameters, gp3 thought to be over 175 billion and of course had no optimisations like MoE, it was searching its entire library every single question so was rather slow as well

          We know as well that there is no slow down in pushing for optimisations, Deepseeks initial release was the initial driver for you don’t have to just scale up using hardware alone

          https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/

          They’re also pushing with Chinese native chips from Huawei trying to diversify away from nvidia holding the crown

          The problem I’ve got is that you all have a god of the gaps, the conversation I was having 3 years ago was different to 2 years ago was different to 1 year ago, I was told AI could never do songs good enough then suddenly people were worried they couldn’t tell the difference, then they said they could never do movies, now apparently not only is it good enough it’s hilarious

          https://www.youtube.com/watch?v=fgHn7PI55J4

          The open source LLM’s we have today are incredible and in the last few months we’ve had Qwen, GLM, Nemotron/Nvidia, Mistral, Google and heeaaps of others released, it feels like you’re just looking for a reason to be dour and pessimistic but that’s just me

          Any way I’m off to sleep, have a good one :)