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Comment by mikestorrent

2 months ago

Consider also that right now LLMs run slowly enough you can watch them think. I've seen a demo of an LLM running at an absurdly high speed and it reminds me of when I moved from a 2400 baud modem to a 14.4 - BBS screens that I could watch draw were all of a sudden nigh-interactive. Faster-than-realtime video generation is also coming, and will also continue to require huge hardware for a long while yet.

I love local models - I have a machine at home that runs a few for me and it's a lot of fun - but for the time being they are not super trustworthy on tool calls and staying on script. Another year or so might change all that!

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mikestorrent

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KoolKat23  2 months ago

If anyone wishes to see the future. A fast LLM is quite eye-opening. I think chatjimmy uses Talaas' chips where models are hardcoded into the silicon.

https://chatjimmy.ai/

  • calgoo  2 months ago

    Yea, is almost "scary fast" in a sense... the amount of compute you can do in parallel one one of those chips is amazing. hopefully they get their next chip completed as that will be a lot more useful for general workloads. I think the current one is based on llama3.

    *corrected llama version to 3

  • flyingjoe  2 months ago

    Thanks, I didn't know that one! Very impressive speed although quality seems very bad

    • msdz  2 months ago

      > although quality seems very bad

      The weights they “etched” into the FPGA card that’s used for the ChatJimmy demo are that of a Llama 3-something 8b model.

      The actually impressive and novel thing is that Taalas’ve managed to automate that process (clearly – nobody transforms 8 billion numbers into a physical representation by hand).

      So now, they can work on scaling this process up, and with low enough lead times (I’ll be convinced they have inside connections to TSMC if they can actually deliver on the promised mere 3-4 months delay), will be able to offer 30-100b+ parameter models under half a year after they’re released, at thousands of tokens per second while probably drawing less wattage (per token, not sure about overall).

      Exciting times ahead, folks.

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