Running llama-2-7b-chat at 8 bit quantization, and completions are essentially at GPT-3.5 levels on a single 4090 using 15gb VRAM. I don’t think most people realize just how small and efficient these models are going to become.

[cut out many, many paragraphs of LLM-generated output which prove… something?]

my chatbot is so small and efficient it only fully utilizes one $2000 graphics card per user! that’s only 450W for as long as it takes the thing to generate whatever bullshit it’s outputting, drawn by a graphics card that’s priced so high not even gamers are buying them!

you’d think my industry would have learned anything at all from being tricked into running loud, hot, incredibly power-hungry crypto mining rigs under their desks for no profit at all, but nah

not a single thought spared for how this can’t possibly be any more cost-effective for OpenAI either; just the assumption that their APIs will somehow always be cheaper than the hardware and energy required to run the model

  • froztbyte@awful.systemsEnglish
    1·
    1 year ago

    also, as an interesting semi-segue on this thought: there is an active group of researchers between a number of entities in africa working on creating better online corpuses of african languages (because what exists online is so scant)

    they’ve been at it for over 2 years now, as far as I know. when I last looked, fairly little of their work had gotten wider recognition

    “too small, too niche” to “address properly” is how each of these large outfits treat things like this. if they ever do give it some attention at all, it would likely be as part of some wider (batched) brush-stroke push to “improve our support for non-english languages” (or “$x art” or or or), and each will be given their respective 5% of attention for 3 hours then never again