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Show HN: Run an 80B Qwen in 4.3 GB of RAM on a Mac, and a 35B on an iPhone

310 points · 140 comments · leonickson

  1. dghlsakjg · · 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. arjie · · focus · HN ↗
      Haha 1T on $50k might be a bit hopeful, mate, even at FP8. But I too am hopeful.
      1. apimade · · focus · HN ↗
        8800 GTX in 2006. Cutting-edge, an insanely powered consumer card for the time. Theoretically around 0.3456 TFLOPS.

        1080 GTX in 2016. Cutting-edge, an insanely powerful consumer card for the time. Theoretically around 8.87 to 8.9 TFLOPS.

        5090 RTX in 2026. Cutting-edge, an insanely powerful consumer card for today. Theoretically around 104.8 TFLOPS.

        In the same timeframe mobile processor CPU's went from 0.001 TFLOPS, to today's Apple's A19 Pro chip which delivers 2.074 TFLOPS.

        That's _without_ getting into ASIC's, or purpose-built hardware like Taalas's model on silicon HC1, or generic AI dies like what they're planning with HC2 or Cerebras, which will massively compress the timeline.

        1. foxrider · · focus · HN ↗
          Speaking of ASICs - how likely is it that as models get better we'll see someone baking a whole model directly into the silicon? It's like having l0 cache.
          1. kaelwd · · focus · HN ↗
            Only 8B currently but it&#x27;s been done: <a href="https:&#x2F;&#x2F;taalas.com&#x2F;products&#x2F;" rel="nofollow">https:&#x2F;&#x2F;taalas.com&#x2F;products&#x2F;
            1. apimade · · focus · HN ↗
              The only _public_ example we know.

              This is definitely being done with private models by HFT&#x2F;quant firms, data processing agencies&#x2F;orgs (large intelligence agencies, _every_ data analytics org, etc).

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