3 ms·

This is still amazing work, imagine running chungus models on a single 3090.

• Sohcahtoa82 2y ago
  The bottleneck on a consumer-grade GPU like a 3090 isn't the processing power, it's the lack of RAM. The PCI-Express bus ends up being your bottleneck from having to swap in parts of the model. Even with PCIe 5.0 and 16 lanes, you only get 64 GB/s of bandwidth. If you're trying to run a model too big for your GPU, then for every token, it has to reload the entire model. With a 70B parameter model, 8 bit quantization, you're looking at just under 1 token/sec just from having to transfer parts of the model in constantly. Making the actual computation faster won't make it any faster.
  • dragonwriter 2y ago
    OTOH, doesn't it also mean that (given appropriate software framework support) iGPUs with less processing capacity and slower-but-more RAM available (because system RAM is comparatively cheap and plentiful compared to VRAM) without swapping anything are more competitive against consumer dGPUs with fast-but-small RAM for both inference and training with larger models?
    • Sohcahtoa82 2y ago
      System memory isn't that fast, either. Even with DDR5-8400, the fastest memory you can get right now, you're only looking at a memory transfer speed of 67.2 GB/s, barely faster than the PCI-E bus. So even if you could store that entire 70B model in RAM, you're still getting just under 1 token/sec, and that's assuming your CPU doesn't become a bottleneck. Your best bet would likely be a laptop that has integrated system RAM with VRAM, but I don't think any of those offer enough RAM to store an entire 70B model. A 7B parameter model would work fine, but you could do those on a consumer-grade GPU anyways.
      • DeveloperErrata 2y ago
        Macbook Pros with M3 & integrated RAM & VRAM can do 70B models :)