this post was submitted on 14 Feb 2024
678 points (95.4% liked)
Technology
59440 readers
3610 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related content.
- Be excellent to each another!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, to ask if your bot can be added please contact us.
- Check for duplicates before posting, duplicates may be removed
Approved Bots
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I don't think any inference engines have actually been optimised to run on CPUs. You're stuck with 32-bit floats but OTOH that just means that you can do gigantic winograd transformations with the excess precision, needing far fewer fmuladds in total and CPUs are better at dealing with the memory access patterns that come with transforming the convolution. Most people have at least around 1TFLOP of compute in their CPU (e.g. a Ryzen 3600 has that much) that's not ever seeing the light of day. About a fifth of what an RX 570 has, it's a difference but not a magnitude and you can run SDXL with that kind of class of card (maybe not the 570 dunno about software support but a 5500 works, despite AMD's best efforts to cripple rocm).
Also from what I gather they're more or less doing summarybot for your browsing history, that's not a ChatGPT or Llama-style giant model you can talk with.
Also to all those people complaining: There's already AI in firefox, the translation models are about 17MB per language pair, gzipped.
ONNX Runtime is actually decently well optimized to run on CPUs; even with large models. However, the simple truth is that there’s really no escaping that Billion+parameter models need to be quantized and even pruned heavily to fit in memory and not saturate the CPU cache so inferences/generations don’t take forever. That’s a reduction in accuracy, so the quality of the generations aren’t great.
There is a lot of really interesting research and development being done right now on smart quantization and pruning. Model serving technologies are improving rapidly too—paged attention is a really cool technique (for transformer based models) for effectively leveraging tensor core hardware—I don’t think that’s supported on CPU yet but it’s probably not that far off.
It’s a really active field and there’s just as much interest in running huge models on huge hardware as there is big models on small hardware. I recently heard of layerwise inference for CPUs; load each layer of the network to the CPU cache on demand. That’s typically a bottleneck operation on GPUs but CPU memoery so bloody fast that it might actually work fine. I haven’t played with it myself, or read the paper all that deeply so I can’t really comment more than it’s an interesting idea.