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The point about using FP32 for training is wrong. Mixed precision (FP16 multiplies, FP32 accumulates) has been use for years – the original paper came out in 2017.
When he says better linux drivers than AMD he's strictly talking about for AI, right? Because for video the opposite has been the case for as far back as I can remember.
Earlier quoted context omitted.
Sorry, I don’t know who George Hotz is, but why isn’t AMD making better drivers for AMD?
George Hotz is a hot Internet celebrity that has basically accomplished nothing of value but has a large cult following. You can safely ignore. (Famous for hacking the PS3–except he just took credit for a separate group’s work. And for making a self-driving car in his garage—except oh wait that didn’t happen either.)
If we are to get to AGI why do we need to train on all data? That's silly, and all we get is compression and probabliatic retrieval. Intelligence by definition is not compression, but ability to think and act according to new data, based on experience. Trully AGI models will work on the this principle, not on best compression of as much data as possible. We need a new approach.
Great article but it seems to have a fatal flaw. As pointed out in the article, Nvidia has several advantages including: - Better Linux drivers than AMD - CUDA - pytorch is optimized for Nvidia - High-speed interconnect Each of the advantages is under attack: - George Hotz is making better drivers for AMD - MLX, Triton, JAX: Higher level abstractions that compile down to CUDA - Cerbras and Groq solve the interconnect…
> - Better Linux drivers than AMD Unless something radically changed in the last couple years, I am not sure where you got this from? (I am specifically talking about GPUs for computer usage rather than training/inference)
This was the first thing that stuck out to me when I skimmed the article, and the reason I decided to invest the time reading it all. I can tell the author knows his shit and isn't just parroting everyone's praise for AMD Linux drivers.
> (I am specifically talking about GPUs for computer usage rather than training/inference)
Same here. I suffered through the Vega 64 after everyone said how great it is. So many AMD-specific driver bugs, AMD driver devs not wanting to fix them for non-technical reasons, so many hard-locks when using less popular software.
The only complaints about Nvidia drivers I found were "it's proprietary" and "you have to rebuild the modules when you update the kernel" or "doesn't work with wayland".
I'd hesitate to ever touch an AMD GPU again after my experience with it, haven't had a single hick-up for years after switching to Nvidia.
Earlier quoted context omitted.
Exactly. You just need to see a slight deceleration in projected revenue growth (which has been running 120%+ YoY recently) and some downward pressure on gross margins, and maybe even just some market share loss, and the stock could easily fall 25% from that.
AMD P/E ratio is 109, NVDA is 56. Which stock is overvalued?
Great article but it seems to have a fatal flaw. As pointed out in the article, Nvidia has several advantages including: - Better Linux drivers than AMD - CUDA - pytorch is optimized for Nvidia - High-speed interconnect Each of the advantages is under attack: - George Hotz is making better drivers for AMD - MLX, Triton, JAX: Higher level abstractions that compile down to CUDA - Cerbras and Groq solve the interconnect…
> - Better Linux drivers than AMD Unless something radically changed in the last couple years, I am not sure where you got this from? (I am specifically talking about GPUs for computer usage rather than training/inference)
Looks like huge astroturfing effort from CCP. I am seeing these coordinated propaganda inside every AI related sub on reddit, on social media and now - here.
This is excellent writing. Even if you have no interest at all in stock market shorting strategies there is plenty of meaty technical content in here, including some of the clearest summaries I've seen anywhere of the interesting ideas from the DeepSeek v3 and R1 papers.
Thanks Simon! I’m a big fan of your writing (and tools) so it means a lot coming from you.