At this point, machine learning has seeped into a lot of my workflows. What I can do and how well is capped by video memory. This is useful enough that it's jus

At this point, machine learning has seeped into a lot of my workflows. What I can do and how well is capped by video memory. This is useful enough that it's just a matter of time before this makes it into mainstream tools. There are GPGPU things other than machine learning which will likely follow. Right now, I have a card with 16GB, which cost a bit over a grand. This means it can do Stable Diffusion at 512x512, but not higher resolutions. It can do some NLP models, but not GPT-3 scale ones. It can do awesome speech recognition. These things are useful enough that I claim more people will want to use them as soon as they don't require knowing Python. I think it's just a matter of time before enterprising folks will write code to make some of these workflows available to everyone. I don't think it's unlikely that GPGPU will be standard at least in business laptops in the near future, for the productivity increase. If Intel can do this for 1/2-1/3 the cost of NVidia, the impact will be huge. If Intel can democratize truly large models, like GPT-3, the impact will be even greater. If a model takes 20 seconds instead of 10, that's not a huge difference. If it runs out of VRAM, it is. business market >>> data center market > gamer market This kind of stuff was Intel's sweet spot maybe two decades ago. They shipped high-quality numerical code, stable drivers, a lot of open-source, and robust integration with operating systems.