How cheap must rivalling high vram offerings be to upset the balance and move devs towards Intel/AMD?
Do you think their current platform offerings are mature enough to grab market share with “more for less” hardware or is the software support advantage just too large?
They need to be substantially cheaper and (more importantly) they need loads more memory.
The problem is that everyone (chiefly nvidia, but not only) is afraid to hurt their professional offerings by introducing consumer grade ML cards. They are not afraid of Joe having to use a smaller model to do AI on his security cameras, they are afraid of large companies ditching all their A100 cards for consumer equipment.
So they try and segment the market any way they can think of and Joe gets screwed.
The bizarre thing about this is that AMD’s workstation card volume is comically small, and Intel’s is probably nonexistant.
On the high end… Intel literally discontinued their HPC GPUs. The AMD MI300X is doing OK, but clearly suffering from a lack of grassroots software support.
From my limited consumer-level perspective, Intel/AMD platforms aren’t mature enough. Try looking into any open-source/commercial ML software aimed at consumers, Nvidia is guaranteed and first class.
Intel is not as bad in LLM land as you’d think. Llama.cpp support gets better every day.
Nvidia may be first class, but in this case, it doesn’t matter if the model you want doesn’t fit in VRAM. I’d trade my 3090 for a 48GB Arc card without even blinking, even if the setup is an absolute pain.
Only because AMD/Intel aren’t pricing competitively. I define “best experience” as the largest LLM/context I can fit on my GPU, and right now that’s essentially dictated by VRAM.
That being said, I get how most wouldn’t want to go through the fuss of setting up Intel/AMD inference.
How cheap must rivalling high vram offerings be to upset the balance and move devs towards Intel/AMD?
Do you think their current platform offerings are mature enough to grab market share with “more for less” hardware or is the software support advantage just too large?
They need to be substantially cheaper and (more importantly) they need loads more memory.
The problem is that everyone (chiefly nvidia, but not only) is afraid to hurt their professional offerings by introducing consumer grade ML cards. They are not afraid of Joe having to use a smaller model to do AI on his security cameras, they are afraid of large companies ditching all their A100 cards for consumer equipment.
So they try and segment the market any way they can think of and Joe gets screwed.
It’s classic market failure really.
The bizarre thing about this is that AMD’s workstation card volume is comically small, and Intel’s is probably nonexistant.
On the high end… Intel literally discontinued their HPC GPUs. The AMD MI300X is doing OK, but clearly suffering from a lack of grassroots software support.
WTF are they afraid of losing?
From my limited consumer-level perspective, Intel/AMD platforms aren’t mature enough. Try looking into any open-source/commercial ML software aimed at consumers, Nvidia is guaranteed and first class.
The situation is arguably different in gaming.
Intel is not as bad in LLM land as you’d think. Llama.cpp support gets better every day.
Nvidia may be first class, but in this case, it doesn’t matter if the model you want doesn’t fit in VRAM. I’d trade my 3090 for a 48GB Arc card without even blinking, even if the setup is an absolute pain.
Thanks for the insight. Kinda sad how selfhosted LLM or ML means Nvidia is a must have for the best experience.
Only because AMD/Intel aren’t pricing competitively. I define “best experience” as the largest LLM/context I can fit on my GPU, and right now that’s essentially dictated by VRAM.
That being said, I get how most wouldn’t want to go through the fuss of setting up Intel/AMD inference.