I think that AMD needs to really push ahead on two fronts. The first being price/performance. They need to do much more than just being a few percent ahead of NVidia on price. They need moderate cards that have 48gb vram at under $2k that are competitive to the 4090. That's only half the battle, because said cards need to compete with top NV cards for gaming, just so that people will buy them for play and stay to dev with.
The other front is developer experience and tooling, NVidia is way ahead on this front and entrenched. They need cleaner integrations and abstractions for OpenCL. This should probably include clean support for Python tooling as well as for Rust targets. The former being massive for education and common use space and the latter being for those that want to eek out performance without necessarily using C. Both of which will mean more community involvement and investment that lasts longer than AMD is typically known for.
If AMD targets mainly support for Linux tooling, then is should/must also support WSL for windows users. No idea where Mac is headed in terms of expansion boards on M2 or future gen. But they definitely need to expand the user base with good, relatively cheap higher end cards as well as devex.
Edit: the top end mentioned at 48gb is just for top consumer comparison... I think good tooling for 16-24gb cards in the $500-1200 space that is gaming competitive and can handle AI experimentation and workstation workloads would go a long way as well.
Totally agree on that, securing a piece of the AI market will be a huge challenge. No one will buy AMD for AI when the software isn't compatible and no one will buy AMD for AI to get wonky software for the same price as NVIDIA.
Affordable cards with lots of memory and good software support is the only solution to maybe get into the market. Double the memory for some gaming cards (just like NVIDIA's 3060 12GB and the 4060 TI 16GB)
Additionally data center products with enormous vram amounts and very fast interconnects will be important
AMD can't even take orders that NVIDIA can't keep up with because they are producing on very similar nodes. NVIDIA can easily outspend AMD for TSMC production capacity
Yeah, but my meaning is to offer a good to great value gaming card that can do a job of getting feet wet in the AI side. I don't think the bulk of people dipping their toes into AI on NVidia/Cuda are using their really expensive cards. AMD needs to win over the hobbiest and SOHO workstation types.
The same types that will run a 5950x/7950x for 16-cores without jumping to threadripper or server parts are the same ones that are playing with Cuda on 3080/4080 class hardware. This drives the market in open-source and prosumer into the professional path.
MI300 looks very promising, but it doesn't instantly solve the chicken-egg problem. Nvidia has been placing eggs in the form of "cuda on gaming gpus" in the nests of researchers and students for quite some time.
"Nobody ever got fired for buying nvidia" might be the situation in the future if AMD doesn't manage to get a more popular choice among developers
The other front is developer experience and tooling, NVidia is way ahead on this front and entrenched. They need cleaner integrations and abstractions for OpenCL. This should probably include clean support for Python tooling as well as for Rust targets. The former being massive for education and common use space and the latter being for those that want to eek out performance without necessarily using C. Both of which will mean more community involvement and investment that lasts longer than AMD is typically known for.
If AMD targets mainly support for Linux tooling, then is should/must also support WSL for windows users. No idea where Mac is headed in terms of expansion boards on M2 or future gen. But they definitely need to expand the user base with good, relatively cheap higher end cards as well as devex.
Edit: the top end mentioned at 48gb is just for top consumer comparison... I think good tooling for 16-24gb cards in the $500-1200 space that is gaming competitive and can handle AI experimentation and workstation workloads would go a long way as well.