3 ms·

The part that's not clear to me is how. The value I see in AMDs participation is opening up the CUDA walled garden. This is different than what's good for TW. T

The part that's not clear to me is how. The value I see in AMDs participation is opening up the CUDA walled garden. This is different than what's good for TW. They would be better off with an AMD/TW walled garden that they can provide service for at better prices. But better prices alone won't be enough to get companies to move from the largest/only walled garden to a new/smaller one. The best I could see is developing a service platform that frictionlessly and more efficiently runs CUDA workloads on AMD using a proprietary translation. Not a bad bet, IMO.

latchkey 3y ago
Availability is a big driver as well. If you can get a MI300x today vs. waiting a year for H100's, which do you go with?
- Jlagreen 3y ago
  Wait for H100 and use Nvidia support and any Nvidia card to get start? In my company 90% of computers have a Nvidia card so you can get started with CUDA immediately to start data aggregation and planning your AI training and infercing while waiting for deployment. Totally forget that approach with AMD. Nvidia also assist larger customers with DGX cloud access through their large deployed super computers. Nvidia's AI workbench helps a lot here as you can easily transfer your applications from local RTX cards to on-prem or cloud data centers.
  - latchkey 3y ago
    As of late last year, AMD is now committed to AI. MI300x was released in Dec of last year, only months ago. It has 192gb, while H100's only have 80. There are at least 4 companies now providing bare metal cloud access to these cards. +2 more are hyperscalers (Oracle and MSFT). It is critically important for the long term safety of AI that we are not dependent on a single source for all of the hardware and software related to AI. It takes a lot of effort to course correct a large ship, give it some time.

3 ms·

The part that's not clear to me is how. The value I see in AMDs participation is opening up the CUDA walled garden. This is different than what's good for TW. T

by karmakaze 3y ago

latchkey 3y ago
Availability is a big driver as well. If you can get a MI300x today vs. waiting a year for H100's, which do you go with?
- Jlagreen 3y ago
  Wait for H100 and use Nvidia support and any Nvidia card to get start? In my company 90% of computers have a Nvidia card so you can get started with CUDA immediately to start data aggregation and planning your AI training and infercing while waiting for deployment. Totally forget that approach with AMD. Nvidia also assist larger customers with DGX cloud access through their large deployed super computers. Nvidia's AI workbench helps a lot here as you can easily transfer your applications from local RTX cards to on-prem or cloud data centers.
  - latchkey 3y ago
    As of late last year, AMD is now committed to AI. MI300x was released in Dec of last year, only months ago. It has 192gb, while H100's only have 80. There are at least 4 companies now providing bare metal cloud access to these cards. +2 more are hyperscalers (Oracle and MSFT). It is critically important for the long term safety of AI that we are not dependent on a single source for all of the hardware and software related to AI. It takes a lot of effort to course correct a large ship, give it some time.