Does this also ship only in x8 batches? I really liked MI300 and could afford
one of them for my research, but they only come in batches of x8 in a server rack, so I decided to buy an RTX Pro 6000.
These ppl are very loud online, but they don't make decisions for hyperscalers which are biggest spenders on AI chips.
AMD is doing just fine, Oracle just announced an AI cluster with up to 131,072 of AMD's new MI355X GPUs.
AMD needs to focus on bringing rack-scale mi400 as quickly as possible to market, rather than those hobbyists always find something to complain instead of spending money.
we're talking about the majority of open source developers (I'm one of them). if researchers don't get access to hardware X, they write their paper using hardware Y (Nvidia). AMD isn't doing fine because most low level research on AI is done purely on CUDA.
So nVidia has a huge software lead because of open source developers like you? Or because people employed by nVidia write closed source high performance drivers and kernels? Or because the people employed by Meta and Google that wrote Torch and Tensorflow built it on nVidia?
I am really sympathetic to the complaints. It would just be incredibly useful to have competition and options further down the food chain. But the argument that this is a core strategic mistake makes no sense to me.
Nit: just writing Torch/TF isn't what made the difference. Having them adopted by a huge audience outside those orgs is, and that's bottlenecked on the hardware platform choice.
AMD has demonstrably not even acknowledged that they needed to play catch-up for a significant chunk of the last 20 years. The mistake isn't a recent one.
Look at China. A couple of years ago people thought people in China weren't doing good AI research, but the thing is, there's good AI research from basically everywhere-- even South America. You can't assume that institutions can spend >$250k on computational resources.
Neither their revenue nor their market share in the space looks like just fine. What exactly in trailing the market for years is “just fine”?
AMD is very far behind, and their earnings are so low that even with a nonsensical pe ratio they’re still less than a tenth of nvidia. No, they are not doing anywhere near fine.
Are hobbyists the reason for this? I’m not sure. However, what AMD is doing is clearly failing.
A big chunk of NVidia's current price is a reflection of lacking meaningful competition. So straight comparison isn't quite fair: if AMD started to do better, the gap would shrink from both ends.
When you design software for N customers, where N is very small, and you expect to hold each customers' hand individually, the software is basically guaranteed to be hot garbage that doesn't generalize or actually work except in exactly the use cases you supported (there are exceptions to this, but it requires having exceptional software engineers and leaders that care about doing things correctly and not just closing the next ticket, and in my experience, they are extremely rare).
If you design software for N00000 customers, it can't be shit, because you can't hold the hands of that many people, it's just not possible. By intending to design software for a wide variety of users, it forces you to make your software not suck, or you'll drown in support requests that you cannot possibly handle.
Honestly, if they "don't have the resources to satisfy N00000 customers", they better get them. That will teach them in the hard way to work differently.
> These ppl are very loud online, but they don't make decisions for hyperscalers which are biggest spenders on AI chips.
this guy gets it - absolutely no one cares about the hobby market because it's absolutely not how software development is done (nor is it how software is paid for).
The hobby market should be considered as a pipeline to future customers. It doesn't mean AMD should drop everything and cater specifically to them, but they'd be foolish to ignore them altogether.
Water is wet. Please check the history of their software stack and why it always was superior to alternatives when they didn't have a lot more money than ATI/AMD. The reason they power hyperscalers is because they catered to enthusiasts and academy researchers attempting to use their GPUs for general purpose computations in early 2000s, since GeForce 3. Then they used that experience to build CUDA which simply worked, and quickly gained mindshare. People have used their software for all imaginable purposes, which was a major factor behind their improvements and eventually becoming market leaders as killer applications for GPGPU have been found (simulation and then AI). This experience is not replicable even with dogfooding, which AMD also doesn't seem to do.
no we're not broke! we constantly write grants and receive funding from various sources. guess what hardware we recommend the University to purchase? it's 99.9% Nvidia, and sometimes Mac Studio just to play with MLX.
It has gone through many boom and busy cycles. If you go far back enough, it was very well funded. In particular, I recall reading about the US government investing 1 to 2 billion dollars during the Cold War into AI research to translate Russian into English. It had some very impressive demos on preselected Russian texts that had justified the investments. However, it failed to yield results on arbitrary texts. The translation problem has only been mostly solved in recent years.
A number of people want to purchase their own hardware, not rent cloud hardware. I recently purchased a RTX PRO 6000 for the same reason, despite having the option of renting a B200 VM for $1.49 an hour from DeepInfra until the end of June.
True, but as time goes on, it will become a wider and wider gap between what is deployed in DC’s and what you can run at home.
We see it now with 8x UBB and it will get worse with direct liquid cooling and larger power requirements. Mi300x is 700w. Mi355 is 1200w. Mi450 will be even more.
Certainly amd should make some consumer grade stuff, but they won’t stop on the enterprise side either. Your only option to get super computer level compute, will be to rent it.
The higher power requirements of datacenter GPUs are mostly from running hardware well past a reasonable point on the efficiency curve to eke slightly higher generational increases in performance. Nvidia for example has three versions of the RTX PRO 6000. One runs at 600W while the other two run at 300W. The main differences are the power target and the cooling solutions. If I recall correctly, the performance difference between them is less than 20%, despite a 50% reduction in power consumption, for what is effectively the same hardware. This can be confirmed by changing the power target of the 600W version to 300W and benchmarking the before and after. Plenty of people have made similar observations of AMD’s hardware.
That said, I am confident that Nvidia will continue serve those of us who want our own hardware.
Your comment does not make much sense to me. The 355x and 300x are two different chips, not binned versions of one another. The RTX PRO 6000 has fp4/fp6 support too, so there is no need to use datacenter exclusive hardware to get that.
