However the amount of resources at stake is incredible. The delta between NVIDIA's value and AMD's is bigger than the annual GDP of Spain. Even if they needed to hire a few thousand engineers at a few million in comp each, it'd still be a good investment.
They have made an alternative to the CUDA language with HIP, which can do most of the things the CUDA language can.
You could say that they haven't released supporting libraries like cuDNN, but they are making progress on this with AiTer for example.
You could say that they have fragmented their efforts across too many different paradigms but I don't think this is it because Nvidia also support a lot of different programming models.
I think the reason is that they have not prioritised support for ROCm across all of their products. There are too many different architectures with varying levels of support. This isn't just historical. There is no ROCm support for their latest AI Max 395 APU. There is no nice cross architecture ISA like PTX. The drivers are buggy. It's just all a pain to use. And for that reason "the community" doesn't really want to use it, and so it's a second class citizen.
This is a management and leadership problem. They need to make using their hardware easy. They need to support all of their hardware. They need to fix their driver bugs.
https://github.com/ROCm/ROCm/issues/1714
Users complaining that the docs don't even specify which cards work.
But it goes deeper - a valid complaint is that "this only supports one or two consumer cards!" A common rebuttal is that it works fine on lots of AMD cards if you set some environment flag to force the GPU architecture selection. The fact that this is so close to working on a wide variety of hardware, and yet doesn't, is exactly the vibe you get with the whole ecosystem.
It's the first thing anyone tries when trying to dabble in AI or compute on the gpu, yet it's a clusterfuck to get to work. A few blessed cards work, with proper drivers and kernel; others just crash, perform horribly slow, or output GGGGGGGGGGGGGG to every input (I'm not making this up!) Then you LOL, dump it and go buy nvidia et voila, stuff works first try.
It's quite fast also, probably because that card has fast HBM2 memory (it has the same memory bandwidth as a 4090). And it was really cheap as it was on deep sale as an outgoing model.
I have Nvidia cards too by the way, a 4090 and a 3060 (the latter I use for AI also, but more for Whisper because faster-whisper doesn't do ROCm right now).
AMD keeps having issues because their drivers talk to the hardware directly so their drivers are massive bloated messes, famous for pages of auto-generated register definitions. Likely it's much more difficult to fix anything.
I'm asking because I think a firmware has to directly talk to hardware through lower HAL (hardware abstraction layer), while customer facing parts should be fairly isolated in the upper HAL. Some companies like to add direct HW acces to customer interface via more complex functions (often a recipe made out of lower HAL functions), which I always disliked. I prefer to isolate lower level functions and memory space from the user.
In any case, both Nvidia and AMD should have very similar FW capabilities. I don't know what I'm missing here.
Nvidia cards are much easier to program in the user mode driver. You cannot hang a Nvidia GPU with a bad memory access. You can hang the display engine with one though. At least when I was there.
You can hang an AMD GPU with a bad memory access. At least up to the Navi 3x.
NVIDIA while far from perfect has always easily kept stride in software quality ahead of AMD for over 20 years. While AMD repeatedly keeps falling on their face and getting egg all over themselves again and again and again as far as software goes.
My guess is NVIDIA internally has found a way to keep the software people from feeling like they are "less than" the people designing the hardware.
Sounds easily but apparently not. AKA management problems.
I'm a chip design engineer and I get frustrated with the garbage SW/FW team come up with, to the extent that I write my own FW library for my blocks. While doing that I try to learn the best practices, do quite a bit of research.
One other reason is, SW was only FW till not long ago, which was serving the HW. So there was almost no input from SW to HW development. This is clearly changing but some companies, like Nvidia, are ahead of the pack. Even Apple SoC team is quite HW centric compared to Nvidia.
I kept running into some problem with LLVM's support for HIP code, even though I had not interest in having that functionality.
I realize this isn't exactly an AMD problem, but IIRC it was they were who contributed the troublesome code to LLVM, and it remained unfixed.
Apologies if there's something unfair or uninformed in what I wrote, it's been a while.
It's easy (and mostly correct) to blame management for this, but it's such a foundational issue that even if everyone up to the CEO pivoted on every topic, it wouldn't change anything. They simply don't have the engineering talent to pull this off, because they somehow concluded that making stuff open source means someone else will magically do the work for you. Nvidia on the other hand has accrued top talent for more than a decade and carefully developed their ecosystem to reach this point. And there are only so many talented engineers on the planet. So even if AMD leadership wakes up tomorrow, they won't go anywhere for a looong time.
Of course the specific disciplines need quite an investment into the knowledge of their workers, but it isn't anything insurmountable.
He would probably be able to attract some really good hardware and driver talent.
Yes. This kind of thing is unfortunately endemic in hardware companies, which don't "get" software. It's cultural and requires (a) a leader who does Get It and (b) one of those Amazon memos stating "anyone who does not Get With The Program will be fired".
https://github.com/likelovewant/ROCmLibs-for-gfx1103-AMD780M...
Not saying it works everywhere but it wasn't even that hard to setup, comparable to cuda.
Hate the name though.
Fire the ceo
There has been a long-standing issue between AMD and its mainboard manufacturers. The issue has to do with features required for ROCm, namely PCIe Atomics. AMD has been unable or unwilling to hold the mainboard manufacturers to account for advertising features the mainboard does not support.
The CPU itself must support this feature, but the mainboard must as well (in firmware).
One of the reasons why ROCm hasn't worked in the past is because the mainboard manufacturers have claimed and advertised support for PCIe Atomics, and the support they've claimed has been shown to be false, and the software fails in non-deterministic ways when tested. This is nightmare fuel for the few AMD engineers tasked with ROCm.
PCIe Atomics requires non-translated direct IO to operate correctly, and in order to support the same CPU models from multiple generations they've translated these IO lines in firmware.
This has left most people that query their system to check this showing PCIAtomics is supported, while when actual tests that rely on that support are done they fail, in chaotic ways. There is no technical specification or advertising that the mainboard manufacturers provide showing whether this is supported. Even the boards with multiple x16 slots and the many technologies related to it such as Crossfire/SLI/mGPU brandings these don't necessarily show whether PCIAtomics is properly supported.
In other words, the CPU is supported, the firmware/mainboard fail with no way to differentiate between the two at the upper layers of abstraction.
All in all. You shouldn't be blaming AMD for this. You should be blaming the three mainboard manufacturers who chose to do this. Some of these manufacturers have upper end boards where they actually did do this right they just chose to not do this for any current gen mainboard costing less than ~$300-500.
Nvidia has a large number of GPU related patents.
The fact that AMD chose to design their system this way, in such a roundabout and brittle manner, which is contrary to how engineer's approach things, may have been a direct result of being unable to design such systems any other way because of broad patents tied to the interface/GPU.
I notice that at one point there was a ROCm release which said it didn't require atomics for gfx9 GPUs, but the requirement was reintroduced in a later version of ROCm. Not sure what happened there but this seems to suggest AMD might have had a workaround at some point (though possibly it didn't work).
If this really is due to patent issues AMD can likely afford to licence or cross-license the patent given potential upside.
It would be in line with other decisions taken by AMD if they took this decision because it works well with their datacentre/high-end GPUs, and they don't (or didn't) really care about offering GPGPU to the mass/consumer GPU market.
Because "official ROCm support" means "you can rely on AMD to make this work on your system for your critical needs". If you want "support" in the "you can goof around with this stuff on your own and don't care if there's any breakage" sense, ROCm "supports" a whole lot of AMD hardware. They should just introduce a new "experimental, unsupported" tier and make this official on their end.
I don't see that, these two issues adequately explain why so few GPUs have official support. They don't want to get hit with a lawsuit, as a result of issues outside their sphere of control.
> If this really is due to patent issues AMD can likely afford to license or cross-license the patent given potential upside.
Have you ever known any company willing to cede market dominance and license or cross-license a patent letting competition into a market that they hold an absolute monopoly over, let alone in an environment where antitrust is non-existent and fang-less?
There is no upside for NVIDIA to do that. If you want to do serious AI/ML work you currently need to use NVIDIA hardware, and they can charge whatever they want for that.
The moment you have a competitor, demand is halved at a bare minimum depending on how much the competitor undercuts you by. Any agreement on coordinating prices leads to price-fixing indictments.
I'm sorry I don't follow this. Surely if all AMD GPUs have the same problem with atomics then this can't explain why some GPUs are supported and others aren't?
> There is no upside for NVIDIA to do that.
If NVIDIA felt this patent was actually protecting them from competition then there would be no upside. But NVIDIA has competiton from AMD, Intel, Google, and Amazon. Intel have managed to engineer OneAPI support for their GPUs without licensing this patent or relying on PCIe atomics.
AMD have patents NVIDIA would be interested in. For example multi-chiplet GPUs.
Create a "ROCm compatible" logo and a list of criteria. Motherboard manufacturers can send a pre-production sample to AMD along with a check for some token amount (let's say $1000). AMD runs a comprehensive test suite to check actual compatibility, if it passes the mainboard is allowed to be advertised and sold with the previously mentioned logo. Then just tell consumers to look for that logo if they want to use ROCm. If things go wrong on a mainboard without the certification, communicate that it's probably the mainboard's fault.
Maybe add some kind of versioning scheme to allow updating requirements in the future
This is also one of the reasons AMD and Apple can't simply turn their ship around right now. They've both invested heavily in simplifying their GPU and removing a lot of the creature-comforts people pay Nvidia for. 10 years ago we could at least all standardize on OpenCL, but these days it's all about proprietary frameworks and throwing competitors under the bus.
AMD also have on-die microcontrollers (multiple, actually) that do things like scheduling or pipeline management, again just like Nvidia's GSP. It's been able to schedule new work on-GPU with zero host system involvement since the original GCN, something that Nvidia advertise as "new" with them introducing their GSP (which just replaced a slightly older, slightly less capable controller rather than being /completely/ new too)
The problem is that AMD are a software follower right now - after decades of under-investment they're behind on the treadmill just trying to keep up, so when the Next Big Thing inevitably pops up they're still busy polishing off the Last Big Thing.
I've always seen AMD as a hardware company, with the "build it and they will come" approach - which seems to have worked for the big supercomputers who likely find it worth investing in their own modified stack to get that last few %, but clearly falls down selling to "mere" professionals. Nvidia, however, support the same software APIs on even the lowest end hardware, while nobody is likely running much on their laptop's 3050m in anger, it offers a super easy on-ramp for developers - and it's easy to mistake familiarity with superiority - you already know to avoid the warts so you don't get burned by them. And believe me, CUDA has plenty of warts.
And marketing - remember "Technical Marketing" is still marketing - and to this day lots of people believe that the marketing name for something, or branding a feature, implies anything about the underlying architecture design - go to an "enthusiast" forum and you'll easily find people claiming that because Nvidia call their accelerator a "core" means it's somehow superior/better/"more accelerated" than the direct equivalent on a competitor, or actually believe that it just doesn't support hardware video encoding as it "Doesn't Have NVENC" (again, GCN with video encoding was released before a Geforce with NVENC). Same with branding - AMD hardware can already read the display block's state and timestamp in-shader, but Everyone Knows Nvidia Introduced "Flip Metering" With Blackwell!
I would speculate that their design is self-contained in hardware.
My understanding is that the reason is that the real market for 3 (GPUs for compute) didn't show up until very late, so AMD's GCN bet didn't pay off. Even in 2021, NVIDIA's revenue from gaming was above data center revenue (a segment they basically had no competition in, and 100% of their revenue was from CUDA). AMD meanwhile won the battle for Playstation and Xbox consoles, and was executing a turnaround in data centers with EPYC and CPUs (with Zen). So my guess as to why they might have underinvested is basically: for much of the 2010s they were just trying to survive, so they focused on battles they could win that would bring them revenue.
This high level prioritization would explain a lot of "misexecution", e.g. if they underhired for ROCm, or prioritized APU SDK experience over data center, their testing philosophy ("does this game work ok? great").
Nvidia can afford to develop a comprehensive software platform for the compute market segment because it has a comprehensive share of that segment. AMD cannot afford it because it does not have the market share.
Or to put it another way, I assume that AMD's efforts are motivated rational economic behavior and it has not been economically rational to compete heavily with Nvidia in the compute segment.
AMD was able to buy ATI because ATI could not compete with Nvidia. So AMD's graphics business started out trailing Nvidia. AMD has had a viable graphics strategy without trying to beat Nvidia...which makes sense since the traditional opponent is Intel and the ATI purchase has allowed AMD to compete with them pretty well.
Finally, most of the call for AMD to develop a CUDA alternative is based on a desire for cheaper compute. That's not a good business venture to invest in against a dominate player because price sensitive customers are poor customers.
Nvidia’s gross margins are 80% on compute GPUs, that is excessive and likely higher than what cocaine and heroin dealers have for gross margins. Real competition would be a good thing for everyone except Nvidia.
Or to put it another way, it would not be good for AMD.
I agree with both your comment and the parent comment - serious competition could spell the end for CUDA's dominance. But there will never be serious competition, CUDA has the head-start and their competitors threw in the towel with OpenCL. Khronos can't get Apple to sign onto a spec and they can't get AMD to change their architecture - open GPGPU compute is stuck in neutral while Nvidia is shifting into 6th gear. Reality is that Nvidia could charge cloud-level margins and get away with it, because Apple is the only other TSMC customer with equivalent leverage and they pretend the server market doesn't exist.
This is such a key point. Everyone wants cheaper and cheaper compute - I want cheaper and cheaper compute. But not large-ish company wants to simply facilitate cheapness - they would a significant return on their investment and just making a commodity is generally not what they want. Back in the days of the PC clone, the clone makers were relatively tiny and so didn't have to worry about just serving the commodity market.
They can't bring themselves to put so much money into it that it would be an obvious fail if it didn't work.
https://www.datacenterdynamics.com/en/news/microsoft-bought-...
And probably not putting enough money behind it... it takes enormous courage as a CEO to walk into a boardroom and say "I'm going to spend $50 billion, I think it will probably work, I'm... 60% certain".
Whereas AMD's CEO was appointed, and can be fired. Huge difference in their risk appetite.
I'm reminded of pg's article "founder mode": https://paulgraham.com/foundermode.html
I think some companies simply aren't capable of taking big risks and innovating in big ways, for this reason.
Edit: though emphasis should be put on “easIER” because it’s still far from easy.
There aren't many cases like this. Larry/Sergey were more than comfortable risking $10 billion here and there.
What the hell is going on, they should be able to keep an army of PhDs doing pointless research even if only one paper in 10 years comes to a profitable product. But instead they are cutting down workforce like there is no tomorrow...
(I know, I know, market dynamics, value extraction, stock market returns)
What I am pointing out is that they could be doing a shit ton of research, what happened to big companies sponsoring fringe research? That used to be a thing, at Microsoft even.
But I think more importantly, what is often missed in this analysis is that most programmers doing ML work aren't writing their own custom kernels. They're just using pytorch (or maybe something even more abstracted/multi-backend like keras 3.x) and let the library deal with implementation details related to their GPU.
That doesn't mean there aren't footguns in that particular land of abstraction, but the delta between the two providers is not nearly as stark as its often portrayed. At least not for the average programmer working with ML tooling.
(EDIT: also worth noting that the work being done in the MLIR project has a role to play in closing the gap as well for similar reasons)
That would imply that AMD could just focus on implementing good PyTorch support on their hardware and they would be able to start taking market share. Which doesn't sound like much work compared with writing a full CUDA competitor. But that does not seem to be the strategy, which implies it is not so simple?
I am not an ML engineer so don't have first hand experience, but those I have talked to say they depend on a lot more than just one or two key libraries. But my sample size is small. Interested in other perspectives...
That is exactly what has been happening [1], and not just in pytorch. Geohot has been very dedicated in working with AMD to upgrade their station in this space [2]. If you hang out in the tinygrad discord, you can see this happening in real time.
> those I have talked to say they depend on a lot more than just one or two key libraries.
Theres a ton of libraries out there yes, but if we're talking about python and the libraries in question are talking to GPUs its going to be exceedingly rare that theyre not using one of these under the hood: pytorch, tensorflow, jax, keras, et al.
There are of course exceptions to this, particularly if you're not using python for your ML work (which is actually common for many companies running inference at scale and want better runtime performance, training is a different story). But ultimately the core ecosystem does work just fine with AMD GPUs, provided you're not doing any exotic custom kernel work.
(EDIT: just realized my initial comment unintentionally borrowed the "moat" commentary from geohot's blog. A happy accident in this case, but still very much rings true for my day to day ML dev experience)
[1] https://github.com/pytorch/pytorch/pulls?q=is%3Aopen+is%3Apr...
[2] https://geohot.github.io//blog/jekyll/update/2025/03/08/AMD-...
Just as a quantitative side note here — tinygrad has almost 400 contributors, pytorch has almost 4,000. This might seem small, but both projects have a larger people footprint than most tech companies' headcount that are operating at significant scale.
On top of that, consider that pytorch is a project with its origins at Meta, and Meta has internal teams that spend 100% of their time supporting the project. Coupled with the fact that Meta just purchased nearly 200k units worth of AMD inference gear (MI300X), there is a massive groundswell of tech effort being pushed in AMD's direction.
> wouldn't AMD just invest in doing this themselves, or at least provide much more support to these guys?
That was actually the point of George Hotz's "cultural test" (as he put it). He wanted to see if they were willing to part with some expensive gear in the spirit of enabling him to help them with more velocity. And they came through, so I think that's a win no matter which lens you analyze this through.
Since resources are finite, especially in terms of human capital, there's only so much to go around. AMD naturally can now focus more on the software closer to the metal as a result, namely the driver. They still have significant stability issues in that layer they need to overcome, so letting the greater ML community help them shore up the deltas in other areas is great.
"it'd still be a good investment." - that's definitely not a sure thing. Su isn't a risk taker, seems to prefer incremental growth, mainly focused on the CPU side.
Nvidia seems to pay the bulk of their engineers 200k-400k. If the fully loaded cost is 2.2, then it's closer to 440k-880k per engineer. Probably 500k would be a good number to use
and this isn't just developers, R&D and design are iterative and will require proofing, QA, prototyping -- and that means bodies who can do all of that.
Jensen never said… hey I’m going to bet it all on AI and cuda. Let’s go all in. This never happened. Both Jensen and Su are not huge risk takers imo.
Additionally there’s a lot of luck involved with the success of NVIDIA.
However, the next big looming problem for them is likely to be the shrinking market for x86 vs. the growing market for Arm etc. So they might very well have demonstrated great core competence, that ends up being completely swept away by not just one but two major industry shifts.
Mostly stick to AmdGPU as it seems to work for other stuff, I'd like to be able to run the HIP stuff on there without having to change drivers.
As you see, the technology deprecated in Visual Studio 2022. I don’t know why but I would guess people just didn’t care. Maybe because it only run on Windows.
I believe that approach, i.e. the compute shaders, is the correct thing to do because modern videogames use them a lot, the runtime support is stable and performant now. No need for special HPC-only drivers or runtime components.
Any card you would recommend, when trying to replace the equivalent of a 3090/4090?
The first time, they went ahead and killed off their effort to consolidate on OpenCL. OpenCL went terribly (in no small part because NVIDIA held out on OpenCL 2 support) and that set AMD back a long ways.
Beyond that, AMD does not have a strong software division or one with the teeth to really influence hardware to their needs . They have great engineers but leadership doesn’t know how to get them to where they need to be.
It’s been key to the success of their peers. NVIDIA and Apple are the best examples but even Intel to a smaller degree.
Nvidia is massively overvalued right now. AI has rocketed them into absolute absurdity, and it's not sustainable. Put aside the actual technology for a second and realize that public image of AI is at rock bottom. Every single time a company puts out AI-generated materials, they receive immense public backlash. That's not going away any time soon and it's only likely to get worse.
Speaking as someone that's not even remotely anti-AI, I wouldn't touch the shit with a 10 foot pole because of how bad the public image is. The moment that capital realizes this, that bubble is going to pop and it's going to pop hard.
https://www.zdnet.com/article/how-to-remove-copilot-from-you...
https://www.tomsguide.com/computing/software/how-disable-cop...
https://www.asurion.com/connect/tech-tips/turn-off-apple-int...
https://www.reddit.com/r/GooglePixel/comments/1aunsyk/how_to...
https://mashable.com/article/how-to-turn-off-gemini-gmail-go...
etc. I know these are anecdotal, but think how odd it is for there to be "how to disable" articles about any tech. I don't remember seeing similar articles about how to remove e.g. the search feature from apps. Some people are definitely against this tech.
https://www.cnn.com/2025/03/27/tech/apple-ai-artificial-inte...
HN discussion: https://news.ycombinator.com/item?id=43518576
Why isn't it sustainable? Their biggest customers all have strong finances and legitimate demand. Google and Facebook would happily run every piece of user generated content through an LLM if they had enough GPUs. Same with Microsoft and every enterprise document.
The VC backed companies and Open AI are more fragile, but they're comparatively small customers.
Amazon are on their third generation of in-house AI chips and Anthropic will be using those chips to train the next generation of Claude.
In other words, their biggest customers are looking for cheaper alternatives and are already succeeding in finding them.
.. But how much actual value derives from this?
But the "real value" would come from making adverts better targeted and more interactive. It's hard to quantity as a person outside of the companies, but the intuition for a positive value is pretty strong.
They already do this, it's opt-in.
> But the "real value" would come from making adverts better targeted and more interactive.
Is there any evidence to suggest that a transformer would be better at collaborative filtering than the current deep learning system that was custom engineered and built for this?
Lots of people don’t play the stock market or just invest in funds. It seems like just a way of challenging somebody that looks vaguely clever, or calls them out in a “put your money where your mouth is” sense, but actually presents no argument.
Anyway, if you want to short Nvidia you have to know when their bubble is going to pop to get much benefit out of it, right? The market can remain stupid for longer than you can remain solvent or whatever.
One frustrating aspect of investing is that confident information is tough to come by. It's my take that if you have any (I personally rarely do), you should act on it. So, when someone claims confidently (e.g. with adjectives that imply confidence) that something's going to happen, then that's better than the default.
I don't have the insight the claimer does; my thought is: "I am jealous. I with I could be that confident about a stock's trajectory. I would act on it."
I knew things were bad when a friend of my sister was complaining that her father(a building framer) was not able to get a loan for a 500K house, something that his colleagues had been able to get. It took another 6 months before the collapse started to hit and the banks when up.
Timing is hard.
Setting an indefinite timeline devalues any claim. You could prove this to yourself using Reductio ad absurdum, or by applying it to various general cases.
Lots of very smart people have lost a lot of money by being completely right about the destination, but wrong about the path and how long it will take to get there.
If you make a habit of this and still lose money, then either you statistically were very unlucky, or did not have a history of being right.
Anyways you'd need some kind of window of when a stock is going to collapse to short it. Good luck predicting this one.
For a short, I think you don't need that strong of a window. For an options combination, yes.
Perhaps in keeping with the broader thread here, they had only ever funded a single contract developer working on it, and then discontinued the project (for who-knows-what legal or political reasons). But the developer had specified that he could open-source the pre-AMD state if the contract was dissolved, and he did exactly that! The project is active with an actively contributing community, and is rapidly catching up to where it was.
https://www.phoronix.com/review/radeon-cuda-zluda
https://vosen.github.io/ZLUDA/blog/zludas-third-life/
https://vosen.github.io/ZLUDA/blog/zluda-update-q4-2024/
IMO it's vital that even if NVIDIA's future falters in some way, the (likely) collective millennia of research built on top of CUDA will continue to have a path forward on other constantly-improving hardware.
It's frustrating that AMD will benefit from this without contributing - but given the entire context of this thread, maybe it's best that they aren't actively managing the thing that gives their product a future!
Throwing a vast amount of effort at something isn't sufficient.
I would have said that releasing cards with 32GB+ of onboard RAM, or better yet 128GB, would have gotten things moving. They'd be able to run/train models that nVidia's consumer cards couldn't.
But I think nVidia closed that gap with their "Project Digits" (or whatever the final name is) PCs.
This happened with bitcoin.
[1]https://www.semiaccurate.com/2011/06/22/amd-and-arm-join-for...
Good choice! So many people doing that these days.
OpenCL is completely open (source) and so why wouldn't we, all of us, throw our weight behind OpenCL.
(no, I have no connection with them and have nothing to do with them, other than having learned a bit).
(Maybe "nothing special" is a little bit strong, but as a chip designer I've never seen the actual NVIDIA chips as all that much of a moat. What makes it hard to find alternatives to NVIDIA is their driver and CUDA stack.)
Curious to hear others' opinions on this.
So far those guesses haven't worked out (not surprising as they have no specific ML expertise and are not partnered with any frontier lab), and no amount of papering over with software will help.
That said I'm hopeful the rise of reasoning models can help, no one wants to bet the farm on their untested clusters but buying some chips for inference is much safer.
If it's a question of first principles, there is a small glimmer of hope in a company called tinygrad making the attempt - https://geohot.github.io//blog/jekyll/update/2025/03/08/AMD-...
If the current 1:16 AMD:NVIDIA stock value difference is entirely due to the CUDA moat, you might make some money if the tide turns. But who can say…
https://www.modular.com/blog/democratizing-ai-compute-part-5...
https://www.linkedin.com/posts/jtatarchuk_beyondcuda-democra...
Actually it might be better to spend 1B on shares and 10x 100M on development and take ten attempts in parallel and use the best of them.
That switch will reduce the NVIDIA margins by a lot. NVIDIA probably has 2 years left of being the only one with golden shovels.
Neither will encroach too much on the others turf. The two companies don't want to directly compete on the things that really drive the share price.
Every 'run cuda on AMD' project gets bought then cancelled. Every cuda alternative is understaffed and cancelled as soon as it starts to gain any traction, etc.
To me it looks like a clear attempt to not hurt each others businesses whilst being deny-able if anyone accuses them of unfair market practices/collusion.
If they wanted to prioritize this, they would. They're simply not taking it seriously.
I tend to think that AMD looks well run when compared with Intel [1], but when you consider Nvidia as the relevant counterfactual [2], things don't look so good.
I wrote about this here [3].
[0] https://news.ycombinator.com/item?id=40697341
[1] https://news.ycombinator.com/item?id=41446766
[2] https://news.ycombinator.com/item?id=39344815
[3] https://setharielgreen.com/blog/amd-also-seems-to-be-flounde...
Agree with your last statement, though. For-hire CEOs--as opposed to founder-CEOs, who often have an actual vision--take way more credit for their "work" than they deserve.
Also rofl at that "axe to grind". That one took me offline for a few seconds.
But — too late. First versions of ROCm were terrible. Too much boilerplate. 1200 lines of template-heavy C++ for a simple FFT. Can't just start hacking around.
Since then, the CUDA way is cemented in minds of developers. Intel now has oneAPI, and it is not too bad, and hackable, but there is no hardware and no one will learn it. And HIP is "CUDA-like", so why not CUDA, unless you _have to_ use AMD hardware.
Tl;dr first versions of ROCm were bad. Now they are better, but it is too late.
If I recall, there are various "GPU programming" and "AI" efforts that have existed for AMD GPUs, but none of them have had the same success in large part because they're simply non-"standard?"