Things are turning around for AMD. If you have an AMD card, go to pytorch.org, click Linux+ROCm and install PyTorch. 3 years ago, this was hopeless. Today, most mainline things work. I ran nanochat on MI300X and it just worked. I think that's true about MI350X now too. The MI350X machine is stable.
They are clearly behind NVIDIA, nobody doubts that. And a lot of investment into software will be required to catch up, ecosystem, compiler, and driver. But 2 years ago they seemed hopeless, now they don't. Things take time. HipKittens is a great codebase to study to see where AMD's LLVM backend is still lacking; compare it to the CUDA Kittens.
For training, it's NVIDIA and Google in first. AMD in second. And nobody in third. Intel and Tenstorrent are not remotely close. Huawei examples segfaulted. Groq gave up selling chips. Cerebras isn't available anywhere. Trainium had a 5 day wait time to get one instance and I lost interest.
The out of box experience can be a bit rough around the edges on bleeding edge stuff, but it isn't anything near as bad as it used to be. For example, a month ago nanochat wasn't working well and now it is. The important thing is that people now care enough to make it work.
At the end of the day, AI does need viable options. Having a monopoly on all AI hardware and software might be a good thing for share holders, but isn't a good thing for what is looking like a fundamental technology, akin to the internet.
I like your bet though. The difference between NVDA and AMD has never really existed on a hardware level for decades. AMD has always been on par, and software is software, it will catch up.
AMD will be a stock many people will miss because the opportunity has presented itself at the height of AI bubble talk, and this will leave many in the dust. Doubling and tripling of their market cap is pretty much a forgone conclusion.
1. data layouts to avoid local memory bank conflicts
2. read patterns from global memory to optimize L2 cache reuse
3. warp specialisation
tinygrad doesn't support 3 yet, it's not needed on any AMD GPUs, and not needed on NVIDIA consumer. It wouldn't be hard to add, but it's important to figure out how it best fits with the existing abstractions. I think everything will eventually move to a more producer-consumer model.
Right now AI support on AMD is officially only on specific models. But they are working hard to turn this around to have broader support. And making progress.
That sounds like they're winning.
Competitors now only need to optimize for a narrow set of algorithms. If a vendor can run vLLM and Transformers efficiently, a massive market becomes available. Consequently, companies like AMD or Huawei should be able to catch up easily. What, then, is Nvidia’s moat? Is InfiniBand enough?"
Nvidias valuation and moat are centered around data center class GPUs used for training. I don't think they effectively have that space to themselves for much longer. Google is already using their own TPUs at scale for both training and inference. They still use some Nvidia stuff but they seem to be able to keep that off the critical path for anything that needs to run at "Google scale". OpenAI just ordered a bunch of AMD hardware. A lot of AI engineers use Apple laptops that rely on the M series hardware.
In short, the Cuda moat is shrinking. It's still relevant of course and there are a lot of tooling and frameworks that depend on it. That's why everybody still uses it. But not exclusively. And there's a lot of extremely well funded and active development to cut loose from it. AMD of course wants in. So does Intel. And so does everybody else. This HipKittens thing looks like it makes some big steps towards a more neutral software ecosystem.
There's a ton of pressure on the market to decouple nvidia's proprietary software from literally everything important to AI, and they will either gracefully transition and control it, or it will reach a breaking point and someone else will do it for (and to) them. I'm sure they've got finance nerds and quants informing and minmaxing their strategy, so they probably know to the quarter when they'll pivot and launch their FOSS, industry leading standards narrative (or whatever the strategy is.)
Apple didn’t really “win” out against Android, and it would be a very wrong way of measuring what actually happened. Yet, Apple could have been seen as more premium during various points of that timeline. The truth of the matter was, it was never a swimming race at any point in that smartphone timeline. It was simply a flood that you could convince yourself was an orderly race.
I believe the same is happening now, and it’s in Nvidias interest to maintain the narrative that there is a race and they are winning it. Believing something like this during the smartphone era would have been foolish.
Plus strategic partnerships with cloud providers.
And InfinityBand, yes
For example, many companies do well by selling a less capable but more affordable and available product.
AI is millions of times slower than optimal algorithms for most things.
HipKittens is an improvement but AMD does not have the ability to understand or track kernel performance so it'll be ignored.
This isn't fixable overnight. Company-wide DevOps and infrastructure is outsourced to TCS in India who have no idea what they're doing. Teams with good leadership maintain their own shadow IT teams. ROCm didn't have such a team until hyperscalers lost their shit over our visibly poor development practices.
Even if AMD did extend an offer to hire all the people in the article, it would be below-market since the company benchmarks against Qualcomm, Broadcom, and Walmart, instead of Google, Nvidia, or Meta.
We haven't had a fully funded bonus in the past 4+ years.
This is WILD to hear considering how well it appears AMD is executing from the outside.
Yes, this is true. Painfully true.
They've paid serious amounts in RSUs over the last six years. Not top of market by any stretch but firmly in the category of engineers don't care what the steak costs. Bonus might be team dependent, I remember being annoyed and nicely surprised by it in different years.
The aql profiler confuses me quite a lot but it's definitely a tool for measuring performance.
It's not only not fixable overnight, but it's not fixable at all if the leadership thinks they can coast on simply being not as bad as Intel, and Intel has a helluva lot of inertia and ability to simply sell OEM units on autopilot.
Sounds like the AMD board needs to get their heads out of their asses and shake up leadership.
Well said, their Instinct parts are actually, at a hardware level, very very capable pieces of kit that - ignoring software/dev ecosystem - are very competitive with NVidia.
Problem is, AMD has a terrible history of supporting it's hardware (either just outright lack of support, cough Radeon VII; or constantly scrapping things and starting over and thus the ecosystem never matured) and is at a massive deficit behind the CUDA ecosystem meaning that a lot of that hardware's potential is squandered by the lack of compatibility with CUDA and/or a lack of investment in comparable alternative. Those factors has given NVidia the momentum it has because most orgs/devs will look at the support/ecosystem delta, and ask themselves why they'd expend the resources reinventing the CUDA wheel to leverage AMD hardware when they can just spend that money/time investing in CUDA and NVidia instead.
To their credit, AMD it seems has learned it's lesson as they're actually trying to invest in ROCm and their Instinct ecosystem and seem to be sticking to their guns on it and we're starting to see people pick it up but they're still far behind Nvidia and CUDA.
One key area that Nvidia is far ahead of AMD on in the hardware space is networking.
AMD hires talented people at below-market and doesn't promote them or give raises. This causes employees to aim at resume-driven development by reinventing the wheel so they can get a job somewhere else.
It's a similar problem to Google, except at Google it's because promotions are explicitly for people that ship new products.
ROCM pre Rock, suffers from the ossification in the engineering organization. The Rock seeks to completely change that, and the team driving it is amazing. Try out the pre-alpha installer. It is already better than the default installer.
There is hope.
That’s a complete institutional and leadership failure.
Ironically, building chips is the actual _hard_ part. The software and the compilers are not trivial but the iteration speed is almost infinite by comparison.
It goes to show that some companies just don’t “get” software. Not even AMD!
I think I may need to reduce the number of architectures it's built for to successfully compile it on the official Debian buildd infrastructure, but my (unverified) understanding is that most of its reverse dependencies only need the header-only parts of the library anyway.
I'm told they're working on improving the build times via a few different methods.
I also use OOMD, but I have to work on separating my systemd units better, OOMD has killed my greetd session before, and with that my entire tree of userland processes :D
For example, the following laptop which I'm thinking of picking up, has both a strong AMD CPU/IGPU and a RTX 5080. Could we see the AMD side competing with the RTX?
I know a dedicated gpu will always be faster though.
>HP OMEN MAX 16-ak0003nr 16" Gaming Laptop Computer - Shadow Black Aluminum AMD Ryzen AI 9 HX 375 (2.0GHz) Processor; NVIDIA GeForce RTX 5080 16GB GDDR7; 32GB DDR5-5600 RAM; 1TB Solid State Drive
It's not quite as fast as like Sonnet 4 from an API, but it's really not that bad.
It's really great for quick questions so I don't have to google stuff, and it's probably Sonnet4 level of competency at achieving coding tasks.
No API served model has been fast enough to remove the urge to do something else while waiting for bigger tasks, so the UX is more or less the same in that regard.
Opencode + ollama + Qwen3 Coder has been a very reasonable alternative to ClaudeCode with Sonnet4.
That is amazing for something running locally.
It is possible that if you actually need AI to be doing all your coding, that you're going to feel differently about the setup. But as a small assistant it's great.
https://www.lenovo.com/us/en/p/laptops/thinkpad/thinkpadp/th...?
AMD Ryzen™ AI 9 HX PRO 370 Processor (2.00 GHz up to 5.10 GHz) Operating System Windows 11 Pro 64 Graphic Card Integrated AMD Radeon™ 890M Memory 64 GB DDR5-5600MT/s (SODIMM)(2 x 32 GB)
But I also seriously want to run LLMs. My hunch is a gaming laptop is the best way to do this on the go without spending 5000$ for a Thinkpad with a high end graphics card.
I think this is a port of that to HIP, where generally ports of cuda things to hip are of vague professional interest, but much more so if the library is used by other things.
Raw assembly vs cooked assembly?
Also, I think this attitude wasn’t the most common on CPUs, and people used to write assembly by hand just fine (and sometimes some still do). I think we shouldn’t be afraid of assembly like that.
Compilers could write that assembly in the end, just like the do for CPUs!
I also do wonder what 'raw assembly' is supposed to be. Is it like sushi? Perhaps it is left as future work in the paper for the authors to answer.