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The thought expressed in the title came to my mind when I saw Nvidia described as an "AI company" in the press recently...
An object is what it does. NVIDIA is making the most money through AI, so that's what it is now to the market
The hardware is heavily optimized for low precision matrix math, pretty much only used for AI.
Nvidia is selling hardware. What the buyers are doing with it doesn't change anything about Nvidia.
A company selling knives is not considered a butcher or cook, despite the main uses of knives being just that.
Just like it was a crypto company. Its a computational fad chaser.
Next up: quantum. And that will be the end of them.
Indeed, why would they not call themselves NvidAI to begin with. This company has twice already been super lucky to have their products used for the wrong thing (given GPUs were created to accelerated graphics, not mining or inference)
3 times, if you count the physics GPGPU boom that Nvidia rode before cryptocurrencies.
And other than maybe the crypto stuff, luck had nothing to do with it. Nvidia was ready to support these other use cases because in a very real way they made them happen. Nvidia hardware is not particularly better for these workloads than competitors. The reason they are the $4.6T company is that all the foundational software was built on them. And the reason for that is that JHH invested heavily in supporting the development of that software, before anyone else realized there was a market there worth investing in. He made the call to make all future GPUs support CUDA in 2006, before there were heavy users.
I don't think it's luck. They invested in CUDA long before the AI hype.
They quietly (at first) developed general purpose accelerators for a specific type of parallel compute. It turns out there are more and more applications being discovered for those.
It looks a lot like visionary long term planning to me.
I find myself reaching for Jax more and more where you would have done numpy in the past. The performance difference is insane once you learn how to leverage this style of parallelization.
Or that parallel computing is immensely useful in general and that more use cases will be found for it in the future beyond AI.
At some point, maybe it isn’t luck anymore but a general trend towards parallel computing.
to be fair, the percentage of their revenue derived from ai-related sales is much higher now than before. Why is that not accurate?
GN did a video a few weeks ago in which they were showing a slide from Nvidias shareholder meeting in which it was shown that gaming was a tiny part of Nvidias revenue.
Basically, almost half of their revenue is pure profit and all of that comes from AI.
While the slide looked a lot nicer, the data is also available on their site https://nvidianews.nvidia.com/news/nvidia-announces-financia...
Just because customers use their hardware for AI does not mean the hardware maker is an AI company.
This is similar to evolution. Evolution repurposes old systems for newer tasks. The GPU name is stuck but it has been deployed for AI.
I mean afaik the consumer GPUs portion of their business has always been tiny in comparison to enterprise (except to begin with right at the start of the company's history, I believe).
In a way it's the scientific/AI/etc enterprise use of Nvidia hardware that enables the sale of consumer GPUs as a side effect (which are just byproducts of workstation cards having a certain yield - so flawed chips can be used in consumer cards).
No, gaming revenue for NVIDIA was historically the major revenue percentage from the company (up until 2023). Only with the recent AI boom this changed.
Source (I am not sure how reliable this is because I got this from ChatGPT, but I remember seeing something similar from other sources): https://www.fool.com/investing/2024/02/12/gaming-was-nvidias....
Nvidia started as a gaming company and gaming was the majority of their business until the last 5-10 years.
This is cool! I love this kind of simulation GPU programming stuff. Reminds me of this awesome talk from Peter Whidden: https://youtu.be/Hju0H3NHxVI?si=V_UZugPSL9a8eHEM
Not as technicial but similarly cool.
This is awesome. It also brought back some anxiety from >10 years ago in college that reminds me that computer graphics and my brain do not agree whatsoever.
Everything's just triangles and numbers, and my brain's no good with numbers. Linear algebra I can do though.
Graphics is trivial until you get to shadows and lighting. Then all the simple tricks stop working.
Do most GPUs made for AI even have a graphical output buffer and a video output any more?
They've been aggressively removing or reducing hardware that's vestigial from the perspective of AI. NVIDIA's Hopper has no display outputs, no raytracing hardware, no video encoders, and only one of the eight GPCs has raster graphics functionality; the rest are compute-only. With their newer Blackwell parts, going from B200 to B300 they cut out almost all FP64 and INT8 capabilities so they could squeeze in more FP4 throughput.
Yes still but perhaps not needed in next iteration when we just approximate the graphics pipeline with matrix multiplications
You can game on H100 GPUs, it is terrible though. Someone has tested it and it is on the level of a Radeon 680M, that is the performance of a typical business laptop.
I know that the NVIDIA H100 chips don't, other than those however I'm not too sure, I'd assume that that'd be the case though, no point adding extra tech you aren't gonna be using in a big datacenter.
Holy tangents, Batman! This whole post was a million interrelated topics woven into one semi-coherent textbook.
Room for new competitors then? Surely Nvidia/AMD/Intel are not the only graphics vendors? Or is the tech too hard to even enter the market?
Dedicated GPU are dead for general computing. The whole market converged on APU because they are simply more efficient.
There is plenty of competition there: Qualcomm, Samsung, Apple, MediaTek and of course Intel and AMD, and things are moving fast. The best phone APUs nowadays are more powerful than my not so old MacBook Air M1.
There are a whole raft of other GPU companies out there (Broadcom, MediaTek, PowerVR, Samsung, Qualcomm, ...), but none of them interested in the classic PC gaming space.
And I'm not sure that space has been economical for a long time. Integrated GPUs have more-or-less reached a point where they can handle PC games (albeit not at the latest-and-greatest resolutions/frame-rates/ray-tracing/etc), and the market for multi-thousand-dollar dedicated GPUs just isn't very big
Then who actually delivers on that front aside from AMD? Intel does deliver but only on the low to mid range.
I wonder if they'll ever start doing the graphics with AI.
Could you render the scene in the simplest most efficient way possible, then train a special model that takes that picture, along with the coordinates of lights, depth maps, text descriptions of materials, etc, and adds detail?
Isnt this DLSS and Frame Gen in a nutshell?
DLSS - Render at low resolution, use an ML model to upscale
Frame Gen - Render a frame, use an ML model to generate the next frame. Render the next, gen the next....so on
(I think in practice, Frame Gen is more complicated due to fluctuation between FPS but that's another can of worms)
Naming may provide useful hints about some utility of a tool but naming does not bound the utility of a tool.
They used to refer to it as GPGPU (general purpose) but they just shortened it maybe 10 years ago?
What does Bechara means?
DuckDuckGo doesn't help me much. "Poor thing" maybe?
Anyway, I think you should change friends. Or study subjects. Either way.
Btw, these look like parasites or worms under microscope.
Should change the name to Matrix Processing Units
Nice texture generator came out of this, with seems to be perfectly looped images! Well done!
Generator
> And here are my clearly unimpressed “friends” >:(
These friends don't get it!
I read somewhere that CPUs are better at generating graphics than GPUs (although I imagine much slower). Is that true? Does that explain why GUI libraries like Egui are so much uglier than, for example, Iced?
The main context where I've seen claims that CPUs are 'better' at graphics is where a render that looks precisely right and has the desired image quality is more important than a fast render.
Even that is more about control over the rendering process than what silicon is doing the work. With a lot of (older?) graphics APIs, you're implicitly giving the GPU's driver a lot of control over the output and how it is generated. This is how we got events like [0] where GPU vendors would make their drivers 'cheat' on benchmarks by trading image quality for speed when certain criteria were detected.
I imagine that tradeoff has changed somewhat as the industry has moved towards graphics APIs intended to give the programmer more direct control of the hardware.
[0] https://www.reddit.com/r/quake/comments/168btin/does_anyone_...
What exactly does "better" mean if not faster?
What does "better" mean?
The simple calculations typically used for rendering graphics can easily be parallized on the GPU, hence it's faster. But the result should be identical if the same calculations are done on the CPU.
Also GUI frameworks like iced and egui typically support multiple rendering backands. I know iced is renderer agnostic, and can use a number of backands including the GPU graphics APIs Vulkan, DX12 and Metal.
Since when a name dictates function?
The G now stands for GenAI.