I can't see the cloud providers being happy about this, it whitelabels away their branding and customer experience flows.
It puts nvidia on both the vendor and customer side of the relationship, which seems odd
It puts nvidia on both the vendor and customer side of the relationship, which seems odd
Nvidia has the most desirable chips in the world, and their insane prices reflect that. Every hyperscaler is already massively incentivized to build their own chips, find some way to take Nvidia down a peg in the value chain.
Everyone in the world who can is already coming for Nvidia’s turf. No reason they can’t repay the favor.
And beyond just margin-taking, Nvidia’s true moat is the CUDA ecosystem. Given that, it’s hugely beneficial to them to make it as easy as possible for every developer in the world to build stuff on top of Nvidia chips — so they never even think about looking elsewhere.
It almost sounds like you're cheering on Nvidia, framing it as "everyone else trying to reduce the value of Nvidia", meanwhile they have a long, long history of closed-source drivers, proprietary & patented cost-inflated technology that would be identical if not inferior to alternatives - if it weren't for their market share and vendor lock-in strategies.
"Well, what are they gonna do about it?" When dealing with a bully, you go find friends. They're going to fund other chip manufacturers and push for diversity, fund better drivers and compatibility. That's the best possible future anyone could hope for.
1. "Identical if not for market share" is a complete contradiction when what we're talking about is the network effect of CUDA
2. What vendor lock in? What are you talking about? They have a software and compiler stack that works with their chips. How is that lock in, that's literally just their product offering. In fact the truth is you can compile CUDA for AMD (using hipify) and guess what - the result sucks because AMD isn't a comparable alternative!
You can compile x64 to ARM and performance tanks. Does this means ARM isn't a comparable alternative to x64?
It just means their software works badly with said architecture. Could be that AMD acceleration is horrible (but then the FSR would be worse) or it could be that it's just different, or the translation layer is bad.
There's no translation layer - you don't understand how/what hipify and CUDA are. CUDA is a C/C++ extension and it connotes APIs. 90% of CUDA kernel code (ie the stuff that actually runs on the SMs) does compile for AMD without any changes (intrinsics diff). hipify goes the extra step of remaining APIs to their HIP variants.
Again, all of this is to say there's no vendor lockin like clueless whiny people complain and just a superior product.
If Nvidia is better at AI tasks and is superior. Great. Maybe they can finally leave GPU field.
I'm gonna say it again, loud and clear: you don't have any understanding of what you're saying and 90% of the kernel code is exactly the same, transferrable, compilable ie it's just cpp.
Nothing you said prevents API makers of biasing their API to favor one hardware platform over the other.
EDIT: Which CUDA to AMD GPU translation project are you referring to? AMD's original efforts or ZLUDA?
The issue here isn't as much as nVidia as it is nVidia fanboism and intellectually dishonest argument.
What determines the speed of an algorithm, all things being similar, is the raw power of hardware underneath. For that, you use the device drivers, use their equivalent level (e.g. low level on both) APIs and let them rip. You want to have as equal as comparison as you want.
What I don't expect is to take nVidia drivers, load them onto the AMD graphics card and then when the thing glitches out or underperforms say - see, it's bad.
The fact is that Hipify on AMD isn't the fastest way to run CUDA code on AMD anymore. Not since ZLUDA was created. Which raises unfortunate implications. Why wasn't Hipify able to reach the same performance? Maybe because it's a shitty translation layer. Who knows?
> I don't use Apple, but I don't complain abt them.
Just because you don't use them, doesn't mean they don't negatively impact the world in a huge way. Looks at the app store, Apple's penchant for proprietary charges, and the constant phone upgrade treadmill.
With the Nvidia solution you have at least another option. Vendor agnostic, but Nvidia lock in.
If most ML startups, one hyper scaler and at best also AMD, would go with one common backend, then it might get enough traction to become *the* standard.
Closed source?? Who cares? It's their own products. Vendor lock in? It's their own chips man. You wouldn't expect Nvidia to develop software for AMD chips would you? That would be insane. I would not do that.
Their tech is superior to everybody else's and Jensen keeps pulling rabbits out of a hat. I hope they keep going strong for the next decade.
Because they are an amoral mass that seeks to make profit and has turned GPU market into a cluster fuck?
> Their tech is superior to everybody
Their only saving grace is CUDA, and DLSS, their hardware has been overvalued for quite some time.
They have a nice software stack but the hardware is overvalued
I mean, we could probably not cheer about big techs that routinely do shady things - or straight illegal things - for their own profit knowing they won't face consequences - or very light ones
To be fair to Apple their hardware was always overpriced. Their deal is hardware + software combo.
It is often such a strange thing to see this on HN. From Software developers.
Their Hardware's value is derived from their Software. And Software for GPU is insanely hard. Both the driver and CUDA.
As Jensen once said, their Goal is to make the TCO ( Total Cost of Ownership ) so good, that even if their competitor were selling at cost of giving their GPU away from free they still would not be able to compete with them.
There will also come a point, may be in the next 2-3 years where the volume and margin of those GPU are so good they will be the second in line to take all the Fab capacity for larger die size on leading node. i.e They will always be one node ahead of their competitors. And when that happens both hardware and software will be ahead of everyone else.
As a developer, I have managed to stay outside their (nVidia and Apple both) moats. And what I've seen, as a consumer, has left me wanting. Granted m* battery life is impressive, but I'm not that much of a laptop person.
But I'd love for someone to enlighten me how a 16GB RAM upgrade with $200 dollar tag is any way normal.
> Their Hardware's value is derived from their Software
Their value is derived from their lock-in. If you bought into it, then yeah, it's going to be difficult to switch. OTOH, if you didn't, then there is almost no value.
> As Jensen once said
As Todd Howard once said - Sixteen times the detail![0]
Anecdotes aside, how is that working for nVidia? Oh, they just blackmailed GPU reviewers[1] and their GPU drivers randomly flicker, and cause kernel reboots[2]? Yeah. I definitely feel the TCO getting good, maybe even burnt. Much like their 12VHPWR connections.
But maybe they will fare much better on B2B, I couldn't tell you or care much about it. I honestly wish them a very SGI-experience. And seeing how they weathered the last craze (see cryptocurrency), I wouldn't bet my livelihood on it.
[0]https://www.youtube.com/watch?v=r3rXKCT_STM
I dont like company X, their product must be shit.
It seems most people dont value product quality anymore.
it is true, but also not. nvidia is certainly producing a chip that nobody else can replicate (unless they're the likes of google, and even they are not interested in doing so).
The CUDA moat is the same type of moat as intel's x86 instruction set. Plenty of existing programs/software stack have been written against it, and the cost to migrate away is high. These LLM pipelines are similar, and even more costly to migrate away.
But because LLM is still immature right now (it's only been approx. 3 yrs!), there's still room to move the instruction set. And middleware libraries can help (pytorch, for example, has more than just the CUDA backend, even if they're a bit less mature).
The real moat that nvidia has is their hardware capability, and CUDA is the disguised moat.
AMD, Intel?
Nobody can touch it. Then that's just the hardware. The software is so much better on Nvidia. The width and breadth of their offering is great and nobody is even close.
>Ultra Accelerator Link (UALink) is an open specification for a die-to-die interconnect and serial bus between AI accelerators. It is co-developed by Alibaba, AMD, Apple, Astera Labs,[1] AWS, Cisco, Google, Hewlett Packard Enterprise, Intel, Meta, Microsoft and Synopsys.[2]
I suppose this can change.
There is an inmense amount of work behind the cuDNN libraries that outsiders keep ignoring.
These sort of high performance kernels are co-developed in very close collaboration with the hardware architects designing the chip. Speaking of the hardware in isolation of the high performance libraries reveals a deep misunderstanding of how the system was built. This is true of any mature vendor, not just Nvidia.
Ironically, the most effective thing they can do is probably haul up the AMD rep and yell at them.
Just took a random server: https://instances.vantage.sh/aws/ec2/m5d.8xlarge?duration=mo... - to get a decent price on it you need to commit to three years at $570 per month(no storage or bandwidth included). Over the course of 3 years that's $20520 for a server that's ~10K to buy outright, and even with colo costs over the same time frame you'll spend a lot less, so not exactly crushing those margins to dust.
Cloud bills can be written off in the month in which they are paid; while buying hardware has to be depreciated over years.
> "Nvidia DGX Cloud Lepton connects our network of global GPU cloud providers with AI developers," said Jensen Huang, chief executive of Nvidia in a statement. The news was announced at the Computex conference in Taiwan.
Sounds like a preferred developer resource. The target audience isn't the usual cro-mag that wants to run LLM's for food.
That alone means many users will want to use Nvidia hardware even at a decent price premium when the alternative is an extra few months of engineering time in a very fast moving market.
[0] https://docs.pytorch.org/xla/release/r2.7/learn/xla-overview...