I'm curious: Do you think there is a realistic chance for another chip maker to catch up and overtake NVidia in the AI space in the next few years or is their lead and expertise insurmountable at this point?
Maybe in some cases, but that doesn't even really matter since hardware support is poor.
> Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE).
From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend.
[1] https://github.com/openxla/stablehlo
Other backends are also available, such as CPU-only training. And you can export networks in reasonably-standard formats.
nvidia's moat is much more mature framework support than AMD's cards; widespread popularity due to that good framework support, ensuring everyone develops on nvidia, thus maintaining their support lead; much faster performance than CPU-only training; and a price that, though high, is a lot less than an ML developer's salary.
If you need 24GB of vram and nvidia offers that for $1600 while AMD offers it for $1300, how many compatibility problems do you want to deal with to save a single day's wages?
But nvidia's moat is far from guaranteed. Huge users like OpenAI and Facebook might find improving AMD support pays for itself.
At that scale they may actually develop their own hardware a la Google TPU.
If you want to just focus on the AI problem and not on infrastructure, just use NVidia. If you want control and efficiency, design your own. AMD kind of falls in a weird middle ground with respect to the massive companies.
> Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE).
From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend.
[1] https://github.com/openxla/stablehlo
Not that anyone cares, and everyone keeps using CUDA while simultaneously complaining about Nvidia GPU prices, as if those two things have nothing to do with each other...
(There are some annoying differences in the low-level implementations of OpenCL vs. Vulkan Compute, due to their being based on SPIR-V compute "kernels" vs. "shaders" respectively, that make it hard for them to interop cleanly. So that's why the choice can be significant.)
I did a bit of work in OpenCL almost 10 years ago, and found it decently portable on a range of NVIDIA GPUs as well as Intel iGPUs. On the high end I used something like the Titan X while on the low end it was typical GPUs found in business class laptops.
But my limited exposure to AMD was terrible by comparison. Even though I am away from that work now, I still tend to try to run "clpeak" and one of my simpler image processing scripts on each new system. And while I liked a Ryzen laptop for general use or even games, it seemed like OpenCL was useless there. It seemed my best option was to ignore the GPU and use Intel's x86_64 SIMD OpenCL runtime.
Also my fractal software incl OpenCL multi-GPU / mixed plaftorm rendering: https://chaoticafractals.com/
Both work on [ Nvidia, AMD, Intel, Apple ] x [ CPU, GPU ].
Some of the shared code here: https://github.com/glaretechnologies/glare-core
Don't let anyone tell you OpenCL is dead! Keep writing OpenCL software!!
Only C, C++ and Fortran were never taken seriously enough, other language stacks never considered.
Thus everyone that enjoyed programming in anything not C, with great libraries and graphical debuggers flocked to CUDA, now remains to be seen if SYCL and SPIRV will ever matter enough to regain some of those folks back.
There's relatively few people capable of implementing these frameworks without a solid cuda-like foundation, and those that do exist would need a very strong incentive to do it.
Nothing is insurmountable. :)
https://en.wikipedia.org/wiki/The_Innovator's_Dilemma
>It describes how large incumbent companies lose market share by listening to their customers and providing what appears to be the highest-value products, but new companies that serve low-value customers with poorly developed technology can improve that technology incrementally until it is good enough to quickly take market share from established business.
Given how large the prize is, the next chapter of chip development is likely to be nvidia vs state sponsored projects. China, in particular, will funnel further resources into acquiring this technology by any means necessary, including (more) industrial sabotage and outright theft. It's going to be interesting to see how this will play out. Up until a few years ago China was viewed as being a formidable competitor for projects of this nature, but as the country has moved to become increasingly authoritarian, so too have its decision making and execution declined in quality.
AMD is trying to catch up too, so far Nvidia still remains to be the leader, a few years ahead.
So if TPU clusters are priced right...