Nvidia literally markets H100 as a "GPU" (https://www.nvidia.com/en-us/data-center/h100/) even though it wasn't built for graphics and I doubt there's a single person or company using one to render any kind of graphics. GPU is just a recognizable term for the product category, and will keep being used.
I assume someone is doing rendering on them given the OpenGL support. In theory, you could do rendering in CUDA, although it would be missing access to some of the hardware that those who work with graphics APIs claim is needed for performance purposes.
It's a good question. I'll note that, even in the GPGPU days (eg BrookGPU), they were architecturally designed for graphics applications (eg shaders). The graphics hardware was being re-purposed to do something else. It was quite a stretch to do the other things compared to massively-parallel, general-purpose designs. They started adding more functionality to them, like physics. Now, tensors.
While they've come a long way, I'd imagine they're still highly specialized compared to general-purpose hardware and maybe still graphics-oriented in many ways. One could test this by comparing them to SGI-style NUMA machines, Tilera's tile-based systems, or Adapteva's 1024-core design. Maybe Ambric given it aimed for generality but Am2045's were DSP-style. They might still be GPU's if they still looked more like GPU's side by side with such architectures.
GPUs have been processing “tensors” for decades. What they added that is new is explicit “tensor” instructions.
A tensor operation is a generalization of a matrix operation to include higher order dimensions. Tensors as used in transformers do not use any of those higher order dimensions. They are just simple matrix operations (either GEMV or GEMM, although GEMV can be done by GEMM). Similarly, vectors are matrices, which are tensors. We can take this a step further by saying scalars are vectors, which are matrices, which are tensors. A scalar is just a length 1 vector, which is a 1x1 matrix, which is a tensor with all dimensions set to 1.
As for the “tensor” instructions, they compute tiles for GEMM if I recall my read of them correctly. They are just doing matrix multiplications, which GPUs have done for decades. The main differences are that you do not need need to write code to process the GEMM tile anymore as doing that is a higher level operation and this applies only to certain types introduced for AI while the hardware designers expect code using FP32 or FP64 to process the GEMM tile the old way.
How long until a "PC" isn't CPU + GPU but just a GPU? I know CPUs are good for some things that GPUs aren't and vice versa but... it really kind of makes you wonder.
Press the power button, boot the GPU?
Surely a terrible idea, and I know system-on-a-chip makes this more confusing/complicated (like Apple Silicon, etc.)
Never. You can to a first approximation model a GPU as a whole bunch of slow CPUs harnessed together and ordered to run the same code at the same time, on different data. When you can feed all the slow CPUs different data and do real work, you get the big wins because the CPU count times the compute rate will thrash what CPUs can put up for that same number, due to sheer core count. However, if you are in an environment where you can only have one of those CPUs running at once, or even a small handful, you're transported back to the late 1990s in performance. And you can't speed them up without trashing their GPU performance because the optimizations you'd need are at direct odds with each other.
CPUs are not fast or slow. GPUs are not fast or slow. They are fast and slow for certain workloads. Contra popular belief, CPUs are actually really good at what they do, and the workloads they are fast at are more common than the workloads that GPUs are fast at. There's a lot to be said for being able to bring a lot of power to bear on a single point, and being able to switch that single point reasonably quickly (but not instantaneously). There's also a lot to be said for having a very broad capacity to run the same code on lots of things at once, but it definitely imposes a significant restriction on the shape of the problem that works for.
I'd say that broadly speaking, CPUs can make better GPUs than GPUs can make CPUs. But fortunately, we don't need to choose.
