It seems like pure management incompetence to me. They need to invest a whole lot more in software, integrating their stuff directly into pytorch/TF/XLA/etc and making sure it works on consumer cards too. The investment would be paid back tenfold. The market is crying out for more competition for Nvidia and there's huge money to be made on the datacenter side but it all needs to work on the consumer side too.
Their attempts at entering the ML space so far have been failures, and they are wise to hold off on really competing with Nvidia until they have the bandwidth to go “all in”. Consciously NOT trying to compete with Nvidia is the reason they didn’t go bankrupt. Their Radeon division minted from 2016-2020 because they focused on a niche Nvidia was neglecting- low-end/eSports (also leveraging their APU expertise to win PS4/Xbox contracts).
I think Nvidia will eventually lose its monopoly on ML/AI stuff as AMD, Apple, Qualcomm, Amazon and Google chip away at their “moat” with their own accelerators/NPUs. As mentioned though, the Nvidia Edge really comes from CUDA and other software, not the hardware. I doubt that Apple, Qualcomm, Amazon or Google will be interested in selling hardware direct to consumers. They want that sweet, sweet cloud money and/or competitive advantages in their phones (like photo processing). I don’t want to be paying AWS $100/mo for a GPU I could pay $600 once for. I do think AMD/RTG will go hard on Nvidia eventually, and it will not matter whether you have an AMD or Nvidia GPU for Tensorflow or spaCy or whatever else.
no, they need a product good at training and gpu compute at a reasonable price
that product doesn't need to be good at rendering, ray tracing and similar
sure students and some independent contractors probably love getting both a good graphic card and a CUDA card in one and it makes it easier for people to experiment with it but company PCs normally ban playing games on company PCs and the overlap of "needing max GPU compute" and "needing complicated 3D rendering tasks" is limited.
through having 1 product instead of two does make supply chain and pricing easier
but then 4090 is by now in a price range where students are unlikely to afford it and people will think twice about buying it just to play around with GPU compute.
So e.g. the 7900XTX having somewhat comparable GPU compute usability then a 4080 would have been good enough for the non company use case, where a dedicated compute-per-money cheaper GPU compute only card would be preferable for the company use case I think.
1) Consumer Nvidia GPU cards on custom PCs
2) Self hosted shared server
3) Cloud infrastructure.
There is no "GPU compute only card" that is widely used outside servers.
> company PCs normally ban playing games on company PCs and the overlap of "needing max GPU compute" and "needing complicated 3D rendering tasks" is limited.
The "don't play games thing" isn't a factor. Most companies just buy a 4090 or whatever, and if they have to tell staff not to play games, they say "don't play games". Fortnight runs just fine on pretty much anything anyway.
https://www.amd.com/en/graphics/servers-solutions-rocm-ml
> For this they need a product comparable to NVIDIA 4090, so that entry level researchers could use their hardware.
Why is a high end product a requirement for entry level research?
Also, ROC-M is a bit of a mess to setup. With Nvidia i just need to install cuda, cudnn and then pip install tensorflow/pytorch.
Are these programmable by the end-user? The "software programmability" section describes "Vitis AI" frameworks supported. But can we write our own software on these?
Is this card FPGA-based?
EDIT: [1] more info on the AI-engine tiles: scalar cores + "adaptable hardware (FPGA?)" + {AI+DSP}.
[1] https://www.xilinx.com/products/technology/ai-engine.html
It's possible that AMD could have reworked an existing Xilinx design to incorporate RDNA chiplets in place of some of the FPGA-gate-grid chiplets, creating a heterogeneous mesh; but I find it just as likely that AMD just took their VLSI for an RDNA core and loaded it onto the existing FPGA.
EDIT: 75W is a smaller card than I expected. "Inference" also usually means "cheaper". so maybe we can be optimistic with $5000-ish ??
Anyone shocked by the price, remember that this is an FPGA-line from Xilinx. Not a GPU from Radeon. Expect very high prices.
> [...]
> **: @10 fps, H.264/H.265
Is 10 fps a standard measure for this kind of thing?
[1] You could skip the last P frame before an IDR frame, but that doesn't buy you much.
Who owns lattice?
In my experience, mostly a marketing number, higher TOPS doesn't actually mean it'll be faster than something with a lower TOPS.
As always, you need to do your own benchmarks with your use case in mind.
Hopefully Intel takes a stab at it with their ARC line out now.
16gb RAM / 96 video channels ... I haven't done any of that work but it feels like they expect that "96" not to be fully used in practice.
As a joke I sometimes tell people the automatic flushing toilets in public bathrooms work by having a little camera monitored by someone in a 3rd world country who remotely flushes as needed, while monitoring a whole lot of video feeds. They usually don't buy it, but will often acknowledge that our world is uncomfortably close to having stuff like become reality.
