8 ms·
They have been investing in this for a long time, CUDA is 15 years old. That said, they have plenty of competition now and that's only going to increase. - App
by danpalmer 4y ago
They have been investing in this for a long time, CUDA is 15 years old. That said, they have plenty of competition now and that's only going to increase.
- Apple have got their own chips now for mobile AI applications for iPhones.
- Google have got their own chips for Android and servers.
- AMD are gaining on Intel in the server space, and have their own GPUs, being able to sell CPUs and GPUs that complement each other may be a good strategy, plus AMD have plenty of their own experience with OpenCL.
- bottlepalm 4y agoThey've been investing for a long time, but it's only blown up in the past year due to recent breakthroughs. Good timing for Nvidia.
- jonas21 4y agoIt's been blowing up since at least 2012-13 when deep convolutional neural nets started seeing massive success.
- bottlepalm 4y agoThat's not blowing up. What's happening right now is blowing up.
- jonas21 4y agoWhat's going on now is the continuation of a growth trend that started a decade ago.
- imwithstoopid 4y agoNvidia is in an excellent position - they have CUDA as you point out, and they are moving that into server room dominance in this application space Google has TPUs but have these even made a tiny dent in Nvidia's position? I assume anything Apple is cooking is using Nvidia in the server room already Intel seems completely absent from this market AMD seems content to limit its ambitions to punching Intel its Nvidia's game to lose at this point...I wonder when they start moving in the other direction and realize they have the power to introduce their own client platform (I secretly wish they would try to mainstream a linux laptop running on Nvidia ARM but obviously this is just a fantasy) if anything, I think Huang may not be ambitious enough!
- jitl 4y agoTegra & later Shield were attempts to get closer to full end user platform. The Nintendo Switch is their most successful such device — with a 2-year old Tegra SKU at launch. But going full force into consumer tech is a distraction for them right now. Even the enthusiast graphics market, which should be high margin, is losing their interest. They make much more selling to the big enterprise customer CEO Jensen mentions in the open paragraph.
- echelon 4y agoGamers are going to be so pissed. They subsided the advance in GPU compute and will now be ignored for the much more lucrative enterprise AI customers. Nvidia is making the right call, of course.
- smoldesu 4y agoGamers are in heaven right now. Used 30-series cards are cheap as dirt, keeping the pressure on Intel/AMD/Apple to price their GPUs competitively. The 40-series cards are a hedged bet against anything their competitors can develop - manufactured at great cost on TSMC's 4nm node and priced out-of-reach for most users. Still, it's clear that Nvidia isn't holding out their best stuff, just charging exorbitant amounts for it.
- layoric 4y agoWhere are these cheap as dirt 30 series? A 10gb 3080 is still over $500 usd used ($750 aud) when I’ve looked. When did secondhand GPUs that still cost the same as a brand new PS5 start to be considered cheap?
- my123 4y agoA PS5 is _significantly_ slower than a 3080, it's more RTX 2070 tier.
- 4y ago
- jacooper 4y agoAMD GPUs are a joke for anything but gaming
- sofixa 4y agoYet. Their GPUs were not great for years, but have managed to catch up (and even overperform for their price point), so other workloads are yet to come.
- danpalmer 4y agoAt the moment maybe, but owning the CPU may allow for better integration. Imagine if AMD launch a new bus implementation from CPU to GPU. That's not something Nvidia can do by themselves. Maybe Nvidia buys Intel and does it though!
- paulmd 4y agoYou mean a proprietary connection standard? Like G-Sync but for accelerator cards? ;) https://www.tomshardware.com/news/amd-infinity-fabric-3 https://www.tomshardware.com/news/amd-infinity-fabric-3 AMD already does that, and much like G-Sync there is also an open standard (CXL) that everyone else is converging around. https://en.wikipedia.org/wiki/Compute_Express_Link https://en.wikipedia.org/wiki/Compute_Express_Link
- noogle 4y agoHow much of a moat is CUDA? It's indeed ages beyond any of their competitors. However, most ML/DS people interact with CUDA via a higher-level framework. In recent years this community has consolidated around a few (and even only one platform, PyTorch) framework. For some reason AMD had not invested in platform backends, but there is no network effect or a vendor lock-in to hinder a shift from CUDA to ROCm if it is supported equally well.
- floatngupstream 4y agoThere is an enormous investment beside the training side. Once you have your model, you still need to run it. This is where Triton, TensorRT, and handcrafted CUDA kernels as plugins come in. There is no equivalent on ROCm for this (MIGraphX is not close).
- noogle 4y agoModels are re-trained periodically (months, weeks, even days), and new architectures/implementations come all the time. If a better algorithm appears, practitioners will adopt a new platform (e.g. Transformers for NLP models), so many systems can already plug-in new tools. GPUs are very expensive so there is also a strong incentive to make this little effort.
- floatngupstream 4y agoYes, but this just makes a frictionless runtime for inference even more important (which is something that does not exist in a comparable form for AMD).
- mastax 4y agoI'm not convinced AMD has a good AI play. (Disclaimer: I hold a long position in AMD). AI hardware seems like it can be much simpler than GPGPUs, given the successful implementations by many companies including small startups. AI hardware software seems like it is extremely difficult. Making a simple programmer and ops interface over a massively parallel, distributed, memory bandwidth constrained system that needs to be able to compile and run high performance custom code out of customers' shifting piles of random python packages. AMD has continuously struggled at (2) and hasn't seemed to recommit to doing it properly. AMD certainly has silicon design expertise, but given (1) I don't think that is enough. Xilinix is in interesting alternative path for products or improving AMDs software/devex. I'm not sure what to expect from that, yet.
- A4ET8a8uTh0 4y agoYeah... but have you tried using AMD GPUs for any LLMs for example? All the interesting stuff that is publicly released is for Nvidia. I would love to be able to focus on AMD since Nvidia has been adding some anti-user features lately.
- pier25 4y ago> Google have got their own chips for Android What chips for Android?
- raverbashing 4y agoAnd Intel is, as usual, doing nothing