NVIDIA are not selling shovels, they are selling shovel-making machines to the shovel-sellers (the companies making foundation models).
When the market is saturated with shovel-making machines, what then?
When the market is saturated with shovel-making machines, what then?
I expect at some point a) the training and the inference will move to even more specialised hardware and the current existing silicon go to either hobbyists or some other market that requires the current cards b) the way in which we do AI may fundamentally change in ways we can't predict, causing the required hardware to also change (move to FPGA or favouring some other aspect than VRAM)
In both scenarios NVIDIA isn't the one who win, because the sudden influx lifts a secondary market nv don't profit from directly.
I personally don't think they'll be able to sustain their current valuation past the current AI rush, and will compact back down to somewhere their levels pre-chatgpt.
That said I'm just a guy on the Internet who wouldn't mind some beefy ex-datacentre gear if I could make it work for video editing workloads.
The official specs [0] say the A100 80GB has a 300W TDP. That's pretty doable on standard domestic power... harder if you want to run multiples.
[0] https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Cent...
https://www.ebay.com/itm/335392812272?nordt=true
https://www.ebay.com/itm/335408349837?nordt=true
https://www.ebay.com/itm/335414821299?nordt=true
Too good to be true? Perhaps. For $2000 I might trust Ebay's buyer protections...
(1) MLIR, ROCm, or some other tooling that reduces CUDA's moat,
(2) AMD attracting investment to go after NVIDA
(3) ARM-based GPUs or accelerators gaining traction among cloud companies that have huge fleets of AI devices and also have the money to devote to custom chips
Can anyone who understands the industry explain why those threats (or similar ones) aren't a major issue for NVIDIA?
(1) Much development is already moving from CUDA to the LLM, so less of an issue. Nvidia is also doing more work to increase interoperability. Could be an issue I guess, but doesn't seem like it since there's nothing close to CUDA or the ecosystem.
(2) AMD has attracted significant investment looking at appreciation in its market cap, with a PE ration 3X Nvidia's. However, AMD is so far behind in so many ways, I don't believe it is an investment problem, but structural. Nvidia has just been preparing for this for so long it has a temendous head start, not to mention being more focused on this. Remember AMD also competes with Intel, etc.
(3) Hyperscalers already are building their own chips. It seems even Apple used its own chips for Apple Intelligence. It's relatively (which is doing a lot of lifting in this sentence because it's all HARD) not too hard to make custom chips for AI. The hard (near impossible) thing is making the cutting edge chips. And the cutting edge chips are what the OpenAIs of the world demand for training, but releasing the newest best model 1-3 months ahead of a competitors is worth so much.
If anything, I'd say the biggest threat to Nvidia in the next 1-3 years is an issue with TSMC or some new paradigm that makes Nvidia's approach suboptimal.
I don't think I understand your point in (1) that it's less of an issue because development is moving to the LLM. I can infer that maybe CUDA isn't a big part of the moat given your other points that the hard part is making cutting edge chips.
But since there are plenty of more advanced models now, the next level is getting built out as more developers start building applications that use the models (e.g. apps using GPT's API).
So where 5 years ago most AI dev was on CUDA, now most is on the LLMs that were built with CUDA to build applications.