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Comment by talldayo | Hacker News Reader

> Nvidia’s moat doesn’t seem that big.

Well, look at it this way. Nvidia played their cards so well that their competitors had to invent entirely new product categories to supplant their demand for Nvidia hardware. This new hardware isn't even reprising the role of CUDA, just the subset of tensor operations that are used for training and AI inference. If demand for training and inference wanes, these hardware investments will be almost entirely wasted.

Nvidia's core competencies - scaling hardware up and down, providing good software interfaces and selling direct to consumer are not really assailed at all. The big lesson Nvidia is giving to the industry is that you should invest in complex GPU architectures and write the software to support it. Currently the industry is trying it's hardest to reject that philosophy, and only time will tell if they're correct.

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> CUDA, just the subset of tensor operations that are used for training and AI inference. If demand for training and inference wanes

Interesting take, but why would demand for training and inference wade? This seems like a very contrarian take.

Maybe it won't - I say "time will tell" because we really do not know how much LLMs will be demanded in 10 years. Nvidia's stock skyrocketed because they were incidentally prepared for an enormous increase in demand the moment it happened. Now that expectations are cooling down and Sam Altman is signalling that AGI is a long ways off, the math that justified designing NPU/TPU hardware in-house might not add up anymore. Even if you believe in the tech itself, the hype is cooling and the do-or-die moment is rapidly approaching.

My overall point is that I think Nvidia played smartly from the start. They could derive profit from any sufficiently large niche their competitors were too afraid to exploit, and general purpose GPU compute was the perfect investment. With AMD, Apple and the rest of the industry focusing on simpler GPUs, Nvidia was given an empty soapbox to market CUDA with. The big question is whether demand for CUDA can be supplanted with application-specific accelerators.

> The big question is whether demand for CUDA can be supplanted with application-specific accelerators.

At least for AI workloads, Google's XLA compiler and the JAX ML framework have reduced the need for something like CUDA.

There are two main ways to train ML models today:

1) Kernel-heavy approach: This is where frameworks like PyTorch are used, and developers write custom kernels (using Triton or CUDA) to speed up certain ops.

2) Compiler-heavy approach: This uses tools like XLA, which apply techniques like op fusion and compiler optimizations to automatically generate fast, low-level code.

NVIDIA's CUDA is a major strength in the first approach. But if the second approach gains more traction, NVIDIA’s advantage might not be as important.

And I think the second approach has a strong chance of succeeding, given that two massive companies—Google (TPUs) and Amazon (Trainium)—are heavily investing in it.

(PS: I'm also bit biased towards approach 2), we build llama3 fine-tuning on TPU https://github.com/felafax/felafax)

It's weird to me that folks think NVDA is just sitting there, waiting for everyone to take their lunch. Yes, I'm totally sure NVDA is completely blind to competition and has chosen to sit on their cash rather than develop alternatives...</s>

Not really, no. Over the past several years, JAX was used in only 3% of top publications. PyTorch in 60%. There's no trend to suggest that JAX has "reduced the need" for anything, except for Google itself. https://paperswithcode.com/trends

Not sure if publications are a good proxy for this - there’s definitely a selection bias there, and inertia.

Feel free to suggest a more reliable proxy if you don't like this one. What this means is your chance to find a person who knows/has experience with JAX are very low.

Nvidia’s hardware optimized for gaming was the best crypto miner out of the gate, although it was eventually surpassed for crypto mining by asics. NVIDIA’s cards which (by market share) were mostly sold as crypto miners, were nonetheless the best llm accelerators out of the gate. When in three years the best selling compute task is suddenly fleeple inverting, 900 lines of CUDA will turn Nvidia’s LLM cards into the worlds fastest fleeple inverters, while TPUs will continue to be very good token predictors

> If demand for training and inference wanes, these hardware investments will be almost entirely wasted

Nvidia also need to invent smth then, as pumping mining (or giving good to gamers) again is not sexy. What's next? Will we finally compute for drug development and achieve just as great results as with chatbots?

> Nvidia also need to invent smth then, as pumping mining (or giving good to gamers) again is not sexy.

They do! Their research page is well worth checking out, they wrote a lot of the fundamental papers that people cite for machine learning today: https://research.nvidia.com/publications

> Will we finally compute for drug development and achieve just as great results as with chatbots?

Maybe - but they're not really analogous problem spaces. Fooling humans with text is easy - Markov chains have been doing it for decades. Automating the discovery of drugs and research of proteins is not quite so easy, rudimentary attempts like Folding@Home went on for years without any breakthrough discoveries. It's going to take a lot more research before we get to ChatGPT levels of success. But tools like CUDA certainly help with this by providing flexible compute that's easy to scale.

There was nothing rudimentary about Folding@Home (either in the MD engine or the MSM clustering method), and my paper on GPCRs that used Folding@Home regularly gets cites from pharma (we helped establish the idea that treating proteins as being a single structure at the global energy minimum was too simplistic to design drugs). But F@H was never really a serious attempt at drug discovery- it was intended to probe the underlying physics of protein folding, which is tangentially related.

In drug discovery, we'd love to be able to show that virtual screening really worked- if you could do docking against a protein to find good leads affordably, and also ensure that the resulting leads were likely to pass FDA review (IE, effective and non-toxic), that could potentially greatly increase the rate of discovery.

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