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by willis936 129 days ago
I'm not missing the point. If you recall your computer architecture class there are many vector processing architectures out there. Long before there was nvidia the world's largest and most expensive computers were vector processors. It's inaccurate to say "gaming built SIMD".
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danielmarkbruce 129 days ago
You are missing the point - it's an economic point. Very little R&D was put into said processors. The scale wasn't there. The software stack wasn't there (because the scale wasn't there).

No one is suggesting gaming chips were the first time someone thought of such an architecture or built a chip with such an architecture. They are suggesting the gaming industry produced the required scale to actually do all the work which lead to that hardware and software being really good, and useful for other purposes. In chip world, scale matters a lot.

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willis936 129 days ago
The Cray-1, which produced half a billion USD in revenue in today's dollars, at a time when computing was still science fiction, did not demonstrate scale? I just can't take you in good faith because there has never been a time when large scale SIMD computing was not advanced by commercial interests.
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danielmarkbruce 129 days ago
In this context scale = enough units/revenue to spread fixed costs.

I'll take your word on lifetime revenue numbers for Cray 1.

So yes, in todays dollars, $500 million of lifetime revenue - maybe 60-70 million per year, todays dollars - is not even close to the scale we are seeing today. Even 10 years ago Nvidia was doing ~$5 billion per year (almost 100x your number) and AMD a few bill(another 60-70x ish)

Even if you meant $500m in annual (instead of lifetime), Nvidia was 10x that in 2015. And AMDs GPU revenue which was a few billion that year, so it's more like 17x.

That's a large difference in scale. At the low end 17x and at the high end 170x. Gaming drove that scale. Gaming drove Nvidia to have enough to spend on CUDA. Gaming drove NVidia to have enough to produce chip designs optimized for other types of workloads. CUDA enabled ML work that wasn't possible before. That drove Google to realize they needed to move away from ML on CPU if they wanted to be competitive.

You don't need any faith, just understand the history and how competition drives behavior.

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