Tomasz Tunguz - NVIDIA
Tomasz TunguzVenture Capitalist If you were forwarded this newsletter, and you'd like to receive it in the future, subscribe here. NVIDIA
NVIDIA’s growth is an index on the growth of AI. “Compute revenue grew more than 5x and networking revenue more than 3x from last year.” Data center revenue totaled $26b, with about 45% from the major clouds ($13b). These clouds announced they were spending $40b in capex to build out data centers, implying NVIDIA is capturing very roughly 33% of the total capex budgets for their cloud customers.
NVIDIA has started to highlight the return-on-investment (ROI) for cloud providers. As the prices for GPUs increases, so do NVIDIA’s profits, to a staggering degree - nearly 10x in dollar terms in 2 years. Is this a problem for the clouds?
That may not matter to GPU buyers - at least not yet - because of the unit economics. Today, $1 spent on GPUs produces $5 of revenue.
But soon it, it will generate $7 of revenue. Amazon Web Services operates at a 38% operating margin. If these numbers hold, newer chips should improve cloud GPU profits - assuming the efficiency gains are not competed away.
And this trend should continue with the next generation architecture, Blackwell.
We can also guesstimate the value of some of these customers. DGX H100s cost about $400-450k as of this writing. With 8 GPUs for each DGX, that means Tesla acquired about $1.75b worth of NVIDIA hardware assuming they bought, not rented, the machines.
In a parallel hypothetical, Meta would have spent $1.2b to train Llama 3. But the company plans to have buy 350,000 H100s by the end of 2024 implying about $20b of hardware purchases.
As these costs skyrocket, it wouldn’t be surprising for governments to subsidize these systems just as they have subsidized other kinds of advanced technology, like fusion or quantum computing. Or spend on them as a part of national defense.
There are two workloads in AI : training the models & running queries against them (inference). Today training is 60% and inference is 40%. One intuition is that inference should become the vast majority of the market over time as model performance asymptotes. However it’s unclear if that will happen primarily because of the massive increase of training costs. Anthropic has said models could cost $100b to train in 2 years.
The trend shows no sign of abating. Neither do the profits!
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