The rise of AI over the last decade has been powered by the increasing speed and decreasing cost of GPUs and other accelerator chips. How long will this continue? The past month saw several events that might affect how GPU prices evolve.
In September, Ethereum, a major blockchain that supports the cryptocurrency known as ether, completed a shift that significantly reduced the computation it requires. This shift — dubbed the Merge — should benefit the natural environment by consuming less energy. It will also decrease demand for GPUs to carry out cryptocurrency mining. (The Bitcoin blockchain remains computationally expensive.) I expect that lower demand will help lower GPU prices.
On the other hand, Nvidia CEO Jensen Huang declared recently that the era in which chip prices could be expected to fall is over. Moore’s Law, the longstanding trend that has doubled the number of transistors that could fit in a given area of silicon roughly every two years, is dead, he said. It remains to be seen how accurate his prediction is. After all, many earlier reports of the death of Moore’s Law have turned out to be wrong. Intel continues to bet that it will hold up.
That said, improvements in GPU performance have exceeded the pace of Moore’s Law as Nvidia has optimized its chips to process neural networks, while the pace of improvements in CPUs, which are designed to process a wider range of programming, has fallen behind. So even if chip manufacturers can’t pack silicon more densely with transistors, chip designers may be able to continue optimizing to improve the price/performance ratio for AI.
International news also had implications for chip supply and demand. Last week, the United States government restricted U.S. companies from selling advanced semiconductors and chip-making equipment to China. It also prohibited all sales in China of AI chips made using U.S. technology or products and barred U.S. citizens and permanent residents from working for Chinese chip firms.
No doubt the move will create significant headwinds for many businesses in China. It will also hurt U.S. semiconductor companies by limiting their market and further incentivizing Chinese competitors to replace them. The AI community has always been global, and if this move further decouples the U.S. and China portions, it will have effects that are hard to foresee.
Still, I’m optimistic that AI practitioners will get the processing power they need. While much AI progress has been — and a meaningful fraction still is — driven by using cheaper computation to train bigger neural networks on bigger datasets, other engines of innovation now drive AI as well. Data-centric AI, small data, more efficient algorithms, and ongoing work to adapt AI to thousands (millions?) of new applications will keep things moving forward.
Semiconductor startups have had a hard time in recent years because, by the time they caught up with any particular offering by market leader Nvidia, Nvidia had already moved on to a faster, cheaper product. If chip prices stop falling, they’ll have a bigger market opportunity — albeit with significant technical hurdles — to build competitive chips. The industry for AI accelerators remains dynamic. Intel and AMD are making significant investments and a growing number of companies are duking it out on the MLPerf benchmark that measures chip performance. I believe the options for training and inference in the cloud and at the edge will continue to expand.