Original: 2022-06-06
Updated: 2022-06-06
I have been exposed to a lot of sensational pieces following Nvidia's stock price plummeting 35% over the past few months, and I don't think they are accurate. A lot of these pieces focus on the crypto crash, and how Nvidia was heavily exposed to that particular market. This is accurate to some extent, but I don't think anybody is accounting for where that GPU compute is going to go now.
Deep learning requires incredible amounts of compute resources, and the return on investment for training a massive model is incredibly high. Take GPT-3 for instance, which was trained by OpenAI for somewhere around $10M when including research expenses. Microsoft followed up on their $1B investment in OpenAI by purchasing exclusive licensing rights to GPT-3 in September 2020. It is unknown how large that licensing deal was, but I suspect that it is safe to say that deal would itself have been north of $100M. A 10x ROI for OpenAI, 100x if you include the $1B investment that was made prior to the licensing deal.
Let's consider the compute resources required to train GPT-3 - a model known for being undertrained relative to its size. OpenAI was known to leverage a supercomputer built in partnership with Microsoft Azure that contained around 10,000 Nvidia V100 GPU's. These GPU's had an MSRP of around $11,500 at the time, and accounting for volume discounts I suspect the pricing was closer to $7,000 per GPU, for a total of ~$70M. That being said, that compute was only used for a few months out of the multi-year lifetime of that supercomputer, hence the $10M price tag, with around $6M of that being dedicated to training, and the rest corresponding research and development costs. Thest V100 GPU's are the top of the line silicon from Nvidia. You could build dozens of consumer-grade SKU's out of a single V100 GPU because of the high number of imperfections in the manufacturing process that get thrown out in pursuit of the best possible silicon chip.
Crypto mining rigs have historically used consumer-grade GPU's for running hashing algorithms. This is effectively wasted compute on verifying transactions on the blockchain in an extremely expensive manner. What has changed, however, is that as machine learning has increasing applications in the consumer space, more and more consumer-level GPU's contain the top-end silicon referred to earlier. That means that using 8 consumer GPU's for deep learning workflows might begin to make economic sense instead of a single enterprise GPU like the V100.
In addition to all of this, there are supply constraints. Simply put, chip fabs like TSMC that Nvidia relies on are operating at or above maximum capacity, and new capacity takes time to build. That ignores the potential for a Chinese invasion of Taiwan, which would cause production of these chips to plummet by 65%.
To summarize, these statements appear to be true:
If those set of statements are true, then it follows that GPUs will shift from crypto mining into deep learning, which replaces the demand gap that was recently created. It seems pretty clear to me that in the future, more companies will want supercomputers like that built by OpenAI and Microsoft, with 10s of thousands of GPU's available. Even if we move away from GPU compute in deep learning - towards more specialized chips - at a fabrication level it will likely require the same capacity, for which deep learning as an industry will pay much more than consumers for entertainment.
Unfortunately for individuals, this likely means that GPU prices will not go down a whole lot. It is possible that a company like AMD may focus exclusively on the consumer level hardware, but in a world where they can make 10x the money by mostly just changing the target market, I suspect even they will begin to move to the enterprise space more and more. The current price to performance ratio is likely as low as it will be for the next several years. On the bright side, advances made in deep learning thanks to this shift in demand for GPUs will very likely improve the lives of people around the world to an unprecedented extent. The downside is that these improvements will be delayed a couple years from the initial training runs, instead of the instant gratification that entertainment delivers.