The Future of AI Technology with NVIDIA H100 GPUs

We’re all familiar with NVIDIA and the AI “gold mine” that has just taken the world by storm. In the center of it all are Team Green’s H100 AI GPUs, which are the most sought-after piece of AI hardware at the moment, with everyone attempting to get their hands on one to fuel their AI demands.

The NVIDIA H100 GPU is the best chip for AI right now, and everyone wants more of them.

This post isn’t really newsworthy, but it does inform readers on the current state of the AI market and how businesses are revolving around the H100 GPUs for their “future.”

Once we get into the meat of the article, a little summary is in order. So, at the beginning of 2022, everything was proceeding as usual. However, with the entrance of November, a breakthrough application known as “ChatGPT” surfaced, laying the groundwork for the AI frenzy. While “ChatGPT” cannot be considered the originator of the AI boom, it can be considered a catalyst. As a result, competitors such as Microsoft and Google have been forced into an AI race to produce generative AI products.

Where does NVIDIA fit into this, you might ask? The backbone of generative AI is extensive LLM (Large Language Model) training, and NVIDIA AI GPUs come in handy here. We won’t delve into technical details or factual information because it’s boring and uninteresting to read. If you’re looking for specifics, we’ve included a table below that lists every AI GPU release from NVIDIA, dating back to Tesla models.

NVIDIA HPC / AI GPUs

The subject of why the H100s remains unanswered. So, we’re getting there. The NVIDIA H100 is the company’s highest-end product, with massive computing capabilities. One could argue that the increase in performance results in higher costs, but firms tend to order in large quantities, and “performance per watt” is the goal here. The Hopper “H100” outperforms the A100 in 16-bit inference and training performance by 3.5 times, making it the obvious choice.

Therefore, we’re hoping that the superiority of the H100 GPU is clear here. Moving on to our next topic, why is there a scarcity? The answer incorporates multiple factors, the first of which is the massive number of H100s required to train a single model. Astonishingly, OpenAI’s GPT-4 AI model required 10,000 to 25,000 A100 GPUs (H100s were not available at the time).

Advanced artificial intelligence businesses such as Inflection AI and CoreWeave have acquired massive amounts to H100s, totaling billions of dollars. This demonstrates that even to train a basic-to-decent AI model, a single organization requires massive volumes, resulting in massive demand.

If NVIDIA’s method is questioned, one can respond, “NVIDIA could increase production to meet demand.” It is much easier to say this than it is to do it. Unlike gaming GPUs, NVIDIA AI GPUs necessitate sophisticated processes, with the majority of production delegated to Taiwanese semiconductor titan TSMC. TSMC is NVIDIA’s sole supplier of the AI GPU, overseeing all processes from wafer purchase through sophisticated packaging.

The H100 GPUs are built on TSMC’s 4N technology, which is a redesigned variant of the 5nm series. NVIDIA is the most important customer for this method because Apple previously used it for its A15 Bionic chipset, which has since been superseded by the A16 Bionic. The creation of HBM memory is the most difficult of all critical phases since it requires advanced equipment that is only used by a few manufacturers.

HBM vendors include SK Hynix, Micron, and Samsung, although TSMC has limited its suppliers, the identities of whom we are unclear. Aside from HBM, TSMC is also struggling to maintain CoWoS (Chip-on-Wafer-on-Substrate) capacity, a 2.5D packaging technique, and a critical stage in the development of H100s. Because TSMC is unable to meet NVIDIA’s demand, order backlogs have reached record heights, with delivery delayed until December.

We have left out numerous facts because going into detail would detract from our core goal of informing the ordinary user about the situation. While we do not believe the scarcity will be alleviated very soon, it is expected to worsen. However, with AMD’s decision to consolidate its position in the AI sector, we may witness a shift in the landscape.

According to DigiTimes, “TSMC appears to be particularly optimistic about demand for AMD’s upcoming Instinct MI300 , claiming that it will account for half of Nvidia’s total output of CoWoS-packaged chips.”” It is possible that the burden will be distributed across organizations. Still, given Team Green’s greedy policies in the past, something like this would necessitate a significant offering from AMD.

To summarize our discussion, NVIDIA’s H100 GPUs are driving the AI craze to new heights, which is why they are surrounded by such a frenzy. We hoped to conclude our discussion by providing readers with a general overview of the situation. GPU Utilis deserves credit for the inspiration for this article; be sure to read their report as well.