OpenAI, a leading AI startup with ample financial backing, is exploring the possibility of developing its own AI chips.
This move comes as a response to the ongoing chip shortage that has hindered AI model training. According to Reuters, Discussions within OpenAI about AI chip strategies began as early as last year. To address this challenge, OpenAI is considering several strategies, which include either acquiring an AI chip manufacturer or embarking on an internal chip design endeavor.
OpenAI’s CEO, Sam Altman, has made obtaining more AI chips a top priority for the company. Reuters reports.
Currently, OpenAI, much like its competitors, relies on GPU-based hardware for the development of models like ChatGPT, GPT-4, and DALL-E 3. GPUs are known for their ability to perform multiple computations in parallel, which is crucial for training the most advanced AI models.
However, the recent surge in generative AI has led to an unprecedented demand for GPUs, putting immense pressure on the supply chain. This heightened demand has significantly strained the availability of GPUs, impacting not only OpenAI but also other industry players. In fact, Microsoft is facing a critical shortage of server hardware required for AI operations, which could potentially lead to service disruptions, as indicated in their summer earnings report. Furthermore, Nvidia, a prominent GPU manufacturer, is struggling to meet the demand for its best-performing AI chips, with reports suggesting that they will be sold out until 2024.
OpenAI, the groundbreaking AI company, relies heavily on powerful GPUs to run its models. These GPUs are essential, but they come at an exceptionally high cost.
According to an analysis by Bernstein analyst Stacy Rasgon, if ChatGPT were to reach one-tenth the scale of Google Search, it would demand an initial investment of around $48.1 billion worth of GPUs and an annual expenditure of about $16 billion in chips just to maintain its operations.
This expenditure places significant financial strain on OpenAI. However, they are not the only ones exploring the possibility of creating their own AI chips.
Tech giants like Google, Amazon, and Microsoft have already ventured into this domain. For instance, Google employs TPUs (tensor processing units) for training large generative AI systems like PaLM-2 and Imagen. Amazon offers custom chips to its AWS customers for both training (Trainium) and inferencing (Inferentia). Microsoft is even rumored to be working with AMD on their in-house AI chip, Athena, a chip that OpenAI is reportedly testing.
OpenAI does have the financial strength to invest heavily in research and development. With over $11 billion in venture capital funding and nearing $1 billion in annual revenue, they are considering a share sale that could skyrocket their secondary-market valuation to a staggering $90 billion. according to a recent Wall Street Journal report.
However, the business of hardware, especially AI chips, is exceptionally unforgiving.
To illustrate, Graphcore, an AI chipmaker, saw its valuation plummet by $1 billion after a deal with Microsoft fell through. The company had to consider job cuts due to the “extremely challenging” economic environment. Habana Labs, an AI chip company owned by Intel, also had to lay off an estimated 10% of its workforce. Even Meta, the tech giant, has faced difficulties in its custom AI chip endeavors, leading to the abandonment of some experimental hardware.
Even if OpenAI decides to move forward with creating its own custom chip, this endeavor would be a long-term and extremely costly one. It may take several years and require hundreds of millions of dollars annually. The big question that remains is whether OpenAI’s investors, including Microsoft, are willing to take such a significant and risky bet on this venture.
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