The trajectory of the AI semiconductor ecosystem is characterized by an evolving landscape, driven by the increasing demand for computing power required to advance AI.
According to Barclays analysts, the sector is at a critical juncture as global appetite for AI-based solutions, particularly for large language models, continues to outstrip existing chip supply and performance.
A selloff in AI chip names such as NVIDIA ( NASDAQ:NVDA ) after the earnings reports has raised concerns about whether the market has peaked.
However, Barclays argues that the future of the industry is still full of growth, driven by the ever-increasing computational needs of AI models.
Barclays points out that the AI semiconductor ecosystem is in the early stages of development and this period is characterized by significant supply constraints.
Projections show that the computational resources required to train the next generation of LLMs, some of which are 50 trillion parameters in size, are monumental.
The brokerage firm’s estimates suggest that by 2027, nearly 20 million chips will be needed just to train these models. This number highlights the harsh reality that demand for AI computing is growing at a much faster rate than current chip technology can keep up with, even as AI accelerators improve.
The gap between AI computing demand and chip supply becomes even more apparent when we consider the training requirements for models like GPT-5, which is expected to require 46 times more computing power compared to GPT-4.
However, over the same period, the performance improvement of cutting-edge chips such as NVIDIA’s next-generation Blackwell is expected to be just sevenfold.
This issue is exacerbated by limited chip production capacity, with Taiwan Semiconductor Manufacturing Company (NYSE:TSM), for example, limited to producing about 11.5 million Blackwell chips through 2025.
Adding to the complexity is the projected demand for inference chips. Inference, the stage where AI models produce results after being trained, is set to consume much of the AI computing ecosystem.
Barclays notes that inference could represent up to about 40% of the AI chip market, as evidenced by NVIDIA’s claims that a large portion of its chips are used for this purpose. The total demand for chips in both training and inference could exceed 30 million units by 2027.
As the industry grapples with these challenges, Barclays proposes a two-pronged approach to the AI accelerator market, where both commercial and custom silicon solutions can thrive.
On the one hand, companies like NVIDIA and AMD ( NASDAQ:AMD ) are well-positioned to supply chips for training and inferring large-scale, frontier AI models. On the other hand, hyperscalers—companies that operate massive data centers—are likely to continue to develop custom silicon for more specialized AI workloads.
This split approach will allow flexibility in the market and support various use cases outside the scope of large LLMs.
Inference is expected to play an increasingly critical role, not only as a driver of demand but also as a potential source of revenue.
New inference optimization methods, such as reinforcement learning implemented in OpenAI’s latest ‘o1’ model, signal the potential for breakthroughs in AI performance.
With better allocation of resources and cost-effective inference strategies, the ROI for AI models could be significantly improved, providing incentives for continued investment in both training and inference infrastructure.
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Reference;
Acharya, N. (2024) Where does the AI semiconductor ecosystem go from here? By Investing.com, Investing.com. Investing.com. Available at: https://www.investing.com/news/stock-market-news/where-does-the-ai-semiconductor-ecosystem-go-from-here-3647319 (Accessed: 6 October 2024).
