Does the Surge in Demand for ASICs Make Broadcom a No-Brainer Buy Before June 3?
2026-06-02 03:04:00 ET
The initial boom in artificial intelligence (AI) data center investment centered around hardware for training AI models -- workloads that demand massive parallel-processing capabilities. Graphics processing units (GPUs) are powerful and flexible parallel processors, and that property propelled the rise of GPU specialist Nvidia (NASDAQ: NVDA) from an ordinary large cap worth around $350 billion at the start of 2023 to its current position as the world's most valuable company. Today, it's worth more than $5 trillion.
But data centers are growing in size, creating cost constraints and an AI energy bottleneck . What's more, hyperscalers' needs are evolving as AI inference becomes a growing part of the overall workload. While training is needed to build a model's intelligence, inference applies that intelligence in real-world applications, such as through AI chatbots, AI agents, robotics, and self-driving cars.
Application-specific integrated circuits (ASICs) aren't nearly as flexible as GPUs, but they can be highly cost-effective at scale. This is why investment bank Goldman Sachs forecasts that demand for ASICs will surpass GPU demand in the coming years.
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