{"id":143364,"date":"2026-06-02T06:10:05","date_gmt":"2026-06-02T09:10:05","guid":{"rendered":"https:\/\/ubirataonline.com.br\/does-the-surge-in-demand-for-asics-make-broadcom-a-no-brainer-buy-before-june-3\/"},"modified":"2026-06-02T06:10:08","modified_gmt":"2026-06-02T09:10:08","slug":"does-the-surge-in-demand-for-asics-make-broadcom-a-no-brainer-buy-before-june-3","status":"publish","type":"post","link":"https:\/\/ubirataonline.com.br\/en\/does-the-surge-in-demand-for-asics-make-broadcom-a-no-brainer-buy-before-june-3\/","title":{"rendered":"Does the Surge in Demand for ASICs Make Broadcom a No-Brainer Buy Before June 3?"},"content":{"rendered":"

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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<\/strong> (NASDAQ: NVDA)<\/span> 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.<\/p>\n

But data centers are growing in size, creating cost constraints and an AI energy bottleneck<\/a>. 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.<\/p>\n

Missed Nvidia in 2009? This Rare Signal Is Flashing Again.<\/strong>\u00a0In 2009, a “Double Down” signal flashed for a little-known chipmaker called Nvidia.<\/span>\u00a0For the first time in years, that same “Total Conviction” signal is flashing for a company 1\/100th the size of Nvidia.\u00a0Continue \u00bb<\/strong><\/span><\/a><\/em><\/p>\n

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<\/strong> forecasts that demand for ASICs will surpass GPU demand<\/a> in the coming years.<\/p>\n

Broadcom <\/strong>(NASDAQ: AVGO)<\/span> is the leading designer of ASICs, so it stands to benefit from that boom. But is the semiconductor stock a buy before it reports earnings on June 3?<\/p>\n

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Image source: Getty Images. <\/figcaption><\/figure>\n

ASICs at the hyperscale <\/h2>\n

Alphabet<\/strong>‘s (NASDAQ: GOOGL)<\/span> (NASDAQ: GOOG)<\/span> Tensor Processing Units (TPUs), Meta Platforms<\/strong>‘ (NASDAQ: META)<\/span> Meta Training and Inference Accelerator (MTIA), and Amazon’<\/strong>s (NASDAQ: AMZN)<\/span> Trainium chips are examples of ASICs.<\/p>\n

Meta and Alphabet’s Google work with Broadcom’s custom accelerator platform to design their chips, while Amazon Web Services (AWS) has an in-house semiconductor division, Annapurna Labs.<\/p>\n

Google and AWS have achieved cost savings and efficiency improvements by deploying their custom chips at scale for cloud services. Google uses TPUs to power its Gemini large language model and other AI-powered applications like Google Search, Google Maps, and Google Photos. Meta designed MTIA for its internal infrastructure, including its search and content recommendation algorithms.<\/p>\n

Broadcom hasn’t been shy about quantifying the surging demand for ASICs. It’s forecasting that it will book $100 billion in fiscal 2027 sales from its AI chips alone. Beyond that, the company also has massive non-AI semiconductor and infrastructure software businesses. However, it would be a mistake for investors to assume ASICs will overtake GPUs in data centers.<\/p>\n<\/p><\/div>\n