JPMorgan Chase: AI custom chip shipments may surpass GPU shipments in 2027, with Broadcom and Marvell poised for takeoff.

According to Mars Finance, on June 22nd, JPMorgan Chase stated that the custom ASIC market is entering a new growth cycle as large cloud computing companies and tech giants seek to reduce AI computing costs, improve energy efficiency, and move away from a single path dependence on general-purpose GPUs. Broadcom and Marvell are expected to be the biggest beneficiaries of this trend. In a recent semiconductor industry research report, JPMorgan analysts Harlan Sur and Mayur Ramdhani estimated that the digital AI ASIC market will reach approximately $60 billion to $70 billion by 2026, maintaining a compound annual growth rate of over 40% to 50% in the coming years. The report states that Broadcom currently holds approximately 80% to 85% of the high-end ASIC market share, with Marvell ranking second with approximately 10% to 12%. The rapid growth in AI computing demand is changing chip procurement structures. JPMorgan believes that customers such as Google, Amazon, Meta, Microsoft, OpenAI, and SoftBank/Arm are accelerating the development of their own or custom AI processors to achieve better performance, power consumption, and total cost of ownership. Unlike Nvidia and AMD's general-purpose GPUs, ASICs are typically designed for specific customers, software stacks, or platforms, making them more suitable for hyperscale cloud vendors with large-scale internal workloads. The report projects that Broadcom's AI revenue will grow significantly from approximately $20 billion in fiscal year 2025 to over $60 billion in fiscal year 2026, and track to over $150 billion in fiscal year 2027. Its project pipeline includes Google TPU, Meta MTIA, ByteDance AI video and network chips, OpenAI XPU, SoftBank/Arm XPU, and Anthropic's related TPU rack-mount solutions. For Marvell, JPMorgan Chase projects its data center revenue to grow from approximately $6.1 billion in 2025 to approximately $9.3 billion in 2026, reaching approximately $14.6 billion in 2027. Growth drivers include Amazon Trainium 3 and Trainium 4, Microsoft Maia, Google SmartNIC/DPU, CXL controllers, and 800G/1.6T optical DSPs, coherent lite, and early CPO solutions. The report also makes a key prediction: by 2027, the unit shipment of AI ASICs/XPUs will surpass that of GPUs. JPMorgan Chase projects total AI accelerator shipments to reach 23.3 million units in 2027, with GPUs accounting for 10.9 million units (47%) and ASICs/XPUs accounting for 12.5 million units (53%). This means that while GPUs will continue to grow, custom chips are likely to capture a larger share of new AI computing power deployments. JPMorgan Chase, citing Google/Broadcom's TPU7x Ironwood and Nvidia's Blackwell as examples, argues that AI ASICs are competitive in terms of cost-effectiveness and power efficiency. The report shows that the TPU7x Ironwood's FP8 computing power is close to that of the Nvidia B200/B300, but its estimated price is around $13,000, lower than the B200's $35,000 and the B300's $40,000; its computing power per dollar and computing power per watt are also superior to comparable GPUs. This assessment does not imply a rapid decline in Nvidia demand. Instead, it points to a divergence in AI infrastructure investment: GPUs continue to serve general training and inference needs, while cloud vendors' self-developed ASICs will achieve higher penetration rates in large-scale, stable, and predictable internal workloads. For investors, JPMorgan Chase's report reinforces the logic of the AI hardware chain diversifying from GPUs to ASICs, advanced packaging, HBM interfaces, SerDes, optical interconnects, and CPOs. If the predictions in the report come true, Broadcom and Marvell will not just be suppliers of AI networks or connectivity chips, but will become core platform companies in the next phase of AI computing architecture migration.