AI Spending Is Shifting — And Broadcom, Marvell Are Positioned To Win

Core Insights - AI datacenters are entering a new phase focused on inference rather than training, which is expected to reshape the competitive landscape and spending patterns in the industry [1][2][8] Shift from Training to Inference - The focus is shifting from training large models to optimizing inference processes, with techniques like distillation and quantization making inference cheaper and more efficient [2][3] - By 2027, inference is expected to dominate incremental compute spending, with a notable shift already occurring in 2025-2026 [3] Beneficiaries of the Shift - Broadcom is highlighted as a key beneficiary due to its custom ASICs that support inference for major companies like Google, Amazon, and Meta [4] - Marvell Technology is also positioned to benefit as inference workloads increasingly rely on Ethernet and PCIe, moving away from expensive training-oriented technologies [5] Hardware and Networking Trends - Celestica is well-positioned as the industry moves towards standardized, cost-effective inference hardware, allowing operators to source from multiple vendors [6] - Arista Networks continues to support high-performance training networks, but the shift towards Ethernet in inference may create new opportunities for networking companies [6] Power Efficiency and Deployment - Inference is significantly less power-hungry than training, often requiring 5-10 times less power, making it easier to deploy in datacenters with limited grid capacity [7] - The trend towards making AI cheaper, faster, and easier to run is expected to drive spending towards companies like Broadcom and Marvell [8]