Industry Analysis
With AI inference now consuming two-thirds of data center compute, the chip stack is shifting decisively toward energy-efficient CPUs and ASICs, sidelining GPU-centric training architectures. AMD’s Zen4c-based server CPUs, ARM’s Neoverse-driven silicon ambitions, and Marvell’s hyperscaler-tailored ASICs collectively erode NVIDIA’s monopoly by targeting post-training workloads. EU Chips Act compliance and U.S. export controls raise operational costs, yet bolster ARM’s design sovereignty in Europe. NVIDIA may be forced to open CUDA to retain relevance, while rivals could rally around open standards. Over the next 18 months, leadership will pivot from raw FLOPS to watt-per-inference efficiency—favoring firms with chiplet integration and heterogeneous compute prowess. This isn’t just a market shift; it’s a geopolitically charged battle for architectural autonomy.
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