Meta prioritizes high-margin AI inference as Wiwynn, Quanta eye CPU server demand

Meta's capex pivot from training to inference marks AI infrastructure entering its margin-harvesting phase. Inference workloads demand higher single-core IPC and memory bandwidth, pushing CPU value share in server BOMs from roughly 15% back toward 30%—the structural driver behind Wiwynn and Quanta repositioning around CPU server orders. Competitively, Microsoft Azure and AWS Bedrock are architecturally locked into train-infer integrated loops and will not replicate Meta's path. Meta's inference-heavy profile is a structural artifact of its ad-recommendation stack, not a replicable strategy. The 12-to-24-month tail effect is where the real signal sits: if two or more hyperscalers follow suit by H2 2025, NVIDIA's B200 order growth will face its first structural deceleration, and server ODMs in Taiwan, China will reclaim CPU server volume for the first time since the 2023 GPU-monopoly narrative took hold. This is the first substantive reallocation of supply-chain bargaining power in the AI era, and it signals that margin in the next capex cycle will migrate from chip design toward system integration.