Industry leaders are pivoting back to CPU-centric architectures for specific AI workloads, challenging the long-standing dominance of GPU clusters.

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Industry leaders are pivoting back to CPU-centric architectures for specific AI workloads, challenging the long-standing dominance of GPU clusters. Optimized instruction sets and memory bandwidth improvements are making CPUs more cost-effective for latency-sensitive inference and hybrid training pipelines.