Analysts predict a significant evolution in AI workloads, becoming more complex and demanding, which will require specialized architectures for improved inference performance. This shift is pressuring data center networks, driving hyperscalers to diversify their computing strategies. They are incorporating Nvidia GPUs for general AI tasks, leveraging in-house AI accelerators for optimized functions, and utilizing systems like Cerebras for specialized, low-latency workloads. As a result, infrastructure providers are moving away from traditional monolithic clusters towards more tiered, heterogeneous systems. OpenAI’s adoption of Cerebras exemplifies this trend, emphasizing diversification as inference workloads scale. At this scale, infrastructure resembles an AI factory where city-scale power delivery and low-latency networking are critical, surpassing the importance of peak FLOPS. Conventional models for rack density and cooling become impractical, necessitating flatter network topologies and tighter integration of compute, memory, and interconnect to manage the continuous, latency-sensitive traffic generated by inference workloads.
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