The Semiconductor Shift When Latency And Throughput Architectures Join Forces
Nano Banana Two Worlds Of AI Compute Are Finally Colliding For more than a decade, AI silicon has evolved along two independent trajectories. On one side sat throughput-optimized architectures built to train massive models across thousands of accelerators. These prioritize raw FLOPS, memory bandwidth, and scaling efficiency. On the other hand, latency-optimized designs were engineered to deliver fast, deterministic inference. They are used at the edge or in tightly constrained data center environments. Each solved a different bottleneck, served a different buyer, and spoke a different architectural language. That division made sense when training and inference occurred separately. Training was infrequent and centralized in hyperscale data centers. Inference ran continuously, near users, under strict latency and power limits. Chip companies specialized: some in large-scale matrix math, others […]
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