… Addressing the Memory-Scale Tradeoff Traditional AI infrastructure forces a tradeoff between speed and scale. High-performance memory tiers such as HBM and DRAM provide microsecond latency but are capacity-constrained and expensive. …
… TPU v8i “Zebrafish” The inference chip shares v8t’s Axion host platform, native FP4, and HBM3e memory generation, but the silicon underneath targets a different bottleneck. Training is compute-bound; inference decode is memory-bandwidth-bound. …
… The C1000 supports PCIe Gen 7 connectivity at over 2 TB/s, along with CXL support for memory disaggregation and high-speed storage and networking integration. Its memory subsystem is designed for high bandwidth, capacity, and power efficiency using leading-edge low-power memory technology. …
… Micron’s portfolio of high-bandwidth memory HBM , DRAM, and data center SSDs will serve as the foundation for this work, underpinning performance, power efficiency, and total cost of ownership across AI training and inference. …