This article explains why specialized AI chips like Google's TPU and mobile NPUs are needed, details their architectures (systolic arrays, SoC integration), compares generations and vendors (Ascend, Cambricon, Edge TPU, Habana), and covers key metrics (TOPS, TOPS/W) and market landscape across training, inference, and edge.
This article explains why GPUs are essential for AI large models, detailing their parallel computing architecture, high-bandwidth memory (HBM), and multi-GPU interconnect technologies, then compares mainstream AI chips including NVIDIA's H100, H200, B200, AMD's MI300X, MI350X, and Google TPU across architecture, memory, bandwidth, and target workloads.
At Next 2026 Google unveiled the 8th‑generation TPU, separating training and inference into two dedicated chips—TPU 8t with 121 ExaFLOPS for massive models and TPU 8i with ultra‑low latency memory—while boosting performance, efficiency, and ecosystem support, signaling a shift toward specialized AI hardware and intensifying competition with Nvidia.
The article analyzes Google’s shift from scaling‑law to efficiency‑law, detailing how speculative decoding, language‑model cascades, distillation, CALM, accurate quantized training, and the Mixture‑of‑Recursions architecture together form a multi‑layered strategy to cut inference cost, boost throughput, and sustain the company’s AI moat.
The article examines Google’s AI resurgence after Sergey Brin’s comeback, detailing the evolution of TPU hardware from v1 to v7, the strategic focus on algorithmic efficiency, comparisons with Nvidia’s B200, the role of JAX/XLA, and how these advances create a powerful competitive moat for Google’s AI infrastructure.
