TPU Tensor Processing Unit

The TPU is a custom ASIC chip developed by Google, deeply optimized for large-scale matrix operations under TensorFlow and JAX frameworks, purpose-built for deep learning workloads from the ground up. Cloud TPU provides cloud-based training and inference capabilities. From v2 to the latest Trillium, each generation has seen significant improvements in Matrix Multiply Units (MXU), high-bandwidth memory, and Inter-Chip Interconnect (ICI). TPU v5p and Trillium excel in LLM training scenarios. Combined with Google Cloud's elastic scheduling and GKE orchestration, they enable efficient training clusters and have proven their scalability in training large models such as Gemini. Edge TPU (Coral series) targets edge inference with only 2W power consumption, suitable for IoT, machine vision, and embedded scenarios, providing a complete implementation path for Google's cloud-to-edge AI strategy. The strong binding of TPU to the Google ecosystem (TensorFlow/JAX/Vertex AI) is a key consideration when making a choice; if your team already uses Google Cloud extensively, the cost-performance advantage of TPU is very evident.

This category includes the following AI accelerator chips/compute cards:

• Google Cloud TPU
• Google Coral Edge TPU