After the leap of TPU v3, does the future point to exascale with heterogeneous pods, new precisions beyond bfloat16, and co-optimized architectures with non-volatile memory for multimodal LLMs?
Wednesday, 10 December 2025
by JOSE ALFONSIN PENA
The development of Tensor Processing Units (TPUs) by Google has significantly accelerated the field of large-scale machine learning, particularly for deep learning models that underpin advances in language, vision, and multimodal artificial intelligence. The leap from TPU v2 to TPU v3 marked a substantial increase in computational throughput, memory bandwidth, and system architecture efficiency, positioning
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Expertise in Machine Learning, Diving into the TPU v2 and v3
Tagged under:
Artificial Intelligence, Exascale, Heterogeneous Computing, Non-Volatile Memory, Numerical Precision, TPU