tpu-asic-vs-gpu-general-purpose

IN premise — summaries/2026/08/24/wiki-BERT_language_model-chunk-3-chunk-4.md

Created 2026-08-24T17:11:06+00:00

TPUs (Google) are ASICs optimized specifically for matrix multiplication, while GPUs (NVIDIA) are general-purpose parallel processors; both are critical for LLM training and inference at scale

Summary

There are two distinct families of AI accelerator in play: purpose-built chips optimized for the core math of neural networks, and more flexible parallel processors. This distinction matters because hardware choices for training and running large models are not interchangeable; the underlying silicon architecture shapes what workloads can be done and at what cost.