As large language models (LLMs) continue to grow in scale, the underlying hardware used for training has become the single most critical factor in a project’s success. The industry is currently locked in a fascinating architectural battle: the general-purpose power of NVIDIA’s GPUs versus the purpose-built efficiency of Google’s Tensor Processing Units (TPUs).
For engineers and architects building on Google Cloud Platform (GCP), the choice between an A100/H100 GPU cluster and a TPU v4/v5p pod is not merely a matter of cost — it is a decision that impacts software architecture, data pipelines, and convergence speed. This article provides a deep-dive technical analysis of these two architectures through the lens of real-world LLM training performance.