Fine-Tuning and Serving Gemma 4 31B on Google Cloud TPU: A Technical Comparison with GPU Baselines
A new research paper details the first end-to-end demonstration of fine-tuning and serving Google's Gemma 4 31B model on Google Cloud TPUs. The study provides an empirical comparison between TPU and GPU platforms for large language model adaptation, documenting the necessary code-level adaptations to port a GPU-native training recipe to a JAX-based stack. Results indicate that TPU training is 1.61x faster and 2.12x cheaper than a GPU baseline, with inference throughput being nearly identical and TPU achieving a 2x lower time-to-first-token. AI
IMPACT Provides a reproducible recipe for deploying Gemma 4 on TPUs, potentially lowering costs and improving efficiency for LLM adaptation.