Fine-Tuning and Serving Gemma 4 31B on Google Cloud TPU: A Technical Comparison with GPU Baselines
2026-05-25 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster ComputingArtificial Intelligence
AI summaryⓘ
The authors show how to fine-tune and run Google's Gemma 4 language model on TPU hardware, which is different from typical GPU setups. They explain the technical changes needed to switch from GPU-based training tools to TPU-compatible ones, including adjustments in code and data handling. Their experiments show TPU training is faster and cheaper than GPUs, and TPU inference responds quicker while maintaining similar speed. This work provides a practical guide for using Gemma 4 on TPUs in real-world applications.
Gemma 4TPUGPULoRAJAXFSDPmodel fine-tuninginference latencyvLLMcheckpointing
Authors
Jatin Kishnani, Mayank Goel, Amit Singh, Pulkit Agrawal, Sairanjan Mishra
Abstract
We present the first end-to-end demonstration of fine-tuning and serving Google's Gemma 4 31B model on TPU hardware, providing an empirical comparison of TPU and GPU platforms for large language model adaptation. Using LoRA on a Google TPU v5p-8 for training and TPU v6e-8 (Trillium) for inference, we document the full set of code-level adaptations required to port a GPU-native training recipe, built on PyTorch, HuggingFace TRL, and FSDP, to the JAX + Tunix/Qwix stack. These adaptations span mesh configuration, LoRA module naming conventions, sharding annotation corrections, gradient checkpointing, data pipeline restructuring, and a custom Orbax-to-safetensors checkpoint merging procedure. For inference, we detail the vLLM-TPU Docker setup necessary to serve Gemma 4 on v6e-8 and characterize the resulting latency and throughput profile. Compared with a 2xH100 GPU baseline under identical hyperparameters, TPU training completes 1.61x faster at 2.12x lower cost. Inference throughput is within 3% across platforms, while TPU achieves 2x lower time-to-first-token (235 ms vs. 475 ms). Together, the TPU configuration is 1.82x cheaper for a representative train-plus-service workload. Our work removes a critical gap in the open tooling ecosystem and provides practitioners with a reproducible, production-ready recipe for Gemma 4 deployment on TPU infrastructure.