Fine-Tuning and Serving Gemma 4 31B on Google Cloud TPU: A Technical Comparison with GPU Baselines
arXiv:2605.25645v2 Announce Type: replace-cross 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 convent
The continuous evolution of large language models and the increasing demand for efficient, scalable compute solutions are driving the need for optimized hardware-software integration.
Demonstrating the fine-tuning and serving of advanced LLMs on Google Cloud TPUs provides a critical alternative to GPU-centric infrastructure, potentially enabling new performance benchmarks and cost efficiencies.
This technical comparison validates TPUs as a viable and potentially superior platform for industrial-scale LLM operations, challenging the sole dominance of GPUs in this domain.
- · Google Cloud
- · JAX/Tunix/Qwix ecosystem developers
- · Organizations requiring scaled LLM operations
- · GPU-only cloud providers (in specific LLM workloads)
- · Organizations locked into GPU-native stacks
Increased adoption of Google Cloud's TPU offerings for advanced AI workloads, particularly LLM fine-tuning and inference.
Accelerated development of AI models and tools optimized for TPU architectures, diversifying the AI compute landscape.
Potential for new AI services and applications that leverage the unique performance characteristics and cost structures of TPUs.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI