TPU Takes the Lead: Google's Gemma 4 Model Outpaces GPUs

In a significant stride for AI hardware, the TPU platform has outperformed its GPU counterpart in both cost and speed when fine-tuning and deploying Google's Gemma 4, a 31 billion parameter model. The TPU's edge is demonstrated through a comprehensive setup on TPU v5p-8 for training and TPU v6e-8 for inference, which highlights the stark contrasts between these two powerful platforms.

Breaking Down the Hardware Race

Google's latest demonstration reveals that training on the TPU platform is 1.61 times faster than using a dual H100 GPU setup, while slashing costs by 2.12 times. This cost-efficiency isn't merely a theoretical advantage but stems from practical implementations such as the use of LoRA (Low-Rank Adaptation) on Google's TPU. The fine-tuning involves porting a GPU-native training recipe built on widely-used frameworks like PyTorch and HuggingFace TRL to the JAX and Tunix/Qwix stack. The specification is as follows: key adaptations include mesh configuration adjustments, correcting sharding annotations, and complex data pipeline restructuring.

Inference Efficiency: A Competitive Edge

inference, the TPU platform holds its ground robustly against GPUs. Inference throughput is nearly identical, with only a 3% variance. However, TPU showcases a significant reduction in time-to-first-token, achieving a latency of 235 milliseconds compared to the 475 milliseconds on GPU. This performance gain underlines TPU's potential in real-time applications where speed is critical.

Why Developers Should Care

For developers, these findings are a call to rethink their hardware choices, especially scaling large models like Gemma 4. The key question is: why stick to GPUs when TPUs offer a faster and cheaper alternative? While the transition may require some initial code-level adjustments, the long-term benefits speed and cost can't be ignored. The upgrade introduces three modifications to the execution layer, all of which are documented for reproducibility in the open-source ecosystem.

Notably, this development fills a essential gap in the availability of open tooling for TPUs, encouraging more practitioners to take advantage of this technology. With the potential for significant savings and performance gains, the TPU platform is poised to become the preferred choice for large-scale AI deployments.