This is a submission for the Gemma 4 Challenge: Build with Gemma 4 After some basic benchamarking - I realized that vLLM defaults to the standard precision instead of the memory optimized Brain Float data type. The full benchmark suite was re-run via MCP and the brain float results were compared to the standard precision results. model: google/gemma-4-26B-A4B-it ✦ The absolute scale benchmark for Gemma 4 (26B-A4B-it) on TPU v6e-4 has successfully completed. 🏁 Final Benchmark Results The sweep confirms that the TPU v6e-4 cluster can handle massive parallel loads, maintaining a peak prefill throughput of nearly 0.5 Million tokens/sec at the model's absolute context ceiling.…