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.

┌─────────────┬──────────────────┬──────────────────┬──────────────────┐

│ Concurrency │ 32K Context TTFT │ 64K Context TTFT │ Peak Prefill TPS │

├─────────────┼──────────────────┼──────────────────┼──────────────────┤

│ 1 User │ 1.289s │ 2.032s │ 32.2K │

│ 64 Users │ 3.994s │ 7.390s │ 426.4K │

│ 256 Users │ 10.202s │ 20.129s…