callensxavier/runux-bench-gemma-2-9b-tpu

RunuX-AI Benchmark: google/gemma-2-9b on TPU v5e

Benchmark validation card — This repository documents the inference performance of google/gemma-2-9b when optimized with the RunuX-AI runtime on Google TPU v5e.

Key Results (BS=1, BF16, TPU v5e)

Model Details

• —Base Model: google/gemma-2-9b
• —Parameters: 9.0B
• —Precision: BF16 (bfloat16)
• —Hardware: Google TPU v5e (v5litepod-1, 197 TFLOPS)
• —Runtime: RunuX-AI v0.2.0 (no_std Rust, 23 crates)

Methodology

• —Input: 512 tokens
• —Decode: 128 tokens (greedy, do_sample=False)
• —Warmup: 3 iterations
• —Measurement: 10 iterations (median)
• —See full methodology

Reproduction

# Install baseline framework
pip install torch torch_xla[tpu] transformers accelerate

# Run baseline benchmark
python benchmark_baselines.py --models gemma-2-9b

Citation

@article{callens2026runux,
  title={RunuX-AI: Memory-Efficient, Energy-Aware Inference Runtime for Edge and Cloud Accelerators},
  author={Callens, Xavier},
  year={2026},
  note={Socrate AI Lab}
}

Author

Xavier Callens — Socrate AI Lab (Non-Profit)

• —GitHub: xaviercallens/runux-ai-runtime
• —Dataset: callensxavier/runux-tpu-v5e-benchmarks