Datasetpublic

Yobitel/google-gemma-2-9b-itllm-quality-arithmetic-mini019e3b97635f

google/gemma-2-9b-it on llm.quality.arithmetic-mini (NVIDIA H100 80GB HBM3) Back to leaderboard Headline metrics Metric Value Unit N Samples 8 N Ok 8 Ok Rate 1 Accuracy 1 Accuracy P05 1 Accuracy P50 1 Accuracy P95 1 TTFT P50 17.0788 ms Total P50 Ms 50.9178 Tokens Out Total 10 Run configuration Model: google/gemma-2-9b-it @ unknown00 Engine: vllm vunknown Quantization: fp16 Hardware: NVIDIA H100 80GB HBM3… See the full description on the dataset page: https://huggingface.co/datasets/Yobitel/google-gemma-2-9b-it__llm-quality-arithmetic-mini__019e3b97635f.

sourceHugging Facecc-by-4.0updated 4mo agoView on Hugging Face

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google/gemma-2-9b-it on llm.quality.arithmetic-mini (NVIDIA H100 80GB HBM3)

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Headline metrics

Metric	Value	Unit
N Samples	8
N Ok	8
Ok Rate	1
Accuracy	1
Accuracy P05	1
Accuracy P50	1
Accuracy P95	1
TTFT P50	17.0788	ms
Total P50 Ms	50.9178
Tokens Out Total	10

Run configuration

—Model: google/gemma-2-9b-it @ unknown00
—Engine: vllm vunknown
—Quantization: fp16
—Hardware: NVIDIA H100 80GB HBM3
—Driver: 580.126.09
—CUDA: 13.0
—Run date: 2026-05-18T14:57:17.663670+00:00
—Seed: 0

Verification

This result is Sigstore-signed and Rekor-logged. Verify:

bash

pip install inferencebench
bench verify hf://datasets/Yobitel/google-gemma-2-9b-it__llm-quality-arithmetic-mini__019e3b97635f/envelope.json

Rekor entry: log index -1

Methodology

See the suite methodology page.

Citation

bibtex

@misc{inferencebench_019e3b97635f,
  title = { google/gemma-2-9b-it on llm.quality.arithmetic-mini },
  author = { {InferenceBench community} },
  year = { 2026 },
  url = { https://huggingface.co/datasets/Yobitel/google-gemma-2-9b-it__llm-quality-arithmetic-mini__019e3b97635f },
}

Published via [InferenceBench](https://github.com/yobitelcomm/bench) — vendor-neutral AI benchmarks.