Datasets:
Download reports/gemma-4-12b-it-q6-k.md from witcheer/rtx-5090-benchmarks: direct link, hf CLI and curl.
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- Download file 2.84 kB
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https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks/resolve/9bb07cc35047ba3301febdf77cbca51cdae0b7d4/reports/gemma-4-12b-it-q6-k.md
- Command line
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hf download hf://datasets/witcheer/rtx-5090-benchmarks@9bb07cc35047ba3301febdf77cbca51cdae0b7d4/reports/gemma-4-12b-it-q6-k.md
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curl -L -o gemma-4-12b-it-q6-k.md https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks/resolve/9bb07cc35047ba3301febdf77cbca51cdae0b7d4/reports/gemma-4-12b-it-q6-k.md
Benchmark Report: gemma-4-12b-it (Q6_K)
Date: 2026-06-04
Author: WITCHEER
Platform: NVIDIA GeForce RTX 5090 Benchmark Rig (capsule)
Model
| Field | Value |
|---|---|
| Model | gemma-4-12b-it |
| Parameters | 11.91 B (dense) |
| Quantization | Q6_K |
| File size | 9.11 GiB |
| Engine | llama.cpp (CUDA 12.8 (patched)) |
Hardware
| Component | Spec |
|---|---|
| GPU | NVIDIA GeForce RTX 5090 |
| CPU | AMD Ryzen 5 9600 |
| RAM | 64GB DDR5-5600 |
| OS | Ubuntu 26.04 LTS |
| CUDA | 12.8 (patched) |
Quality Benchmarks
All benchmarks use generative evaluation via llama-server chat completions. Multiple-choice tasks (MMLU, ARC, HellaSwag) use letter extraction instead of loglikelihood scoring -- results are internally consistent for model comparison but absolute scores may differ from logprob-based evaluations by 5-15%.
Summary
| Benchmark | Score | Metric |
|---|---|---|
| MMLU | 78.86% | accuracy |
| ARC-Challenge | 94.03% | accuracy |
| HellaSwag | 81.62% | accuracy |
| HumanEval | 87.20% | pass@1 |
| GSM8K | 96.36% | exact_match |
MMLU Breakdown by Category
| Category | Score | Correct / Total |
|---|---|---|
| Stem | 77.84% | 1,173 / 1,507 |
| Humanities | 77.81% | 1,231 / 1,582 |
| Social Sciences | 88.57% | 1,464 / 1,653 |
| Other | 73.19% | 1,660 / 2,268 |
Sampled at 50% (seed 42)
Speed Benchmarks
Measured with llama-bench. All layers GPU-offloaded (-ngl 99).
Prompt Processing (tokens/s)
| Context Length | Speed | +/-sigma |
|---|---|---|
| 128 | 5,099 | 452.9 |
| 512 | 7,160 | 149.1 |
| 2048 | 6,788 | 10.4 |
| 4096 | 6,605 | 1.8 |
| 8192 | 6,359 | 5.5 |
| 16384 | 5,846 | 5.2 |
Generation (tokens/s)
| Metric | Speed | +/-sigma |
|---|---|---|
| tg128 | 122.3 | 0.2 |
Methodology
Evaluation Framework
Custom generative evaluators built for this rig. All benchmarks run through llama-server's /v1/chat/completions endpoint.
- Scoring: Generative evaluation (not loglikelihood)
- Thinking: disabled
- MCQ scoring: First valid letter extracted from response (A/B/C/D)
- Sampling: 50% of dataset used
- Temperature: 0 (deterministic)
- Max tokens: 2,048
- GPU offload: All layers (
-ngl 99)
Benchmarked by WITCHEER on the RTX 5090 Benchmark Rig. Source: github.com/notwitcheer/llm-bench-rig/blob/main/reports/gemma-4-12b-it-q6-k.md. Dataset: huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/gemma-4-12b-it-q6-k.md.