Datasets:
File size: 2,836 Bytes
5946aab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | # 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](https://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](https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/gemma-4-12b-it-q6-k.md).*
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