add Gemma 4 31B report

reports/gemma-4-31b-it-q6-k.md

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+# Benchmark Report: gemma-4-31B-it (Q6_K)
+
+**Date:** 2026-05-29  
+**Author:** WITCHEER  
+**Platform:** NVIDIA GeForce RTX 5090 Benchmark Rig (capsule)  
+
+---
+
+## Model
+
+| Field | Value |
+|-------|-------|
+| Model | gemma-4-31B-it |
+| Parameters | 30.70 B (dense) |
+| Quantization | Q6_K |
+| File size | 23.47 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** | **87.82%** | accuracy |
+| **ARC-Challenge** | **97.61%** | accuracy |
+| **HellaSwag** | **91.95%** | accuracy |
+| **HumanEval** | **95.73%** | pass@1 |
+| **GSM8K** | **97.50%** | exact_match |
+
+### MMLU Breakdown by Category
+
+| Category | Score | Correct / Total |
+|----------|------:|----------------:|
+| Stem | 87.52% | 1,319 / 1,507 |
+| Humanities | 90.01% | 1,424 / 1,582 |
+| Social Sciences | 92.86% | 1,535 / 1,653 |
+| Other | 82.80% | 1,878 / 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 | 2,486 | 170.3 |
+| 512 | 2,932 | 29.7 |
+| 2048 | 2,751 | 2.4 |
+| 4096 | 2,657 | 1.6 |
+| 8192 | 2,520 | 2.6 |
+| 16384 | 2,316 | 3.1 |
+
+### Generation (tokens/s)
+
+| Metric | Speed | +/-sigma |
+|--------|------:|---------:|
+| tg128 | 52.8 | 0.0 |
+
+---
+
+## 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-4-31b-it-q6-k.md](https://github.com/notwitcheer/llm-bench-rig/blob/main/reports/gemma-4-31b-it-q6-k.md). Dataset: [huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/gemma-4-31b-it-q6-k.md](https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/gemma-4-31b-it-q6-k.md).*