gpt-oss-120b MXFP4 5090 report

reports/gpt-oss-120b-mxfp4.md

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+# Benchmark Report: gpt-oss-120b (mxfp4)
+
+**Date:** 2026-06-03  
+**Author:** WITCHEER  
+**Platform:** NVIDIA GeForce RTX 5090 Benchmark Rig (capsule)  
+
+---
+
+## Model
+
+| Field | Value |
+|-------|-------|
+| Model | gpt-oss-120b |
+| Parameters | 116.83 B (dense) |
+| Quantization | mxfp4 |
+| File size | 0.01 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** | **89.47%** | accuracy |
+| **ARC-Challenge** | **95.00%** | accuracy |
+| **HellaSwag** | **80.00%** | accuracy |
+| **HumanEval** | **98.00%** | pass@1 |
+| **GSM8K** | **97.00%** | exact_match |
+
+### MMLU Breakdown by Category
+
+| Category | Score | Correct / Total |
+|----------|------:|----------------:|
+| Stem | 97.22% | 35 / 36 |
+| Humanities | 83.33% | 20 / 24 |
+| Social Sciences | 88.46% | 23 / 26 |
+| Other | 85.71% | 24 / 28 |
+
+---
+
+## Speed Benchmarks
+
+Measured with `llama-bench`. MoE experts offloaded to CPU (`--n-cpu-moe 20`), attention/active path on GPU (`-ngl 99`). Model is 59GB and does not fit 32GB VRAM; tuned to ~30GB VRAM with the rest in system RAM.
+
+### Prompt Processing (tokens/s)
+
+| Context Length | Speed | +/-sigma |
+|---------------:|------:|---------:|
+| 128 | 124.5 | 33.1 |
+| 512 | 484.4 | 37.3 |
+| 2048 | 588.2 | 15.0 |
+
+### Generation (tokens/s)
+
+| Metric | Speed | +/-sigma |
+|--------|------:|---------:|
+| tg128 | 46.5 | 0.4 |
+
+---
+
+## 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:** enabled
+- **MCQ scoring:** First valid letter extracted from response (A/B/C/D)
+- **Temperature:** 0 (deterministic)
+- **Max tokens:** 2,048 (MCQ/GSM8K); HumanEval 4,096, no stop sequences (reasoning models emit code after long inline reasoning)
+- **GPU offload:** MoE experts of first 20 layers on CPU (`--n-cpu-moe 20`); attention + active path on GPU. ~30GB of 32GB VRAM, remainder in RAM.
+
+---
+
+*Benchmarked by WITCHEER on the RTX 5090 Benchmark Rig. Source: [github.com/notwitcheer/llm-bench-rig/blob/main/reports/gpt-oss-120b-mxfp4.md](https://github.com/notwitcheer/llm-bench-rig/blob/main/reports/gpt-oss-120b-mxfp4.md). Dataset: [huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/gpt-oss-120b-mxfp4.md](https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/gpt-oss-120b-mxfp4.md).*