| --- |
| license: mit |
| task_categories: |
| - text-generation |
| tags: |
| - benchmark |
| - inference |
| - llm |
| - nvidia |
| - rtx-5090 |
| - llama-cpp |
| - vllm |
| - speed |
| - quality |
| - mmlu |
| - gsm8k |
| - humaneval |
| - moe |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: benchmarks.csv |
| --- |
| |
| # RTX 5090 LLM Benchmarks |
|
|
| Speed and quality benchmarks for quantized LLMs on NVIDIA RTX 5090 32GB, measured with [llm-bench-rig](https://github.com/notwitcheer/llm-bench-rig). |
|
|
| ## Quality Benchmarks |
|
|
| Generative evaluation through llama-server chat completions. Replicates standard benchmark methodology using custom evaluators — no `lm-evaluation-harness` dependency. |
|
|
| | Model | Params | Quant | MMLU | ARC-C | HellaSwag | GSM8K | HumanEval¹ | |
| |-------|-------:|-------|-----:|------:|----------:|------:|----------:| |
| | gpt-oss-120B² | 116.83B | MXFP4 | 89.5 | 95.0 | 80.0 | 97.0 | 98.0 | |
| | Qwen3.6-28B-REAP-A3B | 28.24B | Q6_K | 87.7 | 95.0 | 82.0 | 90.0 | 94.0 | |
| | Gemma 4 31B-it | 30.70B | Q6_K | 87.8 | 97.6 | 92.0 | 97.5 | — | |
| | Qwen3.6-27B | 26.90B | Q6_K | 87.9 | 96.9 | 95.4 | 97.3 | — | |
| | Qwen3.6-35B-A3B | 34.66B | UD-Q4_K_M | 85.0 | 95.7 | 93.4 | 96.7 | — | |
| | Qwen3-Coder-Next | 79.67B | UD-Q2_K_XL | 83.7 | 96.0 | 89.3 | 96.0 | — | |
| | Nemotron-Cascade-2 | 31.58B | Q4_K_M | 74.4 | 91.5 | 75.7 | 87.1 | — | |
| | gpt-oss-20b | 20.91B | Q4_K_M | 78.6 | 94.6 | 74.5 | 94.8 | — | |
| |
| > ¹ **HumanEval** is shown only for models run on the **corrected, reasoning-aware harness** (no premature stop sequences, indentation-preserving response handling). The earlier harness systematically **understated** models that reason inline, so those rows' HumanEval is **withheld pending re-run** — do not cite the prior figures. gpt-oss-120B and Qwen3.6-28B-REAP use the fixed harness. |
| > |
| > ² **gpt-oss-120B** runs via MoE CPU-offload (`--n-cpu-moe 20`) — it does not fit 32GB VRAM (59GB model); ~30GB VRAM + the rest in system RAM, ~47 tok/s generation vs the full-VRAM models. |
| > |
| > The two top rows were run **reasoning-on** on a ~100-item-per-task subset (MMLU 2/subject); earlier rows used the full item counts in the Methodology table, reasoning-off. The two sets are **not directly comparable** on MCQ/GSM8K either. |
| |
| > All models benchmarked with 50% stratified sampling (seed=42), thinking disabled. Full per-model reports in [`reports/`](reports/). |
| |
| ### Methodology |
| |
| | Benchmark | Dataset | Few-shot | Scoring | Items | |
| |-----------|---------|----------|---------|------:| |
| | MMLU | `cais/mmlu` | 5-shot | Letter extraction (A/B/C/D) | 14,042 | |
| | ARC-Challenge | `allenai/ai2_arc` | 25-shot | Letter extraction | 1,172 | |
| | HellaSwag | `Rowan/hellaswag` | 10-shot | Letter extraction | 10,042 | |
| | GSM8K | `openai/gsm8k` | 5-shot CoT | Exact numeric match | 1,319 | |
| | HumanEval | `openai/openai_humaneval` | 0-shot | pass@1 (code execution) | 164 | |
|
|
| All benchmarks run at `temperature=0` with `max_tokens=2048` (accommodates thinking/reasoning models). Multiple-choice tasks use generative letter extraction instead of loglikelihood scoring — scores are internally consistent for model comparison but may differ from logprob-based evaluations by 5-15%. |
|
|
| Full per-model reports with MMLU category breakdowns, parse reliability stats, and speed data: [`reports/`](reports/) |
|
|
| --- |
|
|
| ## Speed Benchmarks |
|
|
| ### What's measured |
|
|
| - **Prompt processing (pp)**: parallel batched token throughput at context lengths 128, 512, 2048, 4096, 8192, 16384 |
| - **Text generation (tg)**: sequential autoregressive token throughput at 128 tokens |
| - All models fully GPU-offloaded (ngl=99) |
|
|
| ### Speed data schema |
|
|
| | Column | Description | |
| |--------|-------------| |
| | `model` | Model name | |
| | `architecture` | Dense or MoE (with active param count) | |
| | `params_b` | Total parameters in billions | |
| | `quant` | Quantization method | |
| | `size_gib` | File size in GiB | |
| | `engine` | Inference engine (llama.cpp or vLLM) | |
| | `backend` | Compute backend (CUDA) | |
| | `gpu` | GPU model | |
| | `vram_gb` | VRAM in GB | |
| | `test` | Benchmark test (pp128, pp512, ..., tg128) | |
| | `tokens_per_sec` | Throughput in tokens/second | |
| | `stddev` | Standard deviation | |
| | `date` | Benchmark date | |
|
|
| ### Key findings |
|
|
| MoE (3B active) vs Dense (27B) on same-family Qwen3.6 models: |
| - Prompt processing: **2.4x faster** across all context lengths |
| - Text generation: **3.5x faster** (271 vs 77 t/s) |
| - Both degrade ~17% at 16K context (attention + VRAM, not parameter count) |
|
|
| --- |
|
|
| ## Hardware |
|
|
| | Component | Spec | |
| |-----------|------| |
| | GPU | NVIDIA GeForce RTX 5090 32GB (Blackwell, sm_120a) | |
| | CPU | AMD Ryzen 5 9600 (6c/12t) | |
| | RAM | 64GB DDR5-5600 | |
| | OS | Ubuntu 26.04 LTS | |
| | CUDA | 12.8 (patched for glibc 2.41) | |
| |
| ## Tooling |
| |
| All benchmarks generated with [llm-bench-rig](https://github.com/notwitcheer/llm-bench-rig) — open-source pipeline for speed and quality benchmarks on GGUF and safetensors models. |
| |
