Datasets:
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.
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 | HumanEval |
|---|---|---|---|---|---|---|---|
| Gemma 4 31B-it | 30.70B | Q6_K | 87.8 | 97.6 | 92.0 | 97.5 | 95.7 |
| Qwen3.6-27B | 26.90B | Q6_K | 87.9 | 96.9 | 95.4 | 97.3 | 18.9 |
| Qwen3.6-35B-A3B | 34.66B | UD-Q4_K_M | 85.0 | 95.7 | 93.4 | 96.7 | 37.2 |
| Qwen3-Coder-Next | 79.67B | UD-Q2_K_XL | 83.7 | 96.0 | 89.3 | 96.0 | 10.4 |
| Nemotron-Cascade-2 | 31.58B | Q4_K_M | 74.4 | 91.5 | 75.7 | 87.1 | 79.3 |
| gpt-oss-20b | 20.91B | Q4_K_M | 78.6 | 94.6 | 74.5 | 94.8 | 12.2 |
All models benchmarked with 50% stratified sampling (seed=42), thinking disabled. Full per-model reports in
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/
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 — open-source pipeline for speed and quality benchmarks on GGUF and safetensors models.