witcheer/rtx-5090-benchmarks

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.

Results are split by reasoning mode: comparing a thinking-on (reasoning) model's quality against a thinking-off model is apples-to-oranges, so the two groups are ranked separately. q_avg is the mean of the five tasks.

Thinking OFF (non-reasoning · direct answer)

Model	Params	Quant	MMLU	ARC-C	HellaSwag	GSM8K	HumanEval	q_avg
Gemma 4 31B-it	30.70B	Q6_K	87.8	97.6	92.0	97.5	96.3	94.2
Qwen3.6-27B	26.90B	Q6_K	87.9	96.9	95.4	97.3	92.7	94.0
Qwen3.6-35B-A3B	34.66B	UD-Q4_K_M	85.0	95.7	93.3	96.7	95.7	93.3
Qwen3.6-27B	26.90B	NVFP4	87.0	96.7	94.9	97.1	90.2	93.2
Qwen3-Coder-Next	79.67B	UD-Q2_K_XL	83.7	96.0	89.3	96.0	93.3	91.7
Gemma 4 12B-it	11.91B	Q6_K	78.9	94.0	81.6	96.4	87.2	87.6
gpt-oss-20b	20.91B	Q4_K_M	78.6	94.6	74.5	94.8	94.5	87.4
Nemotron-3-Nano	31.58B	UD-Q4_K_XL	74.5	89.9	75.6	90.5	80.5	82.2
Nemotron-Cascade-2	31.58B	Q4_K_M	74.4	91.5	75.7	87.1	79.3	81.6

Thinking ON (reasoning · extended chain-of-thought)

Model	Params	Quant	MMLU	ARC-C	HellaSwag	GSM8K	HumanEval	q_avg
Qwen3.6-35B-A3B	34.66B	UD-Q6_K	94.7	97.0	87.0	92.0	98.0	93.8
gpt-oss-120B¹	116.83B	MXFP4	89.5	95.0	80.0	97.0	98.0	91.9
Qwen3.6-28B-REAP-A3B	28.24B	Q6_K	87.7	95.0	82.0	90.0	94.0	89.7

HumanEval correction (2026-06-04). An earlier harness passed API stop sequences (\ndef, \nclass) that fired mid-reasoning, truncating inline-reasoning models before they emitted code — producing false-low scores (Qwen3-Coder-Next read 10%, not 93%). Every model has since been re-run on the fixed, reasoning-aware harness (no stop sequences, max_tokens=4096, indentation-preserving response handling). A second extraction fix (2026-06-04) makes program assembly format-agnostic — it generates candidate assemblies and keeps whichever one compiles — after Nemotron-3-Nano exposed a case where the model indents only the first body line differently (raw HumanEval read 21%; corrected to 80.5%). Do not cite any HumanEval figure published before this date.

Why two tables. Thinking-off rows answer directly; thinking-on rows emit an extended reasoning chain first. The two modes are not comparable on the same axis — including on MCQ/GSM8K — so they are ranked separately. Within a family, turning thinking on trades raw knowledge recall for reasoning depth (compare Qwen3.6-35B-A3B in both tables: MMLU 85.0 → 94.7).

¹ 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. It and the other two thinking-on rows were run on a ~100-item-per-task subset (MMLU 2/subject).

Sampling. MMLU & HellaSwag use 50% stratified sampling (seed=42); ARC-Challenge, GSM8K, and HumanEval run the full item counts (HumanEval = all 164). 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. MCQ and GSM8K use max_tokens=2048; HumanEval uses max_tokens=4096 with no stop sequences (reasoning models emit code only after long inline reasoning — premature stops were the bug corrected above). 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/

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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)

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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.