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.
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 |
| Qwopus3.6-27B-Coder | 27.32B | Q5_K_M | 87.5 | 96.8 | 95.2 | 97.5 | 93.3 | 94.1 |
| 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 |
| North-Mini-Code-1.0† | 30.48B | Q6_K | 73.3 | 60.2 | 70.8 | 95.8 | 86.6 | 77.4 |
† North-Mini-Code-1.0 is a reasoning model, run think-OFF for board parity. ARC-Challenge is reasoning-gated: 60.2 think-OFF to ~95 think-ON (+35), which deflates its q_avg. See the report.
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/
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)
Field Reports
One-shot investigations that don't fit the leaderboard format — claim verification, new-architecture probes, and consumer-hardware autopsies, all measured on the same rig. Newest first.
| Report | Finding |
|---|---|
| Spec-decode three-way (Gemma 4 26B-A4B) · chart | MTP vs EAGLE-3 vs DFlash on one RTX 5090 (vLLM 0.21, sm_120), including the EAGLE-3 leg nobody publishes. Single-stream near-tie: DFlash 2.19x, MTP 2.13x, EAGLE-3 1.69x — DFlash is feast-or-famine (prose 1.04x, repetitive 4.37x), MTP the steady all-rounder. Six consumer-Blackwell fixes to run it at all (NVFP4 to MARLIN, FLEX_ATTENTION for the #42068 attention deadlock) plus a self-caught /metrics parser bug. Dense 31B excluded: no clean quant fits 32GB. |
| Qwopus3.6-27B-Coder · chart | Four legs measured: q_avg 94.1 (#2 thinking-off, beats its base at a smaller quant); "100 tps" MTP verified (96-114 t/s) but the finetuned head accepts worse than the original (1.4-1.6x vs 1.8-2.2x); a perfect 100 Agentic Score — and 57% real SWE-bench resolve, below its own base (63%). Trained on Hermes traces: the in-distribution mirage the reality anchor was built to catch. 67% claim doesn't reproduce. |
| Keye-VL-2.0-30B autopsy · chart | Five measured walls: "lossless 256K" needs 25.8GB of KV alone; the shipped sparse attention is O(N²)-memory (one 30.65GiB allocation at ~32K, measured); 4-bit quant reaches 4.7% of params; the code's API window is two transformers release candidates wide. Does not run on consumer hardware. |
| LocateAnything-3B · chart · raw | ScreenSpot-Pro 55.3% measured vs 60.3 claimed (32GB forces extra downscale; accuracy tracks screenshot size). Real fault line: text 63.2% vs icons 42.7%. PBD parallel box decoding verified at 2.07x on the SDPA fallback. |
| HRM-Text-1B · chart · raw gens | GSM8K 79.5% (claimed 84.5: holds at n=200). Omitting token_type_ids — which every standard harness does — silently costs 26 points. The recurrence bill: a 1.2B that decodes like a ~5B (42.9 tok/s bf16, 4x KV cache). |
| DiffusionGemma vs AR · chart | AR wins at every answer length: diffusion pays a fixed ~3s per 256-token canvas (0.8 effective tok/s on short answers; best case still 2.3x slower). Day-0 public GGUFs were unloadable — convert from source. |
| Gemma 4 31B QAT + MTP · chart | The MTP draft head lifts decode 76 to 125 tok/s (1.67x). QAT's real value is VRAM, not quality: the Q4 footprint is what fits 128K context plus the draft head on one card. |
| NVFP4 vs Q6_K · chart | Qwen3.6-27B: NVFP4 trades ~1pt q_avg against Q6_K. |
| GRPO on one 5090 · chart | Single-GPU RL: +7.66 GSM8K on a 4B. Train-prompt-to-eval-prompt alignment is the lever. |
| Embedding retrieval bench · chart | Local embedding models benchmarked for retrieval quality vs speed on the 5090. |
| Mistral Small 4 speed · chart | Speed profile vs gpt-oss-20b — and a benchmarking trap: reasoning is gated behind reasoning_effort, which defaults off. |
| LFM2.5-VL 1.6B extraction · chart | A 1.6B VL model as a local structured-data extractor. |
| Nex-N2-mini agentic probe | Adaptive Thinking saves 65% of tokens but costs 13pts task success. Superseded by the dedicated Agentic Score leaderboard (below). |
Related Datasets
- witcheer/agentic-score-leaderboard — model-agnostic agentic tool-calling benchmark (7 models, 40 tasks) + the SWE-bench reality anchor
- witcheer/sovereign-asr-bench — local ASR on the 5090: Parakeet-TDT vs Whisper (WER / RTFx / VRAM)
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.