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
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/

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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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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
Sovereign TTS head-to-head: 1.7B Apache beats 4B research-license · chart	Qwen3-TTS-1.7B (Apache) vs Fish-S2-Pro (4B, research license) on one RTX 5090, 150 Seed-TTS-eval EN utterances, both bf16 and neither compiled. Round-trip WER is a tie (0.6% each — Fish's sub-1% claim holds, Qwen matches it); SIM-o 0.699 vs 0.625; but RTFx 2.22× vs 0.39× and first-audio latency 1.72s vs 9.74s. Fish-S2-Pro's serving stack assumes SGLang + torch.compile + datacenter cards (its RTF<0.5 is an H200 number) — out-of-the-box on a consumer GPU the small open model is 5.7× faster at the same intelligibility. Size + serving assumptions, not quality. A compiled-Fish rerun is the obvious follow-up.
GLM-5.2 autopsy: clever MLA+DSA, still datacenter-only · chart	The biggest open-weights drop in months (743B MoE, MIT, 1M context), measured by arithmetic from the published config — not served. Credit first: glm_moe_dsa = MLA + DeepSeek sparse attention compresses the KV cache ~57× (1M = ~88 GiB vs ~4.9 TiB). But the weights can't fit 96GB addressable at any quant (743B needs 1.03 bits/weight; smallest ~1.58-bit = ~147 GB), the 1M KV alone ≈ the whole machine, and DSA saves compute not memory. ~235 GB to use 1M context — past a single H200. Clever ≠ consumer; the home-lab move is to wait for a GLM-5.2-Air.
Qwen3.6-27B pi-tune: the coding tune that works · chart	A community QLoRA SFT of Qwen3.6-27B on REAL non-thinking agent traces, measured controlled vs its base at matched Q6_K across four legs. Quality (93.3 vs 94.0) and synthetic Agentic Score (98.01 vs 98.61) stay flat — but real SWE-bench Verified resolve goes UP 19 → 20/30 (give-ups 8 → 6) and the MTP drafter holds (2.0-2.4× vs base 1.8-2.2×) where Qwopus-Coder's degraded. The first of three Qwen3.6-27B coding tunes to improve real bug-fixing: across all three the synthetic score is a 2.4pt band while real SWE spans 11-20, so training-data provenance (real traces > synthetic distill), not the "agentic coder" label, is what the anchor sees.
Qwable-3.6-27b: the distill every cheap eval passes, SWE-bench fails · chart	A dense Qwen3.6-27B + Fable-5-style SFT, measured controlled vs its base at matched Q4_K_M. Quality (93.4 vs 94.0) AND the synthetic Agentic Score (97.64 vs 98.19) stay flat — but real SWE-bench Verified resolve drops 18 → 11/30 and give-ups rise 7 → 13. Quant ruled out (base Q6 → Q4 = −1 bug). The agentic board would call it neutral; only the reality anchor caught the give-up regression — a 3rd failure mode, and the inverse of the MoE Qwable-v1 (whose synthetic honestly declined).
Qwable-v1: agentic distillation regresses vs base · chart	A Claude-Code/Fable-5-distilled "agentic coder" (Qwen3.6-35B-A3B) measured controlled vs its vanilla base + the Opus-reasoning-distill, same Q5_K_M. Every post-train step LOWERS the agentic score (99.58 → 97.92 → 96.25), and real SWE-bench Verified resolve drops 19 → 11/30 with give-ups nearly doubling (9 → 16 empty patches). Not a mirage — synthetic fairly predicts real here; the distillation regressed a top-tier base (the vanilla base ties best real resolve on the board).
ECHO maze microcosm · chart	ECHO's "world model for free" env-token loss, isolated in a 10M from-scratch maze transformer (behavior cloning; the only A/B is the loss mask). A clean null under both observation regimes — full-obs and walls-only — every gap inside the seed spread, no growth with maze size. The free aux loss is worth exactly that in pure imitation; the reported gain must live in ECHO's on-policy RL coupling, not the loss as a plug-in. (The paper has no maze — this microcosm is original, verified by grepping the source + repo.)
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)

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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.