Datasets:
Benchmark Report: Ling-3.0-flash-IQ3_XXS (IQ3_XXS)
Date: 2026-08-09
Author: WITCHEER
Platform: NVIDIA GeForce RTX 5090 Benchmark Rig (capsule)
Model
| Field | Value |
|---|---|
| Model | Ling-3.0-flash-IQ3_XXS |
| Parameters | 127.49 B total / 5.1 B active (hybrid MoE, bailingmoe3, 512 experts) |
| Quantization | IQ3_XXS (bloomer010/Ling-3.0-flash-GGUF, SwiGLU clamp metadata fix 2026-08-07) |
| File size | 47.69 GiB |
| Engine | llama.cpp (CUDA 12.8 (patched)) |
Hardware
| Component | Spec |
|---|---|
| GPU | NVIDIA GeForce RTX 5090 |
| CPU | AMD Ryzen 5 9600 |
| RAM | 64GB DDR5-5600 |
| OS | Ubuntu 26.04 LTS |
| CUDA | 12.8 (patched) |
Run context. Upstream llama.cpp support for bailingmoe3 had not merged at run time; this run uses the open PR #26608 branch (head 0266ebca6). All 512 routed experts held in system RAM (--n-cpu-moe 99, -ngl 99), identical harness and settings to the Q3_K_M report. Part of the three-quant ladder measured 2026-08-09; companion piece: offload-curve sweep.
Quality Benchmarks
All benchmarks use generative evaluation via llama-server chat completions. Multiple-choice tasks (MMLU, ARC, HellaSwag) use letter extraction instead of loglikelihood scoring -- results are internally consistent for model comparison but absolute scores may differ from logprob-based evaluations by 5-15%.
Summary
| Benchmark | Score | Metric |
|---|---|---|
| MMLU | 83.31% | accuracy |
| ARC-Challenge | 96.08% | accuracy |
| HellaSwag | 92.19% | accuracy |
| HumanEval | 93.29% | pass@1 |
| GSM8K | 93.03% | exact_match |
MMLU Breakdown by Category
| Category | Score | Correct / Total |
|---|---|---|
| Stem | 84.61% | 1,275 / 1,507 |
| Humanities | 81.48% | 1,289 / 1,582 |
| Social Sciences | 90.44% | 1,495 / 1,653 |
| Other | 78.53% | 1,781 / 2,268 |
Sampled at 50% (seed 42)
Speed Benchmarks
Measured with llama-bench. All layers GPU-offloaded (-ngl 99).
Prompt Processing (tokens/s)
| Context Length | Speed | +/-sigma |
|---|---|---|
| 128 | 28.9 | 13.9 |
| 512 | 207.8 | 37.3 |
| 2048 | 338.1 | 15.6 |
| 4096 | 361.3 | 2.9 |
| 8192 | 369.6 | 1.4 |
| 16384 | 374.4 | 1.1 |
Generation (tokens/s)
| Metric | Speed | +/-sigma |
|---|---|---|
| tg128 | 39.5 | 0.1 |
Methodology
Evaluation Framework
Custom generative evaluators built for this rig. All benchmarks run through llama-server's /v1/chat/completions endpoint.
- Scoring: Generative evaluation (not loglikelihood)
- Thinking: disabled
- MCQ scoring: First valid letter extracted from response (A/B/C/D)
- Sampling: 50% of dataset used
- Temperature: 0 (deterministic)
- Max tokens: 2,048
- GPU offload: All layers (
-ngl 99)
Benchmarked by WITCHEER on the RTX 5090 Benchmark Rig. Source: github.com/notwitcheer/llm-bench-rig/blob/main/reports/ling-3-0-flash-iq3-xxs.md. Dataset: huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/ling-3-0-flash-iq3-xxs.md.