mudler / Qwen3-Coder-30B-APEX-GGUF

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Total runs: 4.5K
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7-day runs: -216
30-day runs: 214
Model's Last Updated: August 17 2026

Introduction of Qwen3-Coder-30B-APEX-GGUF

Model Details of Qwen3-Coder-30B-APEX-GGUF

Qwen3-Coder-30B-A3B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Qwen3-Coder-30B-A3B-Instruct .

Brought to you by the LocalAI team | APEX Project | Technical Report

Benchmark Results

All measurements on NVIDIA DGX Spark (GB10, 128 GB VRAM). Perplexity on wikitext-2-raw, context 2048. Accuracy benchmarks via llama.cpp (400 tasks).

Configuration Size (GB) Perplexity KL mean HellaSwag Winogrande MMLU ARC TruthfulQA tg128 (t/s)
Q8_0 30.3 9.537 0.0031 75.8% 68.0% 39.6% 45.8% 30.0% 57.1
APEX I-Balanced 20.8 9.516 0.0074 76.5% 68.3% 40.2% 46.2% 30.4% 68.5
APEX I-Quality 18.1 9.535 0.0108 75.3% 68.5% 39.8% 44.8% 30.5% 74.1
APEX Quality 18.1 9.560 0.0117 75.5% 68.0% 40.1% 44.5% 31.8% 73.7
APEX Balanced 20.5 9.563 0.0083 75.5% 68.5% 39.6% 45.2% 30.5% 68.1
Unsloth Q5_K_S 19.6 9.513 0.0119 75.3% 68.5% 39.8% 45.2% 30.2% 72.2
Unsloth UD-Q4_K_XL 16.5 9.676 0.0246 76.3% 67.0% 39.7% 47.5% 30.5% 82.3
APEX I-Compact 13.8 9.667 0.0418 76.3% 68.8% 39.0% 44.1% 29.0% 84.5
APEX Compact 13.8 9.765 0.0492 75.0% 67.0% 39.1% 45.8% 30.4% 83.8
APEX Mini 11.3 9.838 0.0862 73.5% 68.8% 39.0% 44.1% 31.0% 91.4
Highlights
  • APEX I-Balanced beats Q8_0 in PPL (9.516 vs 9.537), HellaSwag (76.5% vs 75.8%), MMLU (40.2% vs 39.6%), and ARC (46.2% vs 45.8%) while being 31% smaller and 20% faster .
  • APEX I-Compact matches UD-Q4_K_XL quality at 16% less size (13.8 vs 16.5 GB) with higher Winogrande (68.8% vs 67.0%).
  • APEX Mini (11.3 GB) delivers 91.4 t/s -- fastest of any configuration -- while maintaining viable quality for coding tasks.
Available Files
File Profile Size Best For
Qwen3-Coder-30B-APEX-I-Balanced.gguf I-Balanced 20.8 GB Best overall -- beats Q8_0 quality
Qwen3-Coder-30B-APEX-I-Quality.gguf I-Quality 18.1 GB Best accuracy with imatrix
Qwen3-Coder-30B-APEX-Quality.gguf Quality 18.1 GB Lowest perplexity at this size
Qwen3-Coder-30B-APEX-Balanced.gguf Balanced 20.5 GB General purpose, low KL
Qwen3-Coder-30B-APEX-I-Compact.gguf I-Compact 13.8 GB Consumer GPUs, best quality at size
Qwen3-Coder-30B-APEX-Compact.gguf Compact 13.8 GB Consumer 24 GB GPUs
Qwen3-Coder-30B-APEX-Mini.gguf Mini 11.3 GB 16 GB VRAM, fastest inference
What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient -- edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling -- no Wikipedia).

See the APEX project for full details, technical report, and scripts.

Run with LocalAI
local-ai run mudler/[email protected]
Credits

APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp .

Runs of mudler Qwen3-Coder-30B-APEX-GGUF on huggingface.co

4.5K
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24-hour runs
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3-day runs
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7-day runs
214
30-day runs

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