mudler/Qwen3.6-35B-A3B-APEX-GGUF
mudler • general⚡ Each donation = another big MoE quantized
I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory) — enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant.
If APEX quants are useful to you, your support directly funds those bigger runs.
🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors
💚 Big thanks to Hugging Face for generously donating additional storage — much appreciated.
Qwen 3.6 35B-A3B APEX GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of Qwen/Qwen3.6-35B-A3B.
Brought to you by the LocalAI team | APEX Project | Technical Report
Benchmark Results
All benchmarks run with llama.cpp b8797 on NVIDIA GB10 (122 GB VRAM). Perplexity and KL divergence measured on wikitext-2. HellaSwag zero-shot (400 tasks). KL divergence computed against BF16 reference logits.
APEX vs Baselines (unsloth UD quants)
| Model | Size | PPL ↓ | KL mean ↓ | KL median ↓ | KL max ↓ | HellaSwag ↑ |
|---|---|---|---|---|---|---|
| BF16 (reference) | 65 GB | 6.722 | — | — | — | — |
| Q8_0 | 35 GB | 6.720 | 0.0059 | 0.0022 | 9.72 | 82.5% |
| UD-Q5_K_XL | 25 GB | 6.725 | 0.0083 | 0.0030 | 9.06 | 82.8% |
| UD-Q5_K_S | 24 GB | 6.728 | 0.0095 | 0.0035 | 8.72 | 82.8% |
| APEX I-Balanced | 24 GB | 6.727 | 0.0103 | 0.0041 | 4.53 | 83.0% |
| APEX Balanced | 24 GB | 6.726 | 0.0117 | 0.0047 | 14.14 | 83.0% |
| APEX I-Quality | 22 GB | 6.735 | 0.0141 | 0.0054 | 5.69 | 82.5% |
| APEX Quality | 22 GB | 6.753 | 0.0155 | 0.0060 | 13.01 | 82.8% |
| UD-Q4_K_XL | 21 GB | 6.735 | 0.0134 | 0.0050 | 5.14 | 82.3% |
| UD-Q4_K_M | 21 GB | 6.736 | 0.0138 | 0.0054 | 7.86 | 83.3% |
| APEX I-Compact | 17 GB | 6.857 | 0.0451 | 0.0182 | 8.76 | 83.5% |
| APEX Compact | 17 GB | 6.862 | 0.0614 | 0.0261 | 17.58 | 83.3% |
| UD-Q3_K_M | 16 GB | 6.883 | 0.0435 | 0.0163 | 9.37 | 82.8% |
| APEX I-Mini | 14 GB | 7.238 | 0.0999 | 0.0414 | 9.21 | 82.8% |
Highlights
- APEX I-Balanced (24 GB) achieves the lowest KL max (4.53) of any quant tested — even lower than Q8_0 (9.72). The imatrix dramatically reduces worst-case divergence while matching UD-Q5_K_S on perplexity.
- At 17 GB, APEX I-Compact beats UD-Q3_K_M (16 GB) on PPL (6.857 vs 6.883) and HellaSwag (83.5% vs 82.8%).
- imatrix consistently halves KL max: I-Balanced 4.53 vs Balanced 14.14, I-Quality 5.69 vs Quality 13.01.
- APEX I-Mini (14 GB) delivers usable quality (PPL 7.24, HellaSwag 82.8%) in the smallest package.
Available Files
| File | Profile | Size | Best For |
|---|---|---|---|
| Qwen3.6-35B-A3B-APEX-I-Balanced.gguf | I-Balanced | 24 GB | Best overall — lowest KL max of any quant |
| Qwen3.6-35B-A3B-APEX-I-Quality.gguf | I-Quality | 22 GB | Highest quality with imatrix, 2 GB smaller |
| Qwen3.6-35B-A3B-APEX-Quality.gguf | Quality | 22 GB | Highest quality standard |
| Qwen3.6-35B-A3B-APEX-Balanced.gguf | Balanced | 24 GB | General purpose |
| Qwen3.6-35B-A3B-APEX-I-Compact.gguf | I-Compact | 17 GB | Consumer GPUs, beats UD-Q3_K_M quality |
| Qwen3.6-35B-A3B-APEX-Compact.gguf | Compact | 17 GB | Consumer GPUs |
| Qwen3.6-35B-A3B-APEX-I-Mini.gguf | I-Mini | 14 GB | Smallest viable, fastest inference |
| mmproj.gguf | Vision projector | ~1 GB | Required for image understanding |
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, agentic traces, Wikipedia).
The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only ~8/256 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers (first/last 5) and keeping attention, SSM/Mamba, and shared expert tensors at higher precision.
See the APEX project for full details, technical report, and scripts.
Architecture
- Model: Qwen 3.6 35B-A3B (Qwen/Qwen3.6-35B-A3B)
- Layers: 40
- Experts: 256 routed + shared (8 active per token)
- Total Parameters: ~35B
- Active Parameters: ~3B per token
- Attention: Hybrid (full attention every 4th layer, linear/Mamba otherwise)
- Vision: Built-in vision encoder (mmproj included)
- APEX Config: 5+5 symmetric edge gradient across 40 layers
- Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)
- llama.cpp: Built with b8797
Run with LocalAI
local-ai run mudler/Qwen3.6-35B-A3B-APEX-GGUF@Qwen3.6-35B-A3B-APEX-I-Balanced.gguf
Credits
APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.