---
license: apache-2.0
base_model: Qwen/Qwen3.6-35B-A3B
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - qwen3
  - vlm
  - vision
---

# Qwen 3.6 35B-A3B APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B).

**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant) | [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)

## 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%** |

![APEX vs Baselines](qwen36_apex_benchmarks.png)

### 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](https://github.com/mudler/apex-quant) 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

```bash
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](https://github.com/mudler/LocalAI) team. Developed through human-driven, AI-assisted research. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp).