mudler/Qwen3.6-35B-A3B-APEX-GGUF

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.