mudler/Qwen3.5-122B-A10B-APEX-GGUF

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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.

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💚 Big thanks to Hugging Face for generously donating additional storage — much appreciated.

Qwen3.5-122B-A10B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Qwen3.5-122B-A10B.

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

Benchmark Results

All measurements on 8xRTX PRO 6000 Blackwell (768 GB VRAM). Perplexity on wikitext-2-raw, context 512. Accuracy benchmarks via llama.cpp (400 tasks each).

Configuration	Size (GB)	Perplexity	KL mean	HellaSwag	Winogrande	MMLU	ARC	tg128 (t/s)
Q8_0 (Unsloth)	121	4.819	0.004	85.5%	77.3%	44.19	57.19	85.5
Q5_K_S (Unsloth)	~81	4.826	0.007	85.3%	76.0%	43.80	57.86	90.4
UD-Q4_K_XL (Unsloth)	~72	4.829	0.010	84.8%	76.3%	44.25	55.85	91.8
APEX I-Balanced	83.4	4.831	0.008	85.5%	77.8%	43.86	57.86	96.7
APEX I-Quality	72.3	4.838	0.012	85.3%	77.3%	43.86	56.86	99.7
APEX Quality	72.3	4.848	0.013	85.5%	76.3%	44.44	55.52	99.8
APEX Balanced	83.4	4.840	0.008	85.0%	76.3%	43.93	56.86	96.7
APEX I-Compact	55.1	4.978	0.041	84.5%	77.5%	44.06	57.86	106.3
APEX Compact	55.1	5.046	0.049	84.5%	77.8%	43.54	56.19	106.2
APEX I-Mini	44.9	5.306	0.102	84.0%	75.3%	42.83	56.52	110.0

Highlights

• APEX I-Balanced matches or beats Q8_0 on HellaSwag (85.5%), Winogrande (77.8% vs 77.3%), and ARC (57.86 vs 57.19) while being 31% smaller and 13% faster.
• APEX I-Quality (72.3 GB) beats UD-Q4_K_XL at the same size on HellaSwag (85.3% vs 84.8%), Winogrande (77.3% vs 76.3%), and ARC (56.86 vs 55.85).
• APEX I-Compact (55.1 GB) achieves 84.5% HellaSwag and 57.86 ARC at 55% less size than Q8_0 — fastest standard profile at 106 t/s.
• APEX I-Mini (44.9 GB) is the smallest at 63% less size than Q8_0, still 84% HellaSwag, fastest at 110 t/s.
• I-variants consistently improve over standard profiles across PPL, KL, and ARC.

Available Files

File	Profile	Size	Best For
Qwen3.5-122B-A10B-APEX-I-Balanced.gguf	I-Balanced	83.4 GB	Best overall -- matches Q8_0 quality at 31% less size
Qwen3.5-122B-A10B-APEX-I-Quality.gguf	I-Quality	72.3 GB	Best quality at ~72 GB tier
Qwen3.5-122B-A10B-APEX-Quality.gguf	Quality	72.3 GB	Highest MMLU (44.44)
Qwen3.5-122B-A10B-APEX-Balanced.gguf	Balanced	83.4 GB	General purpose, low KL
Qwen3.5-122B-A10B-APEX-I-Compact.gguf	I-Compact	55.1 GB	Consumer multi-GPU, best quality/size ratio
Qwen3.5-122B-A10B-APEX-Compact.gguf	Compact	55.1 GB	Consumer multi-GPU setups
Qwen3.5-122B-A10B-APEX-I-Mini.gguf	I-Mini	44.9 GB	Smallest viable, 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, agentic traces, Wikipedia).

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

Architecture

• Model: Qwen3.5-122B-A10B (Qwen3.5-MoE)
• Layers: 48
• Experts: 256 routed + 1 shared (8 active per token)
• Total Parameters: 122B
• Active Parameters: ~10B per token
• APEX Config: 5+5 symmetric edge gradient across 48 layers

Run with LocalAI

local-ai run mudler/Qwen3.5-122B-A10B-APEX-GGUF@Qwen3.5-122B-A10B-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.