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.
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.
