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 NVIDIA DGX Spark (GB10, 128 GB VRAM). Perplexity on wikitext-2-raw, context 2048. Accuracy benchmarks via llama.cpp (400 tasks).
Configuration
Size (GB)
Perplexity
KL mean
HellaSwag
Winogrande
MMLU
ARC
TruthfulQA
tg128 (t/s)
Q8_0
30.3
9.537
0.0031
75.8%
68.0%
39.6%
45.8%
30.0%
57.1
APEX I-Balanced
20.8
9.516
0.0074
76.5%
68.3%
40.2%
46.2%
30.4%
68.5
APEX I-Quality
18.1
9.535
0.0108
75.3%
68.5%
39.8%
44.8%
30.5%
74.1
APEX Quality
18.1
9.560
0.0117
75.5%
68.0%
40.1%
44.5%
31.8%
73.7
APEX Balanced
20.5
9.563
0.0083
75.5%
68.5%
39.6%
45.2%
30.5%
68.1
Unsloth Q5_K_S
19.6
9.513
0.0119
75.3%
68.5%
39.8%
45.2%
30.2%
72.2
Unsloth UD-Q4_K_XL
16.5
9.676
0.0246
76.3%
67.0%
39.7%
47.5%
30.5%
82.3
APEX I-Compact
13.8
9.667
0.0418
76.3%
68.8%
39.0%
44.1%
29.0%
84.5
APEX Compact
13.8
9.765
0.0492
75.0%
67.0%
39.1%
45.8%
30.4%
83.8
APEX Mini
11.3
9.838
0.0862
73.5%
68.8%
39.0%
44.1%
31.0%
91.4
Highlights
APEX I-Balanced beats Q8_0 in PPL (9.516 vs 9.537), HellaSwag (76.5% vs 75.8%), MMLU (40.2% vs 39.6%), and ARC (46.2% vs 45.8%) while being 31% smaller and 20% faster.
APEX I-Compact matches UD-Q4_K_XL quality at 16% less size (13.8 vs 16.5 GB) with higher Winogrande (68.8% vs 67.0%).
APEX Mini (11.3 GB) delivers 91.4 t/s -- fastest of any configuration -- while maintaining viable quality for coding tasks.
Available Files
File
Profile
Size
Best For
Qwen3-Coder-30B-APEX-I-Balanced.gguf
I-Balanced
20.8 GB
Best overall -- beats Q8_0 quality
Qwen3-Coder-30B-APEX-I-Quality.gguf
I-Quality
18.1 GB
Best accuracy with imatrix
Qwen3-Coder-30B-APEX-Quality.gguf
Quality
18.1 GB
Lowest perplexity at this size
Qwen3-Coder-30B-APEX-Balanced.gguf
Balanced
20.5 GB
General purpose, low KL
Qwen3-Coder-30B-APEX-I-Compact.gguf
I-Compact
13.8 GB
Consumer GPUs, best quality at size
Qwen3-Coder-30B-APEX-Compact.gguf
Compact
13.8 GB
Consumer 24 GB GPUs
Qwen3-Coder-30B-APEX-Mini.gguf
Mini
11.3 GB
16 GB VRAM, 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 -- no Wikipedia).
See the APEX project for full details, technical report, and scripts.
Run with LocalAI
local-ai run mudler/Qwen3-Coder-30B-APEX-GGUF@Qwen3-Coder-30B-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.
