⚡ Each donation = another big MoE quantized
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.
Holo3-35B-A3B APEX GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of Holo3-35B-A3B.
Brought to you by the LocalAI team | APEX Project | Technical Report
Benchmark Results
Benchmarks coming soon. For reference APEX benchmarks on the same Qwen3.5-MoE architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.
Available Files
| File | Profile | Size | Best For |
|---|---|---|---|
| Holo3-35B-A3B-APEX-I-Balanced.gguf | I-Balanced | ~24 GB | Best overall quality/size ratio |
| Holo3-35B-A3B-APEX-I-Quality.gguf | I-Quality | ~22 GB | Highest quality with imatrix |
| Holo3-35B-A3B-APEX-Quality.gguf | Quality | 22 GB | Highest quality standard |
| Holo3-35B-A3B-APEX-Balanced.gguf | Balanced | 24 GB | General purpose |
| Holo3-35B-A3B-APEX-I-Compact.gguf | I-Compact | ~17 GB | Consumer GPUs, best quality/size |
| Holo3-35B-A3B-APEX-Compact.gguf | Compact | 17 GB | Consumer GPUs |
| Holo3-35B-A3B-APEX-I-Mini.gguf | I-Mini | ~13 GB | Smallest viable, fastest inference |
| mmproj.gguf | Vision projector | ~858 MB | 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).
See the APEX project for full details, technical report, and scripts.
Architecture
- Model: Holo3-35B-A3B (Qwen3.5-MoE variant, VLM)
- Layers: 40 (30 linear attention + 10 full attention)
- Experts: 256 routed + 1 shared (8 active per token)
- Total Parameters: ~35B
- Active Parameters: ~3B per token
- Vision: Built-in vision encoder (mmproj included)
- APEX Config: 5+5 symmetric edge gradient across 40 layers
Run with LocalAI
local-ai run mudler/Holo3-35B-A3B-APEX-GGUF@Holo3-35B-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.