⚡ 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.
🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors
💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.
GLM-4.7-Flash APEX GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of GLM-4.7-Flash.
Brought to you by the LocalAI team | APEX Project | Technical Report
Benchmark Results
Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.
Available Files
| File | Profile | Size | Best For |
|---|---|---|---|
| GLM-4.7-Flash-APEX-I-Balanced.gguf | I-Balanced | 21 GB | Best overall quality/size ratio |
| GLM-4.7-Flash-APEX-I-Quality.gguf | I-Quality | 18 GB | Highest quality with imatrix |
| GLM-4.7-Flash-APEX-Quality.gguf | Quality | 18 GB | Highest quality standard |
| GLM-4.7-Flash-APEX-Balanced.gguf | Balanced | 21 GB | General purpose |
| GLM-4.7-Flash-APEX-I-Compact.gguf | I-Compact | 14 GB | Consumer GPUs, best quality/size |
| GLM-4.7-Flash-APEX-Compact.gguf | Compact | 14 GB | Consumer GPUs |
| GLM-4.7-Flash-APEX-I-Mini.gguf | I-Mini | 12 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: GLM-4.7-Flash (Glm4MoeLite)
- Layers: 47 (1 dense + 46 MoE)
- Experts: 64 routed + 1 shared (4 active per token)
- Total Parameters: ~30B
- Attention: Multi-head Latent Attention (MLA, DeepSeek-V2 style)
- APEX Config: 5+5 symmetric edge gradient across 47 layers, MLA-aware tensor mapping
Run with LocalAI
local-ai run mudler/GLM-4.7-Flash-APEX-GGUF@GLM-4.7-Flash-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.