⚡ 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.
💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.
Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see
mudler/Qwen3.5-35B-A3B-APEX-GGUF.
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.
APEX is brought to you by the
LocalAI team. Developed through human-driven, AI-assisted research. Built on
llama.cpp.
