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.
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).
The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only 6/128 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers (first/last 5) and keeping attention, SSM/Mamba, and shared expert tensors at higher precision.
See the APEX project for full details, technical report, and scripts.
Nano (experimental tier)
The APEX Nano tier pushes mid-layer routed experts to IQ2_XXS (2.06 bpw), near-edge to IQ2_S, edges to Q3_K, with shared experts kept at Q5_K. About 5% smaller than Mini with modest quality cost — viable only on MoE thanks to sparse per-token expert activation. Requires imatrix.
Benchmarks pending. Feedback welcome.
Multimodal Support
This is the Omni variant — supports text + vision + audio inputs. The included mmproj.gguf (sourced from unsloth) provides:
Vision: RADIO ViT encoder (1280-dim)
Audio: Parakeet encoder (1024-dim, 24 layers)
Pass --mmproj mmproj.gguf to llama.cpp / LocalAI to enable multimodal inference. Note: llama.cpp's audio output is not yet supported in mtmd — audio input only.
