I host 30+ 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 (Adaptive Precision for EXpert Models) quantizations of nex-agi/Nex-N2-mini — an agentic model with Agentic Thinking, post-trained on Qwen3.5-35B-A3B-Base for first-tier coding and long-horizon agentic tasks.
Best overall — imatrix-enhanced, lowest worst-case divergence
Nex-N2-mini-APEX-I-Quality.gguf
I-Quality
Highest quality with imatrix
Nex-N2-mini-APEX-Quality.gguf
Quality
Highest quality (no imatrix)
Nex-N2-mini-APEX-Balanced.gguf
Balanced
General purpose
Nex-N2-mini-APEX-I-Compact.gguf
I-Compact
Consumer GPUs, imatrix-enhanced
Nex-N2-mini-APEX-Compact.gguf
Compact
Consumer GPUs
Nex-N2-mini-APEX-I-Mini.gguf
I-Mini
Smallest viable, fastest inference
mmproj.gguf
Vision projector
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 (first/last 5) 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 ~8/256 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers and keeping attention, SSM/Mamba, and shared-expert tensors at higher precision.
See the APEX project for full details, technical report, and scripts.
Architecture
Model: Nex-N2-mini (Qwen3_5MoeForConditionalGeneration, post-trained on Qwen3.5-35B-A3B-Base)
Layers: 40
Experts: 256 routed + 1 shared (8 active per token)
Total Parameters: ~35B
Active Parameters: ~3B per token
Attention: Hybrid (full attention every 4th layer, linear otherwise)
Vision: Built-in vision encoder (mmproj included)
APEX Config: 5+5 symmetric edge gradient across 40 layers
