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
: Qwen3-Coder-Next (Qwen3Next)
Layers
: 48
Experts
: 512 routed + shared (10 active per token)
Total Parameters
: ~80B
Active Parameters
: ~8-10B per token
Attention
: Hybrid (standard every 4th layer + linear attention)
Context
: 262K tokens
APEX Config
: 5+5 symmetric edge gradient across 48 layers
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