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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.

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💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.

Qwen3-Coder-Next APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Qwen3-Coder-Next.

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.

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: 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
Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)

Run with LocalAI

local-ai run mudler/Qwen3-Coder-Next-APEX-GGUF@Qwen3-Coder-Next-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.

mudler/Qwen3-Coder-Next-APEX-GGUF