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

Carnice Qwen3.6 MoE 35B-A3B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of samuelcardillo/Carnice-Qwen3.6-MoE-35B-A3B.

Brought to you by the LocalAI team | APEX Project | Technical Report

Available Files

File	Profile	Size	Best For
Carnice-Qwen3.6-MoE-35B-A3B-APEX-I-Balanced.gguf	I-Balanced	24 GB	Best overall quality/size ratio
Carnice-Qwen3.6-MoE-35B-A3B-APEX-I-Quality.gguf	I-Quality	22 GB	Highest quality with imatrix
Carnice-Qwen3.6-MoE-35B-A3B-APEX-Quality.gguf	Quality	22 GB	Highest quality standard
Carnice-Qwen3.6-MoE-35B-A3B-APEX-Balanced.gguf	Balanced	24 GB	General purpose
Carnice-Qwen3.6-MoE-35B-A3B-APEX-I-Compact.gguf	I-Compact	17 GB	Consumer GPUs, best quality/size
Carnice-Qwen3.6-MoE-35B-A3B-APEX-Compact.gguf	Compact	17 GB	Consumer GPUs
Carnice-Qwen3.6-MoE-35B-A3B-APEX-I-Mini.gguf	I-Mini	14 GB	Smallest viable, fastest inference

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: Carnice Qwen3.6 MoE 35B-A3B (fine-tuned for agentic/tool-calling)
Base: Qwen 3.6 35B-A3B
Layers: 40
Experts: 256 routed + shared (8 active per token)
Total Parameters: ~35B
Active Parameters: ~3B per token
Attention: Hybrid (full attention every 4th layer, linear/Mamba otherwise)
APEX Config: 5+5 symmetric edge gradient across 40 layers
Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)
llama.cpp: Built with b8797

Run with LocalAI

local-ai run mudler/Carnice-Qwen3.6-MoE-35B-A3B-APEX-GGUF@Carnice-Qwen3.6-MoE-35B-A3B-APEX-I-Balanced.gguf

Credits

Carnice fine-tune: samuelcardillo
APEX quantization: LocalAI team
Built on llama.cpp