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mudler/Qwen3.5-35B-A3B-Claude-Distilled-APEX-GGUF

🤗 Hugging Face sourceapache-2.03B activated146 GBGGUF✓ 7 checksumsupdated today
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⚡ Each donation = another big MoE quantized

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.

Qwen3.5-35B-A3B Claude-Distilled APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled.

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

Benchmark Results

Benchmarks coming soon. For reference APEX benchmarks on the same Qwen3.5-MoE architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.

Available Files

File Profile Size Best For
Qwen3.5-35B-A3B-Claude-Distilled-APEX-I-Balanced.gguf I-Balanced ~24 GB Best overall quality/size ratio
Qwen3.5-35B-A3B-Claude-Distilled-APEX-I-Quality.gguf I-Quality ~22 GB Highest quality with imatrix
Qwen3.5-35B-A3B-Claude-Distilled-APEX-Quality.gguf Quality ~22 GB Highest quality standard
Qwen3.5-35B-A3B-Claude-Distilled-APEX-Balanced.gguf Balanced ~24 GB General purpose
Qwen3.5-35B-A3B-Claude-Distilled-APEX-I-Compact.gguf I-Compact ~17 GB Consumer GPUs, best quality/size
Qwen3.5-35B-A3B-Claude-Distilled-APEX-Compact.gguf Compact ~17 GB Consumer GPUs
Qwen3.5-35B-A3B-Claude-Distilled-APEX-I-Mini.gguf I-Mini ~13 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: Qwen3.5-35B-A3B-Claude-Distilled (Qwen3.5-MoE, distilled from Claude 4.6 Opus reasoning)
  • Layers: 40
  • Experts: 256 routed + 1 shared (8 active per token)
  • Total Parameters: ~35B
  • Active Parameters: ~3B per token
  • APEX Config: 5+5 symmetric edge gradient across 40 layers
  • Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)

Run with LocalAI

local-ai run mudler/Qwen3.5-35B-A3B-Claude-Distilled-APEX-GGUF@Qwen3.5-35B-A3B-Claude-Distilled-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.