How to Deploy Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF Offline on PC One-Click Setup

How to Deploy Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF Offline on PC One-Click Setup

The shortest path to running this model is by activating Hyper-V features.

Check out the detailed setup guide below to begin.

Everything happens automatically, including the heavy cloud asset download.

There is no manual tuning required; the builder deploys the best matching configuration.

🗂 Hash: 18c320e5019924918a98ccef18c856c8 • Last Updated: 2026-07-02



  • Processor: next-gen chip for heavy context processing
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Storage:100 GB free space for HuggingFace cache folder
  • Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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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