Quick Run Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF 100% Private PC Easy Build

Quick Run Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF 100% Private PC Easy Build

Deploying locally takes the least amount of time when executed through native OS tools.

Kindly follow the on-screen instructions below.

The tool automatically synchronizes and downloads the model database.

The deployment tool scans your environment and chooses the ideal parameters.

📡 Hash Check: 11a2b7a2baef3d2c5cbc27ea97f3d1a7 | 📅 Last Update: 2026-06-26



  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk: 150+ GB for high-context vector database storage
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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