Uncensored Qwen3.5-122B Heretic GGUFs Out

🔑 Enhanced Key Takeaways

• •Qwen3.5-122B-A10B is a mixture-of-experts model with 122 billion total parameters but only 10 billion active per token, enabling faster inference than dense models while maintaining flagship-level capability[1].
• •The model supports a 262k context window extendable to approximately 1 million tokens, significantly exceeding standard LLM context lengths and enabling processing of very long documents[1].
• •GGUF quantization variants (particularly Q4_K_M) have demonstrated remarkable safety in post-quantization evaluation, with ternary quantization (TQ1_0) reducing memory from ~800GB to ~94GB while maintaining only ~18.4% benchmark error increase[3].

🛠️ Technical Deep Dive

• •Architecture: Mixture-of-experts design with 256 experts using gated routing mechanism[1]
• •Parameters: 122 billion total parameters with 10 billion active per token[1]
• •Context Window: 262k native context, extendable to ~1 million tokens[1]
• •Quantization Support: Multiple GGUF formats available including Q4_K_M (recommended balance), Q2_K_XL, Q3_K_XL, Q4_K_XL, and ternary (TQ1_0)[2][3]
• •Memory Requirements: Ternary quantization ~94GB, 2-bit quantization ~137GB, 4-bit MXFP4 ~256GB[3]
• •Inference Framework: Compatible with llama.cpp for local deployment on systems with recent NVIDIA drivers and CUDA[1]
• •Performance Baseline: Achieves flagship-level capability while running faster than dense 27B models[1]

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline