How to Setup Qwen3.5-122B-A10B 100% Private PC Fully Jailbroken

How to Setup Qwen3.5-122B-A10B 100% Private PC Fully Jailbroken

For the fastest local setup of this model, enabling Windows Features is best.

Execute the commands and steps outlined below.

All large files and heavy weights are downloaded automatically by the script.

The automated script takes care of everything, tailoring the setup to your specs.

📎 HASH: a81ac6abc20873b744034fb28be9fd56 | Updated: 2026-06-28



  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk Space: free: 80 GB on system drive for scratch space
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

Qwen3.5-122B-A10B is a state‑of‑the‑art language model featuring 122 billion parameters and an A10B architecture. It leverages a massive web‑scale training corpus to achieve exceptional performance across a wide range of NLP tasks. The model incorporates advanced attention mechanisms and multi‑layer decoder stacks that enable deep contextual understanding and fluent generation. Benchmark evaluations place it among the top performers, delivering record‑breaking scores in reasoning, comprehension, and code synthesis. Its efficient A10B design balances computational demands with high‑quality output, making it suitable for both research and production environments. Ongoing fine‑tuning initiatives allow developers to customize the model for specialized domains while preserving its core capabilities.

Parameter Value
Model Name Qwen3.5-122B-A10B
Parameters 122 B
Architecture A10B
Training Data Web‑scale corpus
Key Features Advanced attention, multi‑layer decoder
  • Script automating model downloads for OpenCodeInterpreter offline engines
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  • Setup utility enabling modern multi-head attention acceleration keys for host machines
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  • Script downloading experimental weight array tensors for complex model recombination routines
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  • Script fetching custom model merges directly into specific KoboldAI directory trees
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