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How to Autostart Qwen3.5-9B-MLX-8bit Using Pinokio No-Internet Version 2026/2027 Tutorial Windows

2026 年 6 月 29 日

How to Autostart Qwen3.5-9B-MLX-8bit Using Pinokio No-Internet Version 2026/2027 Tutorial Windows

If you want the fastest local installation for this model, use Docker.

Refer to the instructions below to proceed.

The installer automatically pulls the model (could be multiple GBs).

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

📎 HASH: e6c707526e5c88501486d9a80c08cab9 | Updated: 2026-06-26



  • CPU: modern architecture (Zen 3 / Alder Lake minimum)
  • RAM: enough space for background apps and OS overhead
  • Storage:100 GB free space for HuggingFace cache folder
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

The Qwen3.5-9B-MLX-8bit model delivers high‑performance language understanding with a balanced trade‑off between accuracy and computational efficiency. Built on the MLX framework, it leverages 8‑bit quantization to reduce memory footprint while preserving core linguistic capabilities. With 9 billion parameters and a context window of up to 8K tokens, the model can handle complex reasoning tasks and long‑form generation. Its optimized architecture enables fast inference on consumer‑grade hardware, making advanced AI accessible without specialized GPUs. The model has been fine‑tuned on diverse corpora, ensuring robust performance across multilingual benchmarks and domain‑specific applications. Developers benefit from its open‑source nature, allowing seamless integration into production pipelines and custom AI solutions.

Spec Value
Model Name Qwen3.5-9B-MLX-8bit
Parameter Count 9 B
Quantization 8‑bit
Context Length 8K tokens
Framework MLX
License Open Source
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