How to Autostart Qwen3.5-9B-AWQ Full Speed NPU Mode 5-Minute Setup

The fastest method for installing this model locally is by using Docker.

Use the instructions provided below to complete the setup.

The script takes care of fetching the multi-gigabyte model weights.

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

📤 Release Hash: 237d886c1558359ddd61b01d3730e1b3 • 📅 Date: 2026-06-27
  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk Space: free: 80 GB on system drive for scratch space
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The Qwen3.5-9B-AWQ is a 9‑billion parameter language model designed for balanced performance and inference efficiency. It leverages Activation‑aware Quantization (AWQ) to reduce memory footprint while preserving high accuracy on a wide range of tasks. The model supports an extended context length of 8K tokens, enabling it to handle longer documents and complex reasoning chains. Trained on diverse multilingual data, it excels in code generation, dialogue, and factual QA across multiple languages. A compact yet powerful option for developers who need fast inference on consumer‑grade hardware. Key technical specifications are summarized below:

Spec Value
Parameters 9 B
Quantization AWQ (4‑bit)
Context Length 8K tokens
Primary Use‑cases Code, chat, QA
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