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How to Autostart Qwen3.5-9B-AWQ Locally via Ollama 2 with Native FP4 Direct EXE Setup

How to Autostart Qwen3.5-9B-AWQ Locally via Ollama 2 with Native FP4 Direct EXE Setup

The most rapid route to a local installation of this model is through 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-sum — 5a8400a280a4b1689fa5f9ce8d48e744 • šŸ—“ Updated on: 2026-06-26



  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk: high-speed SSD 120 GB to cache model layers
  • 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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