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How to Run Qwen3.5-9B-MLX-4bit Locally via Ollama 2 For Beginners Windows

Using the Windows Package Manager is the quickest way to trigger the setup.

Proceed by following the technical instructions below.

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

An automated hardware sweep ensures the system will select the best tuning parameters.

🧮 Hash-code: 0564281eb4134860600bb5c69ccbaf10 • 📆 2026-06-29



  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: required: 16 GB absolute minimum for small models
  • Storage:100 GB free space for HuggingFace cache folder
  • Graphics: 12 GB VRAM minimum required for basic quantization

The Qwen3.5-9B-MLX-4bit model delivers strong performance while maintaining a compact footprint thanks to its 9B parameters and 4-bit quantization. Its integration with the MLX framework enables optimized memory usage and accelerated inference on consumer‑grade hardware. The model supports an 8K token context window, allowing it to handle longer dialogues and complex reasoning tasks. Benchmarks show it achieves competitive perplexity scores compared to larger models, making it ideal for deployment in resource‑constrained environments. Additionally, the MLX optimizations reduce latency, providing smooth real‑time responses even on laptops and edge devices.

Parameter Value
Model Name Qwen3.5-9B-MLX-4bit
Parameters 9B
Quantization 4‑bit
Framework MLX
Context Length 8K tokens
Inference Speed >100 tokens/s (GPU)
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