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Qwen3.5-9B-MLX-4bit PC with NPU One-Click Setup Full Method

Qwen3.5-9B-MLX-4bit PC with NPU One-Click Setup Full Method

Docker offers the quickest path to setting up this model locally.

Use the instructions provided below to complete the setup.

Completing the installation grants you full access to everything you hoped to achieve with this deployment.

šŸ” Hash sum: d463554a89686be339a4d1c0b6fc2c73 | šŸ“… Last update: 2026-06-25



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Storage:100 GB free space for HuggingFace cache folder
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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