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Qwen3.5-9B-MLX-4bit Using Pinokio

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

Make sure you implement the steps mentioned below.

The process automatically pulls down gigabytes of critical model assets.

The automated script takes care of everything, tailoring the setup to your specs.

🧾 Hash-sum — 099fbc5226f11df43eab7e6465b04322 • 🗓 Updated on: 2026-07-01



  • Processor: high single-core performance needed for token latency
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • 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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