How to Run Qwen3.6-35B-A3B-MLX-4bit on AMD/Nvidia GPU

Using a native PowerShell script is the absolute quickest way to install this model.

Please follow the instructions listed below to get started.

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

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

🧾 Hash-sum — 287403a78ea6d779b0973c2f3b5c31a4 • 🗓 Updated on: 2026-07-02 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: enough space for background apps and OS overhead Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

The Qwen3.6-35B-A3B-MLX-4bit model represents a significant advancement in open‑source language models, delivering strong performance while maintaining a compact footprint. Built on the A3B architecture, it leverages 4‑bit MLX quantization to achieve efficient inference on consumer‑grade hardware. With 35 billion parameters and an 8K token context window, the model excels at both reasoning and generation tasks. It supports multi‑language understanding and integrates seamlessly with the MLX ecosystem for optimized deployment. The following table summarizes the key technical specifications that differentiate this model from its predecessors.

Overall, the combination of high capacity and low‑bit quantization makes Qwen3.6-35B-A3B-MLX-4bit an attractive choice for developers seeking powerful yet resource‑friendly AI solutions.

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