Qwen3.6-35B-A3B-MLX-4bit Step-by-Step Windows

The fastest way to get this model running locally is via Docker.

Follow the guidelines below to continue.

The loader auto-caches the model archive (several GBs included).

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

📄 Hash Value: 79573c049f4945837a7da7c25dbf808e | 📆 Update: 2026-06-27 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Storage:100 GB free space for HuggingFace cache folder GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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