Deploy Qwen3.6-35B-A3B-MLX-8bit on AMD/Nvidia GPU Windows

The fastest method for installing this model locally is by using Docker.

Just follow the guidelines provided below.

1-click setup: the app automatically fetches the large weight files.

The script runs a quick hardware check to dynamically adjust parameters for elite speed.

🔐 Hash sum: 473d8c21b9e81828f02f893c5aa90954 | 📅 Last update: 2026-06-28
  • CPU: 8-core / 16-thread recommended for orchestration
  • 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 Qwen3.6-35B-A3B-MLX-8bit model delivers state‑of‑the‑art performance while maintaining a compact footprint thanks to its 8‑bit quantization. With 35 billion parameters and optimized architecture, it achieves high accuracy on a wide range of NLP tasks. Built on the MLX framework, the model benefits from enhanced hardware compatibility and reduced memory usage. Its inference latency is notably low, enabling real‑time applications in production environments. The following table summarizes the key technical specifications that differentiate this model from earlier versions. Users can expect consistent results across diverse benchmarks, making it a reliable choice for both research and commercial deployment.

Parameter Value
Model Name Qwen3.6-35B-A3B-MLX-8bit
Parameters 35B
Quantization 8-bit
Framework MLX
Context Length 8K tokens
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