Full Deployment Qwen3.6-35B-A3B-MLX-8bit with 1M Context

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

Please follow the instructions listed below to get started.

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

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🔗 SHA sum: 70d93ae47914107bf5ef7fda5ce41a72 | Updated: 2026-06-27 Verify CPU: multi-threading optimized for fast prompt processing RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: high memory bandwidth GPU for next-gen local AI pipeline

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.

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