Qwen3.6-35B-A3B-MLX-8bit Locally (No Cloud)

Deploying this model locally is quickest when done via Docker.

Follow the step-by-step instructions below.

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

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

📊 File Hash: 818afdc0abcbd852b70cc8f52c407773 — Last update: 2026-06-24 Verify Processor: high single-core performance needed for token latency RAM: 48 GB needed to prevent memory swapping to disk Disk Space: free: 80 GB on system drive for scratch space GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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