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HomeEmbeddingsDeploy Qwen3.6-35B-A3B-MLX-8bit Windows 11 For Low VRAM (6GB/8GB) No-Code Guide

Deploy Qwen3.6-35B-A3B-MLX-8bit Windows 11 For Low VRAM (6GB/8GB) No-Code Guide

June 28, 2026
NVS
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Deploy Qwen3.6-35B-A3B-MLX-8bit Windows 11 For Low VRAM (6GB/8GB) No-Code Guide

If you want the fastest local installation for this model, use Docker.

Follow the guidelines below to continue.

Next, start the model by running the docker-compose command.

📡 Hash Check: 93e6bbe39887435d3b70df19a4330b44 | 📅 Last Update: 2026-06-23



  • CPU: modern architecture (Zen 3 / Alder Lake minimum)
  • RAM: required: 16 GB absolute minimum for small models
  • Disk: 150+ GB for high-context vector database storage
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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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