Qwen 3.6-35B-A3B in practice: multimodality, MoE, and RAG in a single pipeline

Qwen 3.6-35B-A3B is one of the most powerful open multimodal MoE-transformers available today. The MarkTechPost team published a detailed end-to-end tutorial demonstrating how to actually use this model in production scenarios — not simply running inference, but building a complete working pipeline.

The Mixture-of-Experts (MoE) architecture with 3.6 billion active parameters out of 35 billion total means each request is processed using only a fraction of the weights. This reduces computational load without noticeable quality loss — and the practical challenge is precisely how to properly orchestrate expert routing and not lose speed.

The tutorial covers several blocks critical for production. The first is adaptive model loading depending on available GPU memory: essential if you're not working on eight A100s and must operate with real hardware. The second is managing "thinking" mode: Qwen 3.

6 can provide a direct answer or deliver an extended chain of reasoning — the authors show how to switch between these modes programmatically. The third is tool calling: connecting external functions, which transforms the model from a chatbot into an agent capable of interacting with APIs and data. A separate section covers RAG — retrieval-augmented generation.

The tutorial demonstrates how to connect an external knowledge base to Qwen and get answers grounded in real documents rather than parametric memory. The final part addresses session persistence: how to preserve dialog context between requests, which is critical for assistants and agents with long task horizons. For developers considering Qwen as an alternative to closed APIs, this material is a practical starting point.

Open weights, real code, and coverage of all key engineering aspects make it a valuable reference when building your own AI products.