Qwen3-VL-235B-A22B-Instruct on Copilot+ PC Offline Setup

A standalone PowerShell module provides the fastest route to local installation.

Check out the detailed setup guide below to begin.

No manual effort needed; the setup auto-ingests the large data.

There is no manual tuning required; the builder deploys the best matching configuration.

📘 Build Hash: f7891996aef3ad117f8b33391cf3ce93 • 🗓 2026-07-05 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: enough space for background apps and OS overhead Disk Space:70 GB free space for full FP16 weights storage Graphics: TensorRT-LLM / vLLM inference engine compatible chip

The Qwen3-VL-235B-A22B-Instruct model combines a massive 235 billion parameters with an A22B architecture to deliver state‑of‑the‑art multimodal understanding. It processes text and images simultaneously, enabling high‑fidelity vision‑language tasks such as caption generation, visual question answering, and diagram interpretation. The model was fine‑tuned on a diverse corpus of web‑scale text and image‑caption pairs, which improves its contextual reasoning and visual grounding. Its context window extends to 32 k tokens, allowing it to retain long‑range dependencies across documents and complex scenes. In benchmark evaluations, Qwen3-VL-235B-A22B-Instruct consistently outperforms prior large multimodal models on both accuracy and efficiency metrics. The accompanying instruction‑tuned variant ensures reliable performance on user‑centric prompts, making it suitable for production‑grade AI assistants.

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