Qwen3-VL-Embedding-2B Full Speed NPU Mode

Running this model locally is fastest when deployed through Docker.

Follow the guidelines below to continue.

The installer automatically pulls the model (could be multiple GBs).

The installer will automatically analyze your hardware and select the optimal configuration for your system.

🧾 Hash-sum — 5731840893f0f010c6cf2f719adce1fe • 🗓 Updated on: 2026-06-28 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Disk Space: required: fast PCIe 4.0 drive for instant boots Graphics: TensorRT-LLM / vLLM inference engine compatible chip

Qwen3-VL-Embedding-2B is a compact yet powerful multimodal embedding model that processes text, images, and videos into a unified vector space. It leverages a vision-language transformer architecture with 2 billion parameters, delivering state‑of‑the‑art retrieval performance across diverse benchmarks. The model supports high‑resolution visual inputs and can handle up to 2048‑token text sequences, enabling flexible downstream tasks such as image search and cross‑modal retrieval. Its training pipeline incorporates large‑scale paired datasets, ensuring robust semantic alignment between modalities while maintaining computational efficiency. The resulting embeddings are widely adopted in production systems due to their fast inference and low memory footprint.

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