STACKIT AI Model Serving: New Model Release Qwen3-VL-Embedding-8B as Multi-Modal Embedding
Section titled “STACKIT AI Model Serving: New Model Release Qwen3-VL-Embedding-8B as Multi-Modal Embedding”We are excited to announce the addition of Qwen3-VL-Embedding-8B to our shared LLM model portfolio. This is a state-of-the-art multimodal embedding model designed to bridge the gap between visual and textual data.
Unlike traditional text-only models, Qwen3-VL-Embedding-8B projects both text and images into a unified semantic vector space. This release unlocks powerful Cross-Modal Retrieval capabilities for your applications, allowing you to perform text-to-image search, image-to-text search, and complex multimodal RAG (Retrieval-Augmented Generation) workflows.
Key Upgrades
Section titled “Key Upgrades”This generation delivers comprehensive improvements in vector representation and retrieval accuracy:
- Unified Multimodality: Computes semantic embedding vectors from chat messages containing both text and images.
- High-Fidelity Embeddings: Features an output dimension of 4096 and 8 Billion parameters for deep semantic nuance.
- Extended Context: Supports a maximum input of 32,000 tokens, enabling the processing of dense documents and high-resolution visual inputs.
- Multi-language Reach: Optimized support for over 30 languages.
Explore our full model portfolio, and access detailed examples and tutorials in our documentation. Our Help Center is always at your disposal if you have any questions.