Join waitlist
Back to products
DeepSeek-VL2

DeepSeek-VL2

MoE vision-language, now easier to access

Website github.com
Overview

What it is

DeepSeek-VL2 are open-source vision-language models with strong multimodal understanding, powered by an efficient MoE architecture. Easily test them out with the new Hugging Face demo.

Intent

I need it when

Build multimodal AI applications that understand both images and text

DeepSeek-VL2 is a vision-language model that processes images and text together, enabling visual question answering, document understanding, and visual grounding tasks. Users can integrate it into applications for advanced multimodal reasoning without relying on proprietary APIs.

Perform optical character recognition and document analysis at scale

DeepSeek-VL2 demonstrates strong capabilities in OCR, document/table/chart understanding, and visual grounding. Users can leverage these capabilities for document processing pipelines, data extraction, and structured information retrieval from images.

Deploy efficient AI models with limited computational resources

DeepSeek-VL2 offers three variants (Tiny with 1.0B, Small with 2.8B, and full with 4.5B activated parameters) using Mixture-of-Experts architecture. This allows researchers and developers to choose models matching their hardware constraints while maintaining competitive performance.

Implement visual grounding and object localization in applications

DeepSeek-VL2 supports visual grounding with special tokens (<|ref|>, <|grounding|>) that enable object localization and bounding box generation. Users can build applications that identify and locate specific objects within images based on natural language descriptions.

Research and experiment with open-source vision-language models

DeepSeek-VL2 is released on Hugging Face with full model weights, inference code, and a research paper. Researchers can download, modify, and experiment with the model architecture, training approaches, and fine-tuning for custom tasks.

Drop

Not a fit when

  • User requires commercial support or SLA guarantees; DeepSeek-VL2 is open-source with community support only
  • User needs a managed API service with usage-based billing; this is a self-hosted model requiring GPU infrastructure
  • User lacks GPU resources (80GB+ VRAM for full model); smaller variants require 40GB+ VRAM minimum
  • User requires real-time inference at scale without infrastructure management; self-hosting demands operational overhead
  • User needs proprietary model weights with commercial licensing; DeepSeek-VL2 uses open-source licenses (MIT for code, model license provided)
Commercials

Pricing

Open-source model available for free download; no commercial pricing model evident