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LG AI Research Unlocks Visual Data With EXAONE-4.5-33B

By vramkickedin | | 2 min read
A magnifying glass made of smooth frosted blue glass floats over a neat stack of white paper documents.

LG AI Research has released EXAONE-4.5-33B, an open-weight vision language model designed to process both images and text. The system combines native visual understanding with strong reasoning skills across a wide range of standard testing benchmarks.

This update focuses on document analysis and Korean language tasks while maintaining a 256,000-token context window for long reports. Developers and privacy-minded users can run the architecture locally or deploy it across standard server setups.

Model Size: 67GB & VRAM GPU: requirements vary

Core processing and multimodal tools

  • Processes images alongside text to extract data from charts, forms, and diagrams.
  • Runs with a 256,000-token context window for analyzing lengthy files.
  • Includes a dedicated reasoning mode and a faster non-reasoning option.
  • Supports direct tool calls for external web searches and file management.
  • Works with multiple serving frameworks like TensorRT-LLM, vLLM, and SGLang.

Small teams handling sensitive documents can extract precise information without sending files to third-party cloud services. Professionals who need to verify complex visual data will find the built-in reasoning toggle useful for switching between careful analysis and quick summaries.

Practical deployment notes

Running this model locally requires careful setup depending on your chosen framework. Standard configurations recommend one high-end server GPU or four A100-40GB cards working together, though quantized versions will likely lower these limits for consumer hardware. Users must also install community-maintained forks of common libraries to match the new architecture.

"EXAONE 4.5 extends context length up to 256K tokens, facilitating long-context reasoning and enterprise-scale use cases,"

noted the researchers in a technical report. The team also warns that statistical training methods occasionally produce incorrect facts, so manual verification remains necessary for critical workflows. You can review the full code on GitHub, download the weights from Hugging Face, and study full performance details in the official paper.