Analysis
This exciting exploration into local Vision Large Language Models (LLMs) showcases the incredible potential of running advanced AI directly on consumer hardware. Using an NVIDIA RTX 5090, the tests reveal that open-source models like Gemma 4 and Qwen 3.5 can successfully and accurately extract complex financial data from image-based PDFs. The standout performer, Gemma 4:26b, delivers lightning-fast throughput while maintaining incredibly low VRAM usage, making advanced document processing highly accessible!
Key Takeaways
- •All tested models perfectly utilized Multimodal capabilities to read image PDFs that lack a text layer.
- •Gemma 4:26b achieved the fastest processing speed (176.3 tok/s) and efficiently scaled to process massive 77-page documents.
- •Financial data extraction accuracy was impressively high across all models, with variances mainly due to complex table structures rather than model limitations.
Reference / Citation
View Original"gemma4:26b (MoE) is the best practical choice, offering the fastest speed and lowest VRAM usage, successfully completing a 77-page document while maintaining high accuracy."
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