The difference with using VLMs for OCR vs. other tasks is that the ratio of output/input…
This is a AI post classified by Jev as RAG & memory (a tutorial), kept by the AI Radar because it carries real work, not commentary.
The difference with using VLMs for OCR vs. other tasks is that the ratio of output/input tokens is quite high - meaning the model can spend a lot of time writing markdown. Speculative decoding offers a useful approach where a fast draft model proposes several tokens, and then the main model checks them together. Accepted tokens move the output forward; rejected proposals are corrected. The net benefit here is you reduce latency on sequential decoding steps, especially if the document structure makes certain text easier to predict. Check out the video as an overview! We've built a lot of
Posted by Jerry Liu (83.9k followers) 2 days ago · 37 likes · 3.7k views · view the original post on X. Kept by the AI Radar as RAG & memory. Tools mentioned: Sign in.
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