This Tiny 1.3B AI Reads Documents INSANELY Well? I Tested It on 30 Pages (RTX 4060)

MiniCPM-OCR 1.0 is a 1.3B-parameter OCR model. I ran it locally on an RTX 4060 laptop and tested it on 30 pages against PaddleOCR, Tesseract and a general vision model (Gemma 4). Result in short: best on tables and equations in my test, ~3 GB VRAM, runs offline. Honest limits are in the video: synthetic pages, one real page, Paddle on CPU. Chapters 0:00 Thirty pages, 72 seconds, 3 GB of VRAM 0:38 What MiniCPM-OCR is 1:24 Can my RTX 4060 laptop run it? 2:07 How it reads a page 2:43 The test: 30 pages, 4 engines 3:25 Handwriting and bad scans 4:10 Tables 4:53 Equations 5:22 Diagrams and one real mistake 5:56 Does shrinking the model hurt? (Q8) 6:38 Speed and memory 7:06 A real 1903 letter 8:09 Run it yourself 8:48 What this test can't tell you 9:08 Who should use it + verdict Links Model: https://huggingface.co/openbmb/MiniCPM-OCR-1.0 Repo: https://github.com/OpenBMB/MiniCPM-OCR GGUF files I used: https://huggingface.co/openbmb/MiniCPM-OCR-1.0-gguf Real page test: Wikimedia Commons / DPLA, public domain (Pierre Chouteau letter, 1903) RunPod: https://get.runpod.io/pe48 Notes: 93.68 (Real5-OmniDocBench) and 1.2 pages/s (RTX 5090) are the developers' own numbers, not mine. All other numbers are from my own run on 2026-10-29 (llama.cpp b11976, GGUF F16 and Q8_0). Disclosure: thumbnail is AI-generated; narration is my cloned voice. Get GPUs Runpod: https://get.runpod.io/pe48 Get Hostinger: https://www.hostg.xyz/SHJiu Coupon Code "PROMPT" 10% discount 🔗 Connect with me: Email → [email protected] Ko-fi → https://ko-fi.com/promptengineer Patreon → https://www.patreon.com/PromptEngineer975 Book a Call → https://calendly.com/prompt-engineer48/call GitHub → https://github.com/PromptEngineer48 Twitter/X → https://twitter.com/prompt48 Website → https://promptengineer48.com/