LOADING DATASHEET
LOADING DATASHEET
by PaddlePaddle
RANKED #308 OF 317 ASSISTANTS · OVERALL #479 OF 493 RANKED · VIBE SCORE 4.7 · 72 VOICES ·
COPY BADGE pastes a README snippet with the rank SVG.
PaddleOCR-VL on Hugging Face (image text to text). 8,440 downloads. Open weights for local or hosted use.
6 mentions
6 mentions
26 weeks · 74 voices in this window
HOLDING UP
Recent vibe is steady. No clear fade in the crowd.
Open weights. You can use a hosted service, or download it and run it yourself, free.
Open weights: download PaddleOCR VL once and it's yours. No subscription, no rate limits, works offline.
0.9B parameters, full FP16 model weights require around 2 GB of storage
NVIDIA GeForce GTX 1650 4GB
4 GB VRAM
~$80 USEDFP16 or INT8, ~25 tok/s, 8k context
NVIDIA GeForce RTX 3060 12GB
12 GB VRAM
~$220 USEDUnquantized FP16, ~75 tok/s, full high-resolution image context
NVIDIA GeForce RTX 4090 24GB
24 GB VRAM
~$1,750 NEWUnquantized FP16 batch processing, ~150 tok/s, high throughput
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long PaddleOCR VL takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMNVIDIA GeForce GTX 1650 4GB | SWEET SPOTNVIDIA GeForce RTX 3060 12GB | FULL POWERNVIDIA GeForce RTX 4090 24GB |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~20 S | ~7 S | ~3 S |
| Summarize a documenta long report boiled down to the points that matter | ~60 S★★★★★ | ~20 S★★★★★ | ~10 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~5.3 MIN★★★★★ | ~1.8 MIN★★★★★ | ~53 S★★★★★ |
| Build a backendan API with routes, storage and tests | ~17 MIN★★★★★ | ~5.6 MIN★★★★★ | ~2.8 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~10 MIN★★★★★ | ~3.3 MIN★★★★★ | ~1.7 MIN★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
Not enough discussion about PaddleOCR VL yet to call it. We found 72 posts but people did not say much either way.
66 POSTS · 6 COMMENTS · REDDIT · GITHUB · HACKER NEWS · DEV.TO
Other models the crowd has fully reviewed, starting with multimodal models like this one.
I compared even more parsers on 14 PDF-parsing capabilities using different types
Turbo-OCR for high-volume image and PDF processing
Xberg v1 is out
OvisOCR2: a promising 0.8B local document parser
Xberg v1 is out
CJK Manga/Manhwa/Manhua 150M OCR model (hayai-ocr-v2) outperforming PaddleOCR-VL-For-Manga
I graded 4 open-source PDF parsers on 12 capabilities for RAG ingestion. The failures are exactly the chunks your retriever needs
What are the best alternatives to AWS Textract for OCR?
Open-source OCR actually tested by document type (invoices vs contracts vs scans)
Architecting OW as the primary AI surface for a 2-person team — multi-model + custom KB + write-back. Looking for similar production setups.
Run GLM-OCR, DeepSeek-OCR-2, and dots.mocr with an OpenAI Compatible API
I tried to pick an OCR model by benchmark score and learned the numbers do not compare