What GPU do I need to run baidu/Unlimited-OCR?
3.3B parameters, published in BF16. View on Hugging Face
Set up Unlimited-OCRUnlimited-OCR is published by baidu on Hugging Face, with 3,181,811 downloads and 4,167 likes to date. It's a UnlimitedOCRForCausalLM model built for image-text-to-text, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 6.2 GB | 7.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 3.1 GB | 3.7 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.6 GB | 1.9 GB | RTX 5060 Ti | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Unlimited-OCR at its published (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Unlimited-OCR: common questions
Does Unlimited-OCR fit on a 8 GB GPU?
Yes. At BF16 it needs 7.5 GB of VRAM, so an 8 GB card holds it with 0.5 GB to spare. A 6 GB card is not enough for it at BF16.
How many copies of Unlimited-OCR fit on one RTX 5060 Ti?
2, by VRAM alone. That card carries 16.0 GB and one copy needs 7.5 GB at BF16, on a live rate of $0.110/hr for the whole card. Throughput is not modelled here, so 2 copies is not 2 times the requests served.
What is the least VRAM Unlimited-OCR can run in?
1.9 GB, at INT4 (quantized), which fits a 6 GB card, against 7.5 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
