What GPU do I need to run nanonets/Nanonets-OCR-s?
A 3.8B vision-language model that reads images alongside text. 3.8B parameters, published in BF16. View on Hugging Face
Nanonets-OCR-s is published by nanonets on Hugging Face, with 349,113 downloads and 1,593 likes to date. It's a Qwen2_5_VLForConditionalGeneration model built for image-text-to-text, published natively in BF16.
What Nanonets-OCR-s is
Nanonets-OCR-s is a 3.8B-parameter vision-language model published by Nanonets on Hugging Face. It is released under Not stated.
License note: no license tag published on the model's Hugging Face card; check the repo directly before any commercial use. Facts in this section are sourced from Nanonets-OCR-s's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Image understanding and captioning
- Visual question answering
- Document/OCR-style reading
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 | 7.0 GB | 8.4 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 3.5 GB | 4.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.7 GB | 2.1 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 Nanonets-OCR-s 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.
Nanonets-OCR-s: common questions
Does Nanonets-OCR-s fit on a 12 GB GPU?
Yes. At BF16 it needs 8.4 GB of VRAM, so a 12 GB card holds it with 3.6 GB to spare. An 8 GB card is not enough for it at BF16.
What is the least VRAM Nanonets-OCR-s can run in?
2.1 GB, at INT4 (quantized), which fits a 6 GB card, against 8.4 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.
How to run Nanonets-OCR-s
Run Nanonets-OCR-s with vLLM
From nanonets/Nanonets-OCR-s's own deployment docs.
vllm serve nanonets/Nanonets-OCR-sSource: https://huggingface.co/nanonets/Nanonets-OCR-s/raw/main/README.md
Run Nanonets-OCR-s with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Nanonets-OCR-s-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Nanonets-OCR-s-GGUFDeploy Nanonets-OCR-s on Aquanode
Aquanode has no one-click deploy template for Nanonets-OCR-s; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX 5060 Ti or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen2.5 models
- Qwen2.5-VL-3B-Instruct (3.8B, BF16)
- Qwen2.5-VL-7B-Instruct (8.3B, BF16)
- NuMarkdown-8B-Thinking (8.3B, BF16)
- Qwen2.5-VL-32B-Instruct (33.5B, BF16)
- Qwen2.5-VL-72B-Instruct (73.4B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
