Vision
How to deploy Nanonets-OCR-s on a GPU cloud
A 3.8B vision-language model that reads images alongside text. Full specs, license and use cases.
Nanonets-OCR-s size and hardware requirements
3.8B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
8.4 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 7.0 GB | 8.4 GB | RTX 4070 Super | 1 | $0.110/hr |
| FP8 (quantized) | 3.5 GB | 4.2 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 1.7 GB | 2.1 GB | RTX 4070 Super | 1 | $0.110/hr |
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 4070 Super 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 pod