Nanonets OCR2 3B — Hardware Requirements & GPU Compatibility
Vision
Nanonets-OCR2-3B is Nanonets' image-to-markdown OCR model, a 3.8-billion-parameter vision-language model fine-tuned from Qwen2.5-VL-3B-Instruct to turn documents into structured markdown with semantic tagging rather than plain extracted text. It converts equations into LaTeX, isolates signatures and watermarks into dedicated tags, converts form checkboxes into standard Unicode symbols, extracts complex tables as markdown or HTML, renders flow and organizational charts as Mermaid diagrams, and handles handwritten and multilingual documents across a dozen or more languages; it can also answer direct questions about a document's contents. It is an OCR and document-understanding tool rather than a general chat assistant. Its small size lets it run on a single consumer GPU. Context length is 128,000 tokens, inherited from its Qwen2.5-VL base. Nanonets has not published a specific open-source license for the model on Hugging Face. It was published in October 2025, alongside a smaller Nanonets-OCR2-1.5B-exp variant and a hosted Nanonets-OCR2-Plus service.
90.9K downloads
516 likesSpecifications
- Publisher
- nanonets
- Parameters
- 3.8B
- Architecture
- Qwen2_5_VLForConditionalGeneration
- Context Length
- 128,000 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-10-13
Get Started
HuggingFace
How Much VRAM Does Nanonets OCR2 3B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.0 GB | 6.6 GB | 1.60 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.0 GB | 6.7 GB | 1.64 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.2 GB | 6.8 GB | 1.83 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.3 GB | 6.9 GB | 1.88 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.6 GB | 7.3 GB | 2.25 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.0 GB | 7.7 GB | 2.68 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.5 GB | 8.1 GB | 3.10 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.1 GB | 8.8 GB | 3.75 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Nanonets OCR2 3B?
Q4_K_M · 2.6 GBNanonets OCR2 3B (Q4_K_M) requires 2.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 128K context window can add up to 4.6 GB, bringing total usage to 7.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Nanonets OCR2 3B?
Q4_K_M · 2.6 GB59 devices with unified memory can run Nanonets OCR2 3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Nanonets OCR2 3B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Nanonets OCR2 3B need?
Nanonets OCR2 3B requires 2.6 GB of VRAM at Q4_K_M, or 7.9 GB at BF16. Full 128K context adds up to 4.6 GB (7.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.8B × 4.8 bits ÷ 8 = 2.3 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 5 GB (at full 128K context)
VRAM usage by quantization
Q4_K_M2.6 GBQ4_K_M + full context7.3 GB- What's the best quantization for Nanonets OCR2 3B?
For Nanonets OCR2 3B, Q4_K_M (2.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.4 GB.
VRAM requirement by quantization
IQ2_XXS1.4 GBIQ3_XS1.9 GBQ4_02.3 GBIQ4_NL2.5 GBQ4_K_M ★2.6 GBBF167.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Nanonets OCR2 3B on a Mac?
Nanonets OCR2 3B requires at least 1.4 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Nanonets OCR2 3B locally?
Yes — Nanonets OCR2 3B can run locally on consumer hardware. At Q4_K_M quantization it needs 2.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Nanonets OCR2 3B?
At Q4_K_M, Nanonets OCR2 3B can reach ~1825 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~249 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.6 × 0.65 = ~1977 tok/s
Estimated speed at Q4_K_M (2.6 GB)
~1977 tok/s~249 tok/s~1977 tok/s~1825 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Nanonets OCR2 3B?
At Q4_K_M, the download is about 2.25 GB. The full-precision BF16 version is 7.51 GB. The smallest option (IQ2_XXS) is 1.03 GB.
- Which GPUs can run Nanonets OCR2 3B?
52 consumer GPUs can run Nanonets OCR2 3B at Q4_K_M (2.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Nanonets OCR2 3B?
59 devices with unified memory can run Nanonets OCR2 3B at Q4_K_M (2.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.