Dots.ocr — Hardware Requirements & GPU Compatibility
Vision
dots.ocr is a 3-billion-parameter vision-language model built around a compact 1.7-billion-parameter language backbone, purpose-built for multilingual document layout parsing and OCR. It unifies layout detection and text recognition in one model, switching tasks — table extraction, formula recognition, reading order — by changing the prompt, instead of chaining separate detection models. Despite its small footprint, its developers report state-of-the-art results on document-parsing benchmarks, including strong performance on low-resource languages. It runs comfortably on a single consumer GPU. Context length is 131,072 tokens, ample for long documents. It is released under the MIT license, a highly permissive option for commercial use. Published in July 2025, it shows a compact single VLM can rival dedicated layout-detection models like DocLayout-YOLO.
933.0K downloads
1.3K likesSpecifications
- Publisher
- dots-studio
- Parameters
- 3.0B
- Architecture
- DotsOCRForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-07-30
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Dots.ocr Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.6 GB | 5.3 GB | 1.29 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.7 GB | 5.4 GB | 1.33 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.8 GB | 5.5 GB | 1.48 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 2.2 GB | 5.9 GB | 1.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.5 GB | 6.2 GB | 2.17 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.9 GB | 6.6 GB | 2.51 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.4 GB | 7.1 GB | 3.04 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 Dots.ocr?
Q4_K_M · 2.2 GBDots.ocr (Q4_K_M) requires 2.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 131K context window can add up to 3.7 GB, bringing total usage to 5.9 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Dots.ocr?
Q4_K_M · 2.2 GB59 devices with unified memory can run Dots.ocr, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Dots.ocr
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Dots.ocr need?
Dots.ocr requires 2.2 GB of VRAM at Q4_K_M, or 6.4 GB at BF16. Full 131K context adds up to 3.7 GB (5.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.0B × 4.8 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.1 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M2.2 GBQ4_K_M + full context5.9 GB- What's the best quantization for Dots.ocr?
For Dots.ocr, Q4_K_M (2.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (2.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.6 GB.
VRAM requirement by quantization
Q2_K1.6 GBQ3_K_L1.9 GBQ4_K_M ★2.2 GBQ5_K_S2.5 GBQ5_K_M2.5 GBBF166.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Dots.ocr on a Mac?
Dots.ocr requires at least 1.6 GB at Q2_K, 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 Dots.ocr locally?
Yes — Dots.ocr can run locally on consumer hardware. At Q4_K_M quantization it needs 2.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Dots.ocr?
At Q4_K_M, Dots.ocr can reach ~2202 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~301 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 ÷ 2.2 × 0.65 = ~2385 tok/s
Estimated speed at Q4_K_M (2.2 GB)
~2385 tok/s~301 tok/s~2385 tok/s~2202 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Dots.ocr?
At Q4_K_M, the download is about 1.82 GB. The full-precision BF16 version is 6.08 GB. The smallest option (Q2_K) is 1.29 GB.
- Which GPUs can run Dots.ocr?
52 consumer GPUs can run Dots.ocr at Q4_K_M (2.2 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 Dots.ocr?
59 devices with unified memory can run Dots.ocr at Q4_K_M (2.2 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.