dots-studioDotsOCRForCausalLM

Dots.mocr — Hardware Requirements & GPU Compatibility

Vision

Dots.mocr is a 3-billion-parameter vision-language model from rednote-hilab, published on Hugging Face under the dots-studio organization, built specifically for OCR and document understanding rather than general chat. It unifies layout detection, text recognition, table and formula extraction, and reading-order recovery in one model, and can also convert charts, UI screenshots, and scientific figures directly into SVG code (a companion dots.mocr-svg checkpoint is tuned specifically for that task). On document-parsing benchmarks such as OmniDocBench and olmOCR-Bench it scores ahead of same-size competitors like MonkeyOCR-pro-3B and PaddleOCR-VL, and vLLM has integrated it natively since version 0.11.0. At just 3 billion parameters, it runs comfortably on a single consumer GPU. Context length is 131,072 tokens. It is released under the MIT license, with no usage restrictions. It was published in March 2026.

3.0B params · dense131K contextMITMar 2026168.7K downloads197 likes4.9K quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

Runs great on a 24 GB GPU

Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~301 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~33 tok/s)

MIT

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Dots.mocr

One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.

Ways to runOther ways

each opens step-by-step

Run in cloud

Runs on RTX 3080 Ti (12 GB) (9 GB headroom) · Q4_K_M

Generation speed: ~272 tok/s; generation speed
Cost per 1M output tokens: $0.12; per 1M output tokens

Deployon Vast.aiBest valuefrom $0.12/hr

Compare GPUs →

Run Dots.mocr

2.6 GB VRAM at 16K context · fits the RTX 4090 (21.4 GB free)

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/prithivMLmods/dots.mocr-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/prithivMLmods/dots.mocr-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/prithivMLmods/dots.mocr-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/prithivMLmods/dots.mocr-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1.
$vllm serve dots-studio/dots.mocr --max-model-len 16384
Runs the original weights, not the Q4_K_M GGUF; needs ~7 GB of GPU memory.

macOS only.

MLX supports only macOS, so follow these steps for that setup.

Install mlx-lm
$pip install mlx-lm
Chat in the terminal
$mlx_lm.chat --model mlx-community/dots.mocr-8bit
Downloads the model on first run. The MLX repo has its own quantisation; it may not match the quant selected above.
Or start a local server
Listens on http://localhost:8080.
$mlx_lm.server --model mlx-community/dots.mocr-8bit

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$prithivMLmods/dots.mocr-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
RTX 3080 Ti (12 GB)CheapestCheapest9 GB free	~272 tok/s	$0.12	Deploy on Vast.aifrom $0.12/hr (opens in new tab)	12 GB · 9 GB free · ~272 tok/s · $0.12 / 1M tokens
RTX 3060 12GB9 GB free	~107 tok/s	$0.16	Deploy on Vast.aifrom $0.061/hr (opens in new tab)	9 GB free · ~107 tok/s · $0.16 / 1M tokens
RTX 4060 (8 GB)5 GB free	~81 tok/s	$0.29	Deploy on Vast.aifrom $0.086/hr (opens in new tab)	8 GB · 5 GB free · ~81 tok/s · $0.29 / 1M tokens
RTX A4000 (16 GB)13 GB free	~134 tok/s	$0.18	Deploy on Vast.aifrom $0.088/hr (opens in new tab)	16 GB · 13 GB free · ~134 tok/s · $0.18 / 1M tokens
RTX 4070 SUPER (12 GB)9 GB free	~150 tok/s	$0.19	Deploy on Vast.aifrom $0.10/hr (opens in new tab)	12 GB · 9 GB free · ~150 tok/s · $0.19 / 1M tokens
RTX PRO 4500 (32 GB)29 GB free	~267 tok/s	$0.35	Deploy on RunPodfrom $0.34/hr (opens in new tab)	32 GB · 29 GB free · ~267 tok/s · $0.35 / 1M tokens
RTX 4090 (24 GB)21 GB free	~301 tok/s	$0.31	Deploy on RunPodfrom $0.34/hr (opens in new tab)	24 GB · 21 GB free · ~301 tok/s · $0.31 / 1M tokens
A40 (48 GB)45 GB free	~208 tok/s	$0.47	Deploy on RunPodfrom $0.35/hr (opens in new tab)	48 GB · 45 GB free · ~208 tok/s · $0.47 / 1M tokens
L4 (24 GB)21 GB free	~89 tok/s	$1.37	Deploy on RunPodfrom $0.44/hr (opens in new tab)	24 GB · 21 GB free · ~89 tok/s · $1.37 / 1M tokens

Compare GPUs →

How Much VRAM Does Dots.mocr Need?

Select a quantization level; the listed hardware adheres to it.

VRAM	+32K	+128K	File	Quality	On RTX 4090 (reference)
1.5 GB	2.4 GB	5.2 GB	0.8 GB	3-bit · i-quant — Importance-weighted 3-bit	Fits: 128K context · ~426 tok/s
1.6 GB	2.5 GB	5.4 GB	~1.3 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Fits: 128K context · ~397 tok/s
1.7 GB	2.6 GB	5.4 GB	0.9 GB	3-bit small — 3-bit small quantization	Fits: 128K context · ~388 tok/s
1.8 GB	2.7 GB	5.5 GB	0.9 GB	3-bit medium — 3-bit medium quantization	Fits: 128K context · ~356 tok/s
1.9 GB	2.8 GB	5.6 GB	1 GB	3-bit large — 3-bit large quantization	Fits: 128K context · ~341 tok/s
2.1 GB	3 GB	5.8 GB	1.1 GB	4-bit small — 4-bit small quantization	Fits: 128K context · ~317 tok/s
2.2 GB	3.1 GB	5.9 GB	1.1 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Fits: 128K context · ~301 tok/s
2.5 GB	3.3 GB	6.2 GB	1.3 GB	5-bit small — 5-bit small quantization	Fits: 128K context · ~267 tok/s
2.5 GB	3.4 GB	6.2 GB	~2.2 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Fits: 128K context · ~260 tok/s
2.9 GB	3.8 GB	6.6 GB	~2.5 GB	6-bit, very good — 6-bit quantization, very good quality	Fits: 128K context · ~228 tok/s
3.4 GB	4.3 GB	7.1 GB	1.9 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Fits: 128K context · ~193 tok/s
6.4 GB	7.3 GB	10.1 GB	~6.1 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Fits: 128K context · ~102 tok/s

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

2.6GBat 16K context

Weights: 1.8 GB
KV cache: 0.5 GB
Overhead: 0.3 GB

Fits RTX 4090 (reference) · 21.4 GB free · ~301 tok/s

≥50 tok/s needs ≥168 GB/s · RTX 5090 ~534, RTX 3090 Ti ~301

Which Hardware Can Run Dots.mocr?

Q4_K_M · 2.2 GB

Dots.mocr (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. 163 devices with unified memory can run Dots.mocr, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Typical hardwareQuantVRAMtok/sContext that fits

8 GB · RTX 4060Q4_K_M · 2.6 GB · ~81 tok/s · 128K2.6~81128K8 GB tier →

12 GB · RTX 3060 12GBQ4_K_M · 2.6 GB · ~107 tok/s · 128K2.6~107128K12 GB tier →

16 GB · RTX 5060 Ti 16GBQ4_K_M · 2.6 GB · ~134 tok/s · 128K2.6~134128K16 GB tier →

24 GB · RTX 4090referenceQ4_K_M · 2.6 GB · ~301 tok/s · 128K2.6~301128K24 GB tier →

Mac 32 GB · Mac Mini M4Q4_K_M · 2.6 GB · ~39 tok/s · 128K2.6~39128KMacs →

Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 2.6 GB · ~175 tok/s · 128K2.6~175128KMacs →

Alternatives to Dots.mocr

Similar models

6tok/s on RTX 4090

Model	Score	VRAM	tok/s	Why it is similar
SmolLM3 3BHugging Face	—	3.4 GB	~285	same size
Qwen2.5 Coder 3BAlibaba	—	2.8 GB	~294	same size
Llama 3.2 3B InstructMeta	—	4.1 GB	~309	same size
Starcoder2 3BBigCode	—	2.6 GB	~301	same size
AI21 Jamba Reasoning 3BAI21 Labs	—	2.3 GB	~291	same size
MiniMax M3 EAGLE3Inferact	—	2.7 GB	~284	same size

Specifications

Publisher: dots-studio
Parameters: 3.0B
Architecture: DotsOCRForCausalLM
Context Length: 131,072 tokens
Vocabulary Size: 151,936
Release Date: 2026-03-19
License: MIT

Resources

Hugging Face model card (opens in a new tab)

Frequently Asked Questions

How much VRAM does Dots.mocr need?

Dots.mocr 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)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 4.1 GB (at full 131K context)

VRAM usage by quantization

Q4_K_M

2.2 GB

Q4_K_M + full context

5.9 GB

Learn more about VRAM estimation →

What's the best quantization for Dots.mocr?

For Dots.mocr, 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 IQ3_XXS at 1.5 GB.

VRAM requirement by quantization

IQ3_XXS

1.5 GB~70%

Q3_K_M

1.8 GB~83%

Q4_K_M ★

2.2 GB~89%

Q5_K_S

2.5 GB~92%

Q5_K_M

2.5 GB~92%

BF16

6.4 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Dots.mocr on a Mac?

Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Dots.mocr. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.2 GB of usable unified memory (RAM minus macOS overhead).

Can I run Dots.mocr locally?

Yes — Dots.mocr can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Dots.mocr?

At Q4_K_M, Dots.mocr 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 ÷ 2.18 × 0.65 = ~2385 tok/s

Estimated speed at Q4_K_M (2.2 GB)

NVIDIA B200

~2385 tok/s

NVIDIA GeForce RTX 4090

~301 tok/s

NVIDIA B300

~2385 tok/s

AMD Instinct MI350X

~2202 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Dots.mocr?

At Q4_K_M, the download is about 1.82 GB. The full-precision BF16 version is 6.08 GB. The smallest option (IQ3_XXS) is 1.18 GB.

Which GPUs can run Dots.mocr?

52 consumer GPUs can run Dots.mocr at Q4_K_M. 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.mocr?

163 devices with unified memory can run Dots.mocr at Q4_K_M (2.2 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.