LLMs · Baidu

Unlimited OCR 2 (3B-A0.6B)

Name: Unlimited OCR 2 (3B-A0.6B)
Author: Baidu

Parameters: 3.3B (0.6B active)
Architecture: Mixture of experts
Max context: 128K tokens
License: mit
Released: 2026-10

How to run it

Original weights · Hugging Face

Full-precision files from Baidu, for fine-tuning, vLLM, Transformers.

huggingface.co/baidu/Unlimited-OCR-2 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Baidu. The weights themselves download from Hugging Face.

github.com/baidu/Unlimited-OCR (opens in a new tab)

git clone https://github.com/baidu/Unlimited-OCR

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

Apps that run Unlimited OCR 2 (3B-A0.6B)

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

LM Studio

Runs the MLX build, typically 35–90% faster than GGUF for a model with 0.6B active parameters on Apple silicon.

Also good:

Windows PC, NVIDIA GPU

Docker Model Runner

Handy if you already work in Docker.

Also good:

Windows PC, AMD Radeon

Docker Model Runner

Handy if you already work in Docker.

Also good:

Text Generation WebUI

Linux, NVIDIA GPU

Docker Model Runner

Handy if you already work in Docker.

Also good:

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good:

App	Type	Opens it as	Systems	Engine	Start
LM Studio	Desktop app	MLX	macOSWindowsLinux	llama.cpp, MLX	Open in LM Studio
Jan	Desktop app	MLX	macOSWindowsLinux	llama.cpp, MLX	Get Jan
MLX LM	CLI + API	MLX	macOS	MLX	`mlx_lm.chat --model mlx-community/Unlimited-OCR-2-4bit`
vLLM	Server	Original weights	WSLWindowsLinux	vLLM	`vllm serve baidu/Unlimited-OCR-2`
SGLang	Server	Original weights	WSLWindowsLinux	SGLang	`python3 -m sglang.launch_server --model-path baidu/Unlimited-OCR-2`
Docker Model Runner	CLI + API	Original weights	macOSWindowsLinux	llama.cpp, vLLM	`docker model run hf.co/baidu/Unlimited-OCR-2`
Unsloth Desktop	Desktop app	MLX	macOSWindowsLinux	llama.cpp, MLX, stable-diffusion.cpp	Get Unsloth Desktop
Atomic Chat	Desktop app	MLX	macOSWindowsLinux	llama.cpp, MLX-VLM	Open in Atomic Chat
Text Generation WebUI	Desktop app	Original weights	macOSWindowsLinux	llama.cpp, Transformers, ExLlamaV3	Get Text Generation WebUI
Msty Studio	Desktop app	MLX	macOSWindowsLinux	Ollama, llama.cpp, MLX	Get Msty Studio
Inferencer	Desktop app	MLX	macOS	MLX	Get Inferencer
exo	Server	MLX	macOSLinux	MLX	Get exo

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

KV cache: sparse reference sliding-window attention: each layer attends to a fixed set of selected blocks; the estimate counts a full KV cache in all 12 layers, so it is on the safe side.

Quantization	Quality	Weights	Total at 8K context	Total at 32K context	Download
`Q2_K`	Low	1.3 GB	2.4 GB	3.9 GB	All files (opens in a new tab)
`Q3_K_M`	Low	1.6 GB	2.7 GB	4.2 GB	All files (opens in a new tab)
`Q4_K_M`	Good	2.0 GB	3.1 GB	4.7 GB	All files (opens in a new tab)
`Q5_K_M`	High	2.4 GB	3.5 GB	5.0 GB	All files (opens in a new tab)
`Q6_K`	High	2.7 GB	3.8 GB	5.4 GB	All files (opens in a new tab)
`Q8_0`	Very high	3.5 GB	4.6 GB	6.2 GB	All files (opens in a new tab)
`F16`	Full	6.7 GB	7.8 GB	9.3 GB	Original weights (opens in a new tab)

Best hardware for Unlimited OCR 2 (3B-A0.6B)

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A380 (launch price $139) at Q8_0.

Quality	Hardware	Quant	Memory	Speed	Runs
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 (16 GB)	`F16`	8.6 GB / 10.7 GB	40.8 tok/s	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	Intel Arc B580	`F16`	8.4 GB / 12.0 GB	96.6 tok/s	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 3060 12GB	`F16`	8.4 GB / 12.0 GB	98.4 tok/s	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4070 SUPER	`F16`	8.4 GB / 12.0 GB	118 tok/s	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4060	`Q8_0`	5.0 GB / 8.0 GB	136 tok/s	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 (24 GB)	`F16`	9.1 GB / 16.1 GB	40.8 tok/s	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 Pro (24 GB)	`F16`	9.1 GB / 16.1 GB	75.5 tok/s	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 9070 XT	`F16`	8.6 GB / 16.0 GB	123 tok/s	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4060 Ti 16GB	`F16`	8.6 GB / 16.0 GB	86.1 tok/s	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4070 Ti SUPER	`F16`	8.6 GB / 16.0 GB	134 tok/s	Runs great

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.