PaddleOCR-VL-1.6-GGUF Offline on PC with 1M Context Windows

Deploying this model locally is quickest when done via a simple curl command.

Check out the detailed setup guide below to begin.

The loader auto-caches the model archive (several GBs included).

The script runs a quick hardware check to dynamically adjust parameters for elite speed.

🔍 Hash-sum: d56f5fd65971a5689be8226559219610 | 🕓 Last update: 2026-06-30

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Processor: 4.0 GHz+ boost clock recommended for CPU inference
RAM: high-speed DDR5 memory preferred for CPU offloading
Storage:100 GB free space for HuggingFace cache folder
GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The PaddleOCR-VL-1.6-GGUF is a state‑of‑the‑art vision‑language model designed for high‑accuracy optical character recognition in multilingual documents. It leverages a transformer‑based encoder‑decoder architecture that jointly processes text and layout information, enabling robust recognition of curved and distorted scripts. The model supports over 100 languages and can handle a wide range of document types, from printed books to handwritten notes. Its quantized GGUF format ensures efficient inference on consumer‑grade hardware while maintaining competitive performance metrics. A built‑in language detection module automatically identifies the script, reducing preprocessing overhead. Users can integrate the model into existing pipelines via simple API calls, benefiting from its low memory footprint and fast loading times.

Model Name	PaddleOCR-VL-1.6-GGUF
Architecture	Transformer‑based encoder‑decoder
Supported Languages	100+
Input Resolution	1024×1024 pixels
Parameter Count	1.6 B
Quantization	GGUF (Q4_K_M)
Hardware Requirements	CPU/GPU with ≥4 GB VRAM
License	Apache 2.0

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