How to Launch PaddleOCR-VL-1.6-GGUF Direct EXE Setup

How to Launch PaddleOCR-VL-1.6-GGUF Direct EXE Setup

Deploying this model locally is quickest when done via Docker.

Refer to the instructions below to proceed.

Then, simply start the container with the provided Docker command.

📎 HASH: 9548171010a7eab8b0b0ba688438725b | Updated: 2026-06-21



  • CPU: modern architecture (Zen 3 / Alder Lake minimum)
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk Space: at least 100 GB for multiple local LLM variants
  • GPU: high memory bandwidth GPU for next-gen local AI pipeline

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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