Published January 30, 2026

PaddleOCR VL 1.5 Now Runs on AMD GPUs

The Chinese text recognition model has been adapted for AMD GPUs – we break down what this means for those working with documents.

Infrastructure
Event Source: AMD Reading Time: 4 – 5 minutes

If you have ever tried to extract text from a scanned document or photograph, you have surely encountered OCR – optical character recognition technology. It turns an image with letters into editable text. It sounds simple, but in practice, it is quite a complex task for a computer.

One of the popular open-source systems for this is called PaddleOCR. It was developed by the Chinese company Baidu, and it can process texts in various languages, including Russian. Recently, version VL 1.5 appeared – an improved model that handles complex documents better.

The news is that this model has now been optimized for use with AMD video cards. Simply put, if you have a computer with a graphics processing unit (GPU) from AMD, you can use PaddleOCR VL 1.5 with good performance.

Why AMD GPU Support for PaddleOCR Matters

Why This Matters

For a long time, NVIDIA video cards reigned supreme in the world of machine learning and neural networks. Most libraries and models were written specifically for them. AMD produced good graphics processors, but their ecosystem for artificial intelligence tasks was significantly weaker.

In the last couple of years, the situation has begun to change. AMD is actively developing its ROCm platform – an analog to NVIDIA CUDA that allows computations to run on their video cards. And more and more tools are receiving AMD support.

PaddleOCR VL 1.5 on AMD is another step in this direction. For developers and companies, this means more choice in hardware. It is not necessary to buy expensive NVIDIA cards if the task can effectively be solved on AMD.

PaddleOCR VL 1.5 Features and Capabilities

What PaddleOCR VL 1.5 Can Do

This model does not just recognize letters. It understands the document structure: where the header is, where a table is, or where ordinary text is. This is especially useful when you need to process an invoice, contract, or scientific article – here, not only is recognition accuracy important, but also understanding the logic of the information layout.

The VL in the name stands for Vision-Language – meaning the model works simultaneously with the visual part of the document and the text content. It does not just see symbols but tries to understand how they are connected by meaning.

Such an approach makes recognition more accurate, especially when it comes to documents with complex layouts or poor scan quality.

Setting Up PaddleOCR VL 1.5 on AMD GPUs

How It Runs on AMD

AMD published a technical article explaining how to set up the environment for working with PaddleOCR VL 1.5 on their video cards. At the core lies a Docker container with pre-installed dependencies and the ROCm library.

In short: you download a ready-made image, launch the container with the necessary parameters, and everything inside is already configured for model operation. This is a standard approach in development – it allows you to avoid wasting time on manually installing dozens of libraries and configuring compatibility.

The article also mentions PaddleX – this is an add-on over PaddleOCR that simplifies recognition pipeline management. Simply put, with its help, you can assemble a document processing chain: first detect text blocks, then recognize them, then extract the necessary data.

Who Is This For

First and foremost, for those involved in document processing automation. These could be companies working with large volumes of paperwork: banks, insurance companies, and logistics firms. Or developers of electronic document management systems.

If you already have infrastructure on AMD, or you are just planning to deploy it, PaddleOCR support is a plus. There is no need to look for alternatives or switch to different hardware.

It is also of interest to those experimenting with open-source models and wanting to try something other than standard solutions based on Tesseract or commercial APIs.

Performance and Support Considerations

What Remains Behind the Scenes

AMD has not published performance benchmarks in the public domain, at least not in this article. It is unclear how fast PaddleOCR VL 1.5 works on their GPUs compared to NVIDIA. Perhaps the difference is insignificant, or perhaps it is noticeable. This is a question each user decides for their specific task through testing.

It is also unclear how actively this integration will be supported in the future. Baidu develops PaddleOCR mainly for its own needs, and if AMD stops investing in adaptation, updates might come out with a delay.

But for now, the fact remains: PaddleOCR VL 1.5 works on AMD GPUs, and this is another tool in the arsenal of those dealing with text recognition.

#applied analysis #systemic analysis #computer vision #engineering #computer systems #infrastructure #open technologies #gpu optimization #inference optimization
Original Title: Unlocking high-performance document parsing of PaddleOCR VL 1 5 on AMD GPUs
Publication Date: Jan 29, 2026
AMD www.amd.com An international company manufacturing processors and computing accelerators for AI workloads.
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From Source to Analysis

How This Text Was Created

This material is not a direct retelling of the original publication. First, the news item itself was selected as an event important for understanding AI development. Then a processing framework was set: what needs clarification, what context to add, and where to place emphasis. This allowed us to turn a single announcement or update into a coherent and meaningful analysis.

Neural Networks Involved in the Process

We openly show which models were used at different stages of processing. Each performed its own role — analyzing the source, rewriting, fact-checking, and visual interpretation. This approach maintains transparency and clearly demonstrates how technologies participated in creating the material.

1.
Claude Sonnet 4.5 Anthropic Analyzing the Original Publication and Writing the Text The neural network studies the original material and generates a coherent text

1. Analyzing the Original Publication and Writing the Text

The neural network studies the original material and generates a coherent text

Claude Sonnet 4.5 Anthropic
2.
Gemini 3 Pro Preview Google DeepMind step.translate-en.title

2. step.translate-en.title

Gemini 3 Pro Preview Google DeepMind
3.
Gemini 2.5 Flash Google DeepMind Text Review and Editing Correction of errors, inaccuracies, and ambiguous phrasing

3. Text Review and Editing

Correction of errors, inaccuracies, and ambiguous phrasing

Gemini 2.5 Flash Google DeepMind
4.
DeepSeek-V3.2 DeepSeek Preparing the Illustration Description Generating a textual prompt for the visual model

4. Preparing the Illustration Description

Generating a textual prompt for the visual model

DeepSeek-V3.2 DeepSeek
5.
FLUX.2 Pro Black Forest Labs Creating the Illustration Generating an image based on the prepared prompt

5. Creating the Illustration

Generating an image based on the prepared prompt

FLUX.2 Pro Black Forest Labs

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