# PaddlePaddle/PaddleOCR-VL — vLLM serving recipe

> PaddleOCR-VL (0.9B) — compact vision-language model for document parsing, OCR, tables, formulas, charts

Configuration: hardware `h200` (8× H200) · strategy `single_node_tp` (Tensor Parallel) · variant `default` (BF16)

- Interactive page: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/PaddlePaddle/PaddleOCR-VL
- This configuration as JSON: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/PaddlePaddle/PaddleOCR-VL.json?hardware=h200

The commands below were rendered for exactly this configuration and take precedence over any example in the guide.

## Other configurations

Change the query string (the same parameters as the page URL) and fetch this URL again. Unknown or incompatible values fall back to the recipe default, so check the Configuration line.

- `hardware`: `h100`, `h200`, `b200`, `gb200`, `b300`, `gb300`, `mi300x`, `mi325x`, `mi350x`, `mi355x`
- `variant`: `default`
- `strategy`: `single_node_tp`, `multi_node_tp` (multi-node strategies also need `nodes=<n>`)
- `features`: comma-separated list of `text_only`, `encoder_parallel` (default: none)

## Install

Requires vLLM >= 0.11.1.

```bash
uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend auto
```

Docker image: `vllm/vllm-openai:latest`

Extra dependencies:

- PaddlePaddle runtime (install in a separate venv from vllm to avoid conflicts)

```bash
uv pip install paddlepaddle-gpu==3.2.1 --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/
```

- PaddleOCR document-parsing helpers

```bash
uv pip install -U "paddleocr[doc-parser]"
```

- Safetensors loader used by the doc-parser path

```bash
uv pip install safetensors
```

## Serve

```bash
vllm serve PaddlePaddle/PaddleOCR-VL \
  --trust-remote-code \
  --max-num-batched-tokens 16384 \
  --no-enable-prefix-caching \
  --mm-processor-cache-gb 0 \
  --tensor-parallel-size 1
```

Docker equivalent:

```bash
docker run --gpus all \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest PaddlePaddle/PaddleOCR-VL \
  --trust-remote-code \
  --max-num-batched-tokens 16384 \
  --no-enable-prefix-caching \
  --mm-processor-cache-gb 0 \
  --tensor-parallel-size 1
```

## Guide

> Background reference. Commands in it are examples and may target another configuration — use the Serve section above.

### Overview

[PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL) is a SOTA resource-efficient
model for document parsing. Its core (PaddleOCR-VL-0.9B) combines a NaViT-style dynamic
resolution visual encoder with an ERNIE-4.5-0.3B language model, optimized for OCR,
tables, formulas, and chart recognition.

### Prerequisites

- Hardware: 1x GPU (small VRAM footprint)
- vLLM >= 0.11.1 (nightly if not released yet)
- AMD ROCm wheels require Python 3.12, ROCm 7.2.3, and glibc >= 2.35.

#### Install vLLM (NVIDIA)

```bash
uv venv
source .venv/bin/activate
uv pip install -U vllm --pre \
  --extra-index-url https://wheels.vllm.ai/nightly \
  --extra-index-url https://download.pytorch.org/whl/cu129 \
  --index-strategy unsafe-best-match
```

#### Install vLLM (AMD ROCm)

```bash
uv venv --python 3.12
source .venv/bin/activate
uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm
```

### Launch command

```bash
vllm serve PaddlePaddle/PaddleOCR-VL \
  --trust-remote-code \
  --max-num-batched-tokens 16384 \
  --no-enable-prefix-caching \
  --mm-processor-cache-gb 0
```

AMD MI300X/MI325X/MI355X:

```bash
export SAFETENSORS_FAST_GPU=1

vllm serve PaddlePaddle/PaddleOCR-VL \
  --trust-remote-code \
  --max-num-batched-tokens 16384 \
  --no-enable-prefix-caching \
  --mm-processor-cache-gb 0
```

Tip: OCR workloads don't benefit much from prefix caching or image reuse, so disable
those to avoid hashing/caching overhead.

### Client Usage

Task-specific prompts:

```python
from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1", timeout=3600)

TASKS = {
    "ocr": "OCR:",
    "table": "Table Recognition:",
    "formula": "Formula Recognition:",
    "chart": "Chart Recognition:",
}

response = client.chat.completions.create(
    model="PaddlePaddle/PaddleOCR-VL",
    messages=[{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "https://.../receipt.png"}},
            {"type": "text", "text": TASKS["ocr"]},
        ],
    }],
    temperature=0.0,
)
print(response.choices[0].message.content)
```

### Offline Inference with PP-DocLayoutV2

Use separate venvs for `vllm` and `paddlepaddle` to avoid conflicts. If you see
"The model PaddleOCR-VL-0.9B does not exist.", add `--served-model-name PaddleOCR-VL-0.9B`.

```bash
uv pip install paddlepaddle-gpu==3.2.1 --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/
uv pip install -U "paddleocr[doc-parser]"
uv pip install safetensors
```

```python
from paddleocr import PaddleOCRVL

pipeline = PaddleOCRVL(
    vl_rec_backend="vllm-server",
    vl_rec_server_url="http://localhost:8000/v1",
    layout_detection_model_name="PP-DocLayoutV2",
    layout_detection_model_dir="/path/to/your/PP-DocLayoutV2/",
)

output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
for i, res in enumerate(output):
    res.save_to_json(save_path=f"output_{i}.json")
    res.save_to_markdown(save_path=f"output_{i}.md")
```

### References

- [PaddleOCR-VL on Hugging Face](https://huggingface.co/PaddlePaddle/PaddleOCR-VL)
- [PaddleOCR GitHub](https://github.com/PaddlePaddle/PaddleOCR)