---
title: DeepSeek-OCR
metatags:
description: "Deploy DeepSeek-OCR with SGLang - high-accuracy text extraction from images and documents for OCR tasks."
---
## 1. Model Introduction
[DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) is DeepSeek's advanced OCR (Optical Character Recognition) model designed for high-accuracy text extraction from images. The model is optimized for various document processing and image-to-text conversion tasks.
**Key Features:**
- **Advanced OCR**: High-accuracy text recognition from images and documents
- **Multi-Modality**: Supports various image formats and document types
**Available Models:**
- **Base Model**: [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) - Recommended for OCR tasks
**License:**
To use DeepSeek-OCR, you must agree to DeepSeek's Community License. See [LICENSE](https://huggingface.co/deepseek-ai/DeepSeek-OCR/blob/main/LICENSE) for details.
For more details, please refer to the [official DeepSeek-OCR repository](https://github.com/deepseek-ai/DeepSeek-OCR).
## 2. SGLang Installation
Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
For SGLang CPU installation, please refer to the [CPU version installation guide](../../../docs/hardware-platforms/cpu_server#installation).
## 3. Model Deployment
This section provides deployment configurations optimized for different hardware platforms and use cases.
### 3.1 Basic Configuration
**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, quantization method, and deployment strategy.
import { DeepSeekOCRDeployment } from "/src/snippets/autoregressive/deepseek-ocr-deployment.jsx";
## 4. Model Invocation
### 4.1 Basic Usage
For basic API usage and request examples, please refer to:
- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)
### 4.2 OCR-Specific Prompts
DeepSeek-OCR accepts recommended prompts from the model card:
```text
<|grounding|>Convert the document to markdown.
```
```text
Free OCR.
```
**OpenAI-compatible image request example:**
```python Example
import requests
url = "http://localhost:30000/v1/chat/completions"
data = {
"model": "deepseek-ai/DeepSeek-OCR",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "\n<|grounding|>Convert the document to markdown."
},
{
"type": "image_url",
"image_url": {"url": "https://example.com/your_image.jpg"}
},
],
}
],
"max_tokens": 512,
}
response = requests.post(url, json=data)
print(response.text)
```
## 5. Benchmark
### 5.1 Speed Benchmark
**Test Environment:**
- Hardware: AMD MI300X GPU (1x)
- Model: DeepSeek-OCR
- Tensor Parallelism: 1
- sglang version: 0.5.7
We use SGLang's built-in benchmarking tool to conduct performance evaluation on the [ShareGPT_Vicuna_unfiltered](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) dataset. This dataset contains real conversation data and can better reflect performance in actual use scenarios. To simulate real-world usage patterns, we configure each request with 1024 input tokens and 1024 output tokens, representing typical medium-length conversations with detailed responses.
#### 5.1.1 Latency-Sensitive Benchmark
- Model Deployment Command:
```shell Command
python3 -m sglang.launch_server \
--model-path deepseek-ai/DeepSeek-OCR \
--tp 1 \
--dtype float16 \
--trust-remote-code \
--host 0.0.0.0 \
--port 8000
```
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--host 127.0.0.1 \
--port 8000 \
--model deepseek-ai/DeepSeek-OCR \
--random-input-len 1024 \
--random-output-len 1024 \
--num-prompts 10 \
--max-concurrency 1
```
- **Test Results:**
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 1
Successful requests: 10
Benchmark duration (s): 4.45
Total input tokens: 1972
Total input text tokens: 1972
Total input vision tokens: 0
Total generated tokens: 2784
Total generated tokens (retokenized): 2770
Request throughput (req/s): 2.25
Input token throughput (tok/s): 442.89
Output token throughput (tok/s): 625.26
Peak output token throughput (tok/s): 635.00
Peak concurrent requests: 4
Total token throughput (tok/s): 1068.16
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 443.32
Median E2E Latency (ms): 493.29
---------------Time to First Token----------------
Mean TTFT (ms): 21.59
Median TTFT (ms): 20.89
P99 TTFT (ms): 24.81
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 1.47
Median TPOT (ms): 1.52
P99 TPOT (ms): 1.53
---------------Inter-Token Latency----------------
Mean ITL (ms): 1.52
Median ITL (ms): 1.51
P95 ITL (ms): 1.76
P99 ITL (ms): 1.93
Max ITL (ms): 8.28
==================================================
```
#### 5.1.2 Throughput-Sensitive Benchmark
- Model Deployment Command:
```shell Command
python3 -m sglang.launch_server \
--model-path deepseek-ai/DeepSeek-OCR \
--tp 1 \
--ep 1 \
--dp 1 \
--enable-dp-attention \
--dtype float16 \
--host 0.0.0.0 \
--port 8000
```
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--host 127.0.0.1 \
--port 8000 \
--model deepseek-ai/DeepSeek-OCR \
--random-input-len 1024 \
--random-output-len 1024 \
--num-prompts 1000 \
--max-concurrency 100
```
- **Test Results:**
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 100
Successful requests: 1000
Benchmark duration (s): 16.24
Total input tokens: 301698
Total input text tokens: 301698
Total input vision tokens: 0
Total generated tokens: 188375
Total generated tokens (retokenized): 186927
Request throughput (req/s): 61.59
Input token throughput (tok/s): 18582.90
Output token throughput (tok/s): 11602.84
Peak output token throughput (tok/s): 15479.00
Peak concurrent requests: 179
Total token throughput (tok/s): 30185.75
Concurrency: 85.53
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 1388.60
Median E2E Latency (ms): 901.43
---------------Time to First Token----------------
Mean TTFT (ms): 73.36
Median TTFT (ms): 50.21
P99 TTFT (ms): 349.53
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 7.42
Median TPOT (ms): 7.31
P99 TPOT (ms): 27.99
---------------Inter-Token Latency----------------
Mean ITL (ms): 7.04
Median ITL (ms): 4.62
P95 ITL (ms): 21.11
P99 ITL (ms): 36.92
Max ITL (ms): 172.15
==================================================
```