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sglang/docs/cookbook/autoregressive/DeepSeek/DeepSeek-OCR-2.mdx
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---
title: DeepSeek-OCR-2
metatags:
description: "Deploy DeepSeek-OCR-2 with SGLang - high-accuracy text extraction from images and documents for OCR tasks."
---
import { DeepSeekOCR2Deployment } from '/src/snippets/autoregressive/deepseek-ocr-v2-deployment.jsx';
## 1. Model Introduction
[DeepSeek-OCR-2](https://github.com/deepseek-ai/DeepSeek-OCR-2) is DeepSeek's next-generation OCR (Optical Character Recognition) model, building on DeepSeek-OCR with improved accuracy and broader document understanding capabilities. The model is optimized for high-accuracy text extraction from images across a wide variety of document types and formats.
**Key Features:**
- **Semantic-Aware Visual Encoding (DeepEncoder V2)**: DeepSeek-OCR-2 introduces DeepEncoder V2, which models document reading order in a more human-like, semantic-driven manner rather than relying on fixed raster scanning. This significantly improves logical reading flow in complex layouts (e.g., multi-column documents).
- **Stronger Layout and Structural Understanding**: DeepSeek-OCR-2 demonstrates improved performance on structured documents such as tables, forms, and dense multi-column pages. It reduces reading-order errors and improves overall document parsing robustness compared to the original version.
- **Improved Accuracy While Maintaining Token Efficiency**: The original DeepSeek-OCR emphasized aggressive visual token compression. OCR-2 maintains high token efficiency while delivering higher benchmark performance, particularly on document-level understanding tasks.
- **Better Generalization Across Complex Document Tasks**: DeepSeek-OCR-2 performs more consistently across multilingual documents, structured data extraction, and visually complex content, making it more suitable for real-world document intelligence scenarios beyond plain text OCR.
**Available Models:**
- **Base Model**: [deepseek-ai/DeepSeek-OCR-2](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2) - Recommended for OCR tasks
**License:**
To use DeepSeek-OCR-2, you must agree to DeepSeek's Community License. See [LICENSE](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2/blob/main/LICENSE.txt) for details.
For more details, please refer to the [official DeepSeek-OCR-2 repository](https://github.com/deepseek-ai/DeepSeek-OCR-2).
## 2. SGLang Installation
Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
## 3. Model Deployment
This section provides deployment configurations optimized for different hardware platforms and use cases.
### 3.1 Basic Configuration
The DeepSeek-OCR-2 series offers models in various sizes and architectures, optimized for different hardware platforms including NVIDIA GPUs, AMD GPUs, Intel Arc Pro B-Series GPUs(codename: BMG (Battlemage)), and Intel Xeon CPUs. The recommended launch configurations vary by hardware and model size.
**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, quantization method, and deployment strategy. SGLang supports serving DeepSeek-OCR-2 on NVIDIA H200 and B200, AMD MI300X, MI355X, and MI325X GPUs, and Intel Arc Pro B-Series GPUs, as well as Intel Xeon CPUs.
<DeepSeekOCR2Deployment />
**Note**: DeepSeek-OCR-2 has ~3B parameters and easily fits on a single modern GPU. For low-latency serving, no model parallelism is needed. For high-throughput requirements, consider using data parallelism with the SGLang Model Gateway — see [DP, DPA and SGLang DP Router](../../../docs/advanced_features/sgl_model_gateway) for more details.
### 3.2 Configuration Tips
- **Single GPU Deployment:** DeepSeek-OCR-2 (~3B parameters) fits on a single modern GPU — no tensor parallelism required for low-latency serving.
- **High Throughput:** For high-throughput scenarios, use data parallelism with the SGLang Model Gateway. See [DP, DPA and SGLang DP Router](../../../docs/advanced_features/sgl_model_gateway).
- **NCCL timeout:** If model loading is slow, increase `--dist-timeout 3600`.
- **Xeon CPU service configuration:** Please refer to the `Notes` part in the serving engine launching section in [the SGLang CPU server document](../../../docs/hardware-platforms/cpu_server#launch-of-the-serving-engine) to better understand how to configure the arguments, especially for NUMA binding settings.
## 4. Model Invocation
### 4.1 Basic Usage
**OpenAI-compatible request example**
```python Example
import requests
url = "http://localhost:30000/v1/chat/completions"
data = {
"model": "deepseek-ai/DeepSeek-OCR-2",
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": "<image>\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)
```
**Reference**
- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)
### 4.2 Recommended Prompts
The following prompts are recommended by the [official model card](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2#main-prompts).
**Structured document conversion** — extracts text while preserving layout:
```text Example
<image>
<|grounding|>Convert the document to markdown.
```
**Free-form OCR** — extracts without layouts:
```text Example
<image>
Free OCR.
```
## 5. Benchmark
### 5.1 Speed Benchmark
**Test Environment:**
- Hardware: NVIDIA H200 GPU (1x)
- Model: DeepSeek-OCR-2
- Tensor Parallelism: 1
- sglang version: 0.0.0.dev1+g93fca0bbc
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. For more details on how to perform evaluation, see [Evaluating New Models with SGLang](../../../docs/developer_guide/evaluating_new_models).
#### 5.1.1 Latency-Sensitive Benchmark
- Model Deployment Command:
```shell Command
sglang serve \
--model-path deepseek-ai/DeepSeek-OCR-2 \
--enable-multimodal \
--host 0.0.0.0 \
--port 30000
```
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--host 0.0.0.0 \
--port 30000 \
--model deepseek-ai/DeepSeek-OCR-2 \
--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): 3.54
Total input tokens: 1972
Total input text tokens: 1972
Total generated tokens: 2784
Total generated tokens (retokenized): 2710
Request throughput (req/s): 2.83
Input token throughput (tok/s): 557.53
Output token throughput (tok/s): 787.10
Peak output token throughput (tok/s): 818.00
Peak concurrent requests: 5
Total token throughput (tok/s): 1344.63
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 352.69
Median E2E Latency (ms): 392.34
P90 E2E Latency (ms): 540.64
P99 E2E Latency (ms): 639.01
---------------Time to First Token----------------
Mean TTFT (ms): 18.08
Median TTFT (ms): 16.57
P99 TTFT (ms): 25.67
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 1.18
Median TPOT (ms): 1.21
P99 TPOT (ms): 1.22
---------------Inter-Token Latency----------------
Mean ITL (ms): 1.21
Median ITL (ms): 1.21
P95 ITL (ms): 1.28
P99 ITL (ms): 1.44
Max ITL (ms): 4.32
==================================================
```
#### 5.1.2 Throughput-Sensitive Benchmark
- Model Deployment Command:
```shell Command
sglang serve \
--model-path deepseek-ai/DeepSeek-OCR-2 \
--enable-multimodal \
--tp 1 \
--ep 1 \
--dp 1 \
--enable-dp-attention \
--host 0.0.0.0 \
--port 30000
```
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--host 0.0.0.0 \
--port 30000 \
--model deepseek-ai/DeepSeek-OCR-2 \
--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): 14.79
Total input tokens: 301698
Total input text tokens: 301698
Total generated tokens: 188375
Total generated tokens (retokenized): 185236
Request throughput (req/s): 67.63
Input token throughput (tok/s): 20402.54
Output token throughput (tok/s): 12738.99
Peak output token throughput (tok/s): 17508.00
Peak concurrent requests: 187
Total token throughput (tok/s): 33141.53
Concurrency: 86.87
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 1284.50
Median E2E Latency (ms): 866.07
P90 E2E Latency (ms): 3027.32
P99 E2E Latency (ms): 5490.63
---------------Time to First Token----------------
Mean TTFT (ms): 86.08
Median TTFT (ms): 50.09
P99 TTFT (ms): 613.92
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 7.79
Median TPOT (ms): 6.54
P99 TPOT (ms): 50.10
---------------Inter-Token Latency----------------
Mean ITL (ms): 6.42
Median ITL (ms): 4.64
P95 ITL (ms): 23.65
P99 ITL (ms): 39.62
Max ITL (ms): 452.65
==================================================
```
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