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1. Model Introduction

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: License: To use DeepSeek-OCR-2, you must agree to DeepSeek’s Community License. See LICENSE for details. For more details, please refer to the official DeepSeek-OCR-2 repository.

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2. SGLang Installation

Please refer to the official SGLang installation guide for installation instructions.

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3. Model Deployment

This section provides deployment configurations optimized for different hardware platforms and use cases.

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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. Note: DeepSeek-OCR-2 has ~3.58B 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 for more details.

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3.2 Configuration Tips

For more detailed configuration tips, please refer to DeepSeek V3/V3.1/R1 Usage.

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4. Model Invocation

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4.1 Basic Usage

OpenAI-compatible request 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 The following prompts are recommended by the official model card. Structured document conversion — extracts text while preserving layout: 
<image>
<|grounding|>Convert the document to markdown.
Free-form OCR — extracts without layouts: 
<image>
Free OCR.

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5. Benchmark

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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 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.

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5.1.1 Latency-Sensitive Benchmark

  • Model Deployment Command:
sglang serve \
  --model-path deepseek-ai/DeepSeek-OCR-2 \
  --enable-multimodal \
  --host 0.0.0.0 \
  --port 30000
  • Benchmark 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:
============ 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
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5.1.2 Throughput-Sensitive Benchmark

  • Model Deployment 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:
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:
============ 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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