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1. Model Introduction

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

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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

For basic API usage and request examples, please refer to:

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5. Benchmark

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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 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.

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5.1.1 Latency-Sensitive Benchmark

  • Model Deployment Command:
python3 -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-OCR \
  --tp 1 \
  --dtype float16 \
  --host 0.0.0.0 \
  --port 8000
  • Benchmark 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:
============ 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
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5.1.2 Throughput-Sensitive Benchmark

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