---
title: Step3-VL-10B
metatags:
    description: "Deploy Step3-VL-10B multimodal model with SGLang - compact 10B dense model with frontier-level vision understanding, complex reasoning, and tool calling capabilities."
---

import { Step3VL10BDeployment } from '/src/snippets/autoregressive/step-3vl-10b-deployment.jsx';

## 1. Model Introduction

[Step3-VL-10B](https://huggingface.co/stepfun-ai/Step3-VL-10B) is a lightweight open-source multimodal model developed by StepFun, designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. Despite its compact 10B parameter footprint, Step3-VL-10B excels in visual perception, complex reasoning, and human-centric alignment.

Key highlights of Step3-VL-10B include:

- **STEM Reasoning**: Achieves 94.43% on AIME 2025 and 75.95% on MathVision (with PaCoRe), demonstrating exceptional complex reasoning capabilities that outperform models 10×–20× larger.
- **Visual Perception**: Records 92.05% on MMBench and 80.11% on MMMU, establishing strong general visual understanding and multimodal reasoning.
- **GUI & OCR**: Delivers state-of-the-art performance on ScreenSpot-V2 (92.61%), ScreenSpot-Pro (51.55%), and OCRBench (86.75%), optimized for agentic and document understanding tasks.
- **Spatial Understanding**: Demonstrates emergent spatial awareness with 66.79% on BLINK and 57.21% on All-Angles-Bench, establishing strong potential for embodied intelligence applications.

For more details, please refer to the [Step3-VL-10B model card on Hugging Face](https://huggingface.co/stepfun-ai/Step3-VL-10B).

## 2. SGLang Installation

SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.

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

Step3-VL-10B is a compact 10B dense model that can run on a single GPU. Recommended starting configurations vary depending on hardware.

**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform and quantization method. SGLang supports serving Step3-VL-10B on NVIDIA B200, H200, H100, and AMD MI355X, MI325X, MI300X GPUs.

<Step3VL10BDeployment />

### 3.2 Configuration Tips

- **Single GPU Deployment**: Step3-VL-10B fits comfortably on a single GPU with BF16 precision, no tensor parallelism required.
- **Memory Management**: Set lower `--context-length` to conserve memory if needed. A value of `32768` is sufficient for most scenarios.
- **FP8 Quantization**: Use FP8 quantization to further reduce memory usage while maintaining quality.

## 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)
- [SGLang OpenAI Vision API Guide](../../../docs/basic_usage/openai_api_vision)

### 4.2 Advanced Usage

#### 4.2.1 Multi-Modal Inputs

Step3-VL-10B supports image inputs. Here's a basic example with image input:

```python Example
import time
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://ofasys-multimodal-wlcb-3-toshanghai.oss-accelerate.aliyuncs.com/wpf272043/keepme/image/receipt.png"
                }
            },
            {
                "type": "text",
                "text": "Read all the text in the image."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=messages,
    max_tokens=2048,
    extra_body={"top_k": -1}
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")
```

**Example output:**

```text Output
Response costs: 5.89s
Generated text: Auntie Anne's

CINNAMON SUGAR
1 × 17,000               17,000

SUB TOTAL                    17,000

GRAND TOTAL                 17,000

CASH IDR                    20,000

CHANGE DUE                 3,000
```

**Multi-Image Input Example:**

Step3-VL-10B can process multiple images in a single request for comparison or analysis:

```python Example
import time
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://www.civitatis.com/f/china/hong-kong/guia/taxi.jpg"
                }
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://cdn.cheapoguides.com/wp-content/uploads/sites/7/2025/05/GettyImages-509614603-1280x600.jpg"
                }
            },
            {
                "type": "text",
                "text": "Compare these two images and describe the differences in 100 words or less."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=messages,
    max_tokens=2048,
    extra_body={"top_k": -1}
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")
```

**Example Output:**

```text Output
Response costs: 3.24s
Generated text: First image: Single red Hong Kong taxi close - up, clear license plate (RX 5004), “4 SEATS” sticker, urban street with shops behind. Second image: Aerial view of many taxis (red, green) on a highway with a viaduct, some hoods open, dense arrangement. Differences: Scale (single vs many), perspective (close - up vs aerial), context (street shops vs highway), and taxi conditions (normal vs some open hoods).
```

#### 4.2.2 Reasoning Parser

Step3-VL-10B supports reasoning mode. Enable the reasoning parser during deployment to separate the thinking and content sections:

```shell Command
python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --reasoning-parser deepseek-r1 \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code
```

**Streaming with Thinking Process:**

```python Example
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

# Enable streaming to see the thinking process in real-time
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=[
        {"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
    ],
    temperature=0.7,
    max_tokens=2048,
    stream=True,
    extra_body={"top_k": -1}
)

# Process the stream
has_thinking = False
has_answer = False
thinking_started = False

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Print answer content
        if delta.content:
            # Close thinking section and add content header
            if has_thinking and not has_answer:
                print("\n=============== Content =================", flush=True)
                has_answer = True
            print(delta.content, end="", flush=True)

print()
```

**Example Output:**
```text Output
=============== Thinking =================
Okay, let's see. The problem is asking for 15% of 240. Hmm, I need to remember how to calculate percentages. So, percentage means "per hundred," right? So, 15% is the same as 15 per 100, or 15/100. To find a percentage of a number, I think you convert the percentage to a decimal and then multiply it by the number. Let me check that.

First, 15% as a decimal. To convert a percentage to a decimal, you divide by 100. So 15 divided by 100 is 0.15. Yeah, that's right. So 15% is 0.15 in decimal form. Then, to find 15% of 24
0, I need to multiply 0.15 by 240. Let me do that calculation.

Let me write it out: 0.15 * 240. Let's compute that. Maybe break it down. 0.1 is 10%, and 0.05 is 5%, so 10% of 240 is 24, and 5% of 240 is 12. Then 10% + 5% is 15%, so 24 + 12 is 36. Oh,
 that's a good way to check. So 15% is 10% plus 5%, which adds up to 36. Let me verify with the decimal method. 0.15 * 240. Let's multiply 240 by 0.1 first: 24. Then 240 by 0.05: 12. Adding them gives 36. Yep, that matches. Alternatively, 240 * 15 = 3600, and then divide by 100 (since it's per hundred), so 3600 / 100 = 36. That's another way. So 15% of 240 is 36. Let me make sure I didn't make a mistake. Let's check with another method. 10% of 240 is 24, 20% would be 48, so 15% is halfway between 10% and 20%, which is (24 + 48)/2 = 36. Yep, that works too
. So all methods point to 36. I think that's the answer.

=============== Content =================

To solve the problem "What is 15% of 240?" step by step:

---

### **Step 1: Understand the concept of percentage**
A percentage represents a portion of a whole. Specifically, "percent" means "per hundred." So, 15% means **15 out of 100**, or **15/100**.

---

### **Step 2: Convert the percentage to a decimal**
To use percentages in calculations, convert them to decimals by dividing by 100:
$$
15\% = \frac{15}{100} = 0.15
$$

---

### **Step 3: Multiply the decimal by the given number**
Now, multiply 0.15 (the decimal form of 15%) by 240:
$$
0.15 \times 240 = 36
$$

---

### **Alternative Verification Methods**

#### **Method A: Break into parts**
- 10% of 240 = $ 0.10 \times 240 = 24 $
- 5% of 240 = $ 0.05 \times 240 = 12 $
- Add them: $ 24 + 12 = 36 $

#### **Method B: Use direct multiplication**
- $ 15\% \text{ of } 240 = \frac{15}{100} \times 240 = \frac{3600}{100} = 36 $

#### **Method C: Estimate using known percentages**
- 20% of 240 = $ 0.20 \times 240 = 48 $
- 10% of 240 = $ 0.10 \times 240 = 24 $
- 15% is halfway between 10% and 20%: $ \frac{24 + 48}{2} = 36 $

---

### **Final Answer**
$$
\boxed{36}
$$
```

**Note:** The reasoning parser captures the model's step-by-step thinking process, allowing you to see how the model arrives at its conclusions.

#### 4.2.3 Tool Calling

Step3-VL-10B supports tool calling capabilities. Enable the tool call parser:

```shell Command
python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --reasoning-parser deepseek-r1 \
  --tool-call-parser hermes \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code
```

**Python Example (with Thinking Process):**

```python Example
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

# Define available tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city name"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Make request with streaming to see thinking process
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing?"}
    ],
    tools=tools,
    temperature=0.7,
    stream=True,
    extra_body={"top_k": -1}
)

# Process streaming response
thinking_started = False
has_thinking = False
tool_calls_accumulator = {}

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Accumulate tool calls
        if hasattr(delta, 'tool_calls') and delta.tool_calls:
            # Close thinking section if needed
            if has_thinking and thinking_started:
                print("\n=============== Content =================\n", flush=True)
                thinking_started = False

            for tool_call in delta.tool_calls:
                index = tool_call.index
                if index not in tool_calls_accumulator:
                    tool_calls_accumulator[index] = {
                        'name': None,
                        'arguments': ''
                    }

                if tool_call.function:
                    if tool_call.function.name:
                        tool_calls_accumulator[index]['name'] = tool_call.function.name
                    if tool_call.function.arguments:
                        tool_calls_accumulator[index]['arguments'] += tool_call.function.arguments

        # Print content
        if delta.content:
            print(delta.content, end="", flush=True)

# Print accumulated tool calls
for index, tool_call in sorted(tool_calls_accumulator.items()):
    print(f"Tool Call: {tool_call['name']}")
    print(f"   Arguments: {tool_call['arguments']}")

print()
```

**Example Output:**
```text Output
=============== Thinking =================
The user is asking about the weather in Beijing. I have a function called "get_weather" that can provide weather information for a location. Let me check the parameters:

- location: required (string) - "Beijing"
- unit: optional (string, enum: ["celsius", "fahrenheit"]) - not specified by the user, so I won't include it

I should call the function with location="Beijing".

<tool_calls>

=============== Content =================

</tool_calls>Tool Call: get_weather
   Arguments: {"location": "Beijing"}
```

**Handling Tool Call Results:**

```python Example
# After getting the tool call, execute the function
def get_weather(location, unit="celsius"):
    # Your actual weather API call here
    return f"The weather in {location} is 22°{unit[0].upper()} and sunny."

# Send tool result back to the model
messages = [
    {"role": "user", "content": "What's the weather in Beijing?"},
    {
        "role": "assistant",
        "content": None,
        "tool_calls": [{
            "id": "call_123",
            "type": "function",
            "function": {
                "name": "get_weather",
                "arguments": '{"location": "Beijing", "unit": "celsius"}'
            }
        }]
    },
    {
        "role": "tool",
        "tool_call_id": "call_123",
        "content": get_weather("Beijing", "celsius")
    }
]

final_response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=messages,
    temperature=0.7,
    extra_body={"top_k": -1}
)

print(final_response.choices[0].message.content)
```

**Note:**

- The reasoning parser shows how the model decides to use a tool
- Tool calls are clearly marked with the function name and arguments
- You can then execute the function and send the result back to continue the conversation

## 5. Benchmark

### 5.1 Speed Benchmark

**Test Environment:**

- Hardware: NVIDIA B200 GPU (1x)
- Model: stepfun-ai/Step3-VL-10B
- Tensor Parallelism: 1
- sglang version: 0.5.8+

We use SGLang's built-in benchmarking tool to conduct performance evaluation with random images.

#### 5.1.1 Latency-Sensitive Benchmark

- Model Deployment Command:

```shell Command
python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code
```

- Benchmark Command:

```shell Command
python3 -m sglang.bench_serving \
  --backend sglang-oai-chat \
  --host 127.0.0.1 \
  --port 30000 \
  --model stepfun-ai/Step3-VL-10B \
  --dataset-name image \
  --image-count 2 \
  --image-resolution 720p \
  --random-input-len 128 \
  --random-output-len 1024 \
  --num-prompts 10 \
  --max-concurrency 1
```

- Result:

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  30.85
Total input tokens:                      14120
Total input text tokens:                 720
Total input vision tokens:               13400
Total generated tokens:                  4220
Total generated tokens (retokenized):    4217
Request throughput (req/s):              0.32
Input token throughput (tok/s):          457.71
Output token throughput (tok/s):         136.79
Peak output token throughput (tok/s):    240.00
Peak concurrent requests:                2
Total token throughput (tok/s):          594.50
Concurrency:                             1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   3083.40
Median E2E Latency (ms):                 2747.00
P90 E2E Latency (ms):                    4574.50
P99 E2E Latency (ms):                    5462.49
---------------Time to First Token----------------
Mean TTFT (ms):                          1327.69
Median TTFT (ms):                        1341.01
P99 TTFT (ms):                           1486.11
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          4.16
Median TPOT (ms):                        4.17
P99 TPOT (ms):                           4.18
---------------Inter-Token Latency----------------
Mean ITL (ms):                           4.17
Median ITL (ms):                         4.18
P95 ITL (ms):                            4.30
P99 ITL (ms):                            4.38
Max ITL (ms):                            8.24
==================================================
```

#### 5.1.2 Throughput-Sensitive Benchmark

- Benchmark Command:

```shell Command
python3 -m sglang.bench_serving \
  --backend sglang-oai-chat \
  --host 127.0.0.1 \
  --port 30000 \
  --model stepfun-ai/Step3-VL-10B \
  --dataset-name image \
  --image-count 2 \
  --image-resolution 720p \
  --random-input-len 128 \
  --random-output-len 1024 \
  --num-prompts 1000 \
  --max-concurrency 100
```

- Result:

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  976.52
Total input tokens:                      1416949
Total input text tokens:                 76949
Total input vision tokens:               1340000
Total generated tokens:                  510855
Total generated tokens (retokenized):    510526
Request throughput (req/s):              1.02
Input token throughput (tok/s):          1451.02
Output token throughput (tok/s):         523.14
Peak output token throughput (tok/s):    20429.00
Peak concurrent requests:                103
Total token throughput (tok/s):          1974.16
Concurrency:                             99.81
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   97463.22
Median E2E Latency (ms):                 91872.75
P90 E2E Latency (ms):                    118553.42
P99 E2E Latency (ms):                    198445.56
---------------Time to First Token----------------
Mean TTFT (ms):                          94379.07
Median TTFT (ms):                        87163.09
P99 TTFT (ms):                           194871.41
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          5.89
Median TPOT (ms):                        5.72
P99 TPOT (ms):                           23.58
---------------Inter-Token Latency----------------
Mean ITL (ms):                           6.05
Median ITL (ms):                         0.13
P95 ITL (ms):                            0.56
P99 ITL (ms):                            3.99
Max ITL (ms):                            97551.06
==================================================
```

### 5.2 Accuracy Benchmark

#### 5.2.1 MMMU Benchmark

You can evaluate the model's accuracy using the MMMU dataset:

- Model Deployment Command:

```shell Command
python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code
```

- Benchmark Command:

```shell Command
python3 benchmark/mmmu/bench_sglang.py \
    --port 30000 \
    --concurrency 64
```

- Result:

```text Output
Benchmark time: 934.6179109360091
answers saved to: ./answer_sglang.json
Evaluating...
answers saved to: ./answer_sglang.json
{'Accounting': {'acc': 0.667, 'num': 30},
 'Agriculture': {'acc': 0.367, 'num': 30},
 'Architecture_and_Engineering': {'acc': 0.4, 'num': 30},
 'Art': {'acc': 0.467, 'num': 30},
 'Art_Theory': {'acc': 0.5, 'num': 30},
 'Basic_Medical_Science': {'acc': 0.367, 'num': 30},
 'Biology': {'acc': 0.3, 'num': 30},
 'Chemistry': {'acc': 0.467, 'num': 30},
 'Clinical_Medicine': {'acc': 0.567, 'num': 30},
 'Computer_Science': {'acc': 0.467, 'num': 30},
 'Design': {'acc': 0.567, 'num': 30},
 'Diagnostics_and_Laboratory_Medicine': {'acc': 0.3, 'num': 30},
 'Economics': {'acc': 0.6, 'num': 30},
 'Electronics': {'acc': 0.567, 'num': 30},
 'Energy_and_Power': {'acc': 0.633, 'num': 30},
 'Finance': {'acc': 0.733, 'num': 30},
 'Geography': {'acc': 0.333, 'num': 30},
 'History': {'acc': 0.533, 'num': 30},
 'Literature': {'acc': 0.533, 'num': 30},
 'Manage': {'acc': 0.6, 'num': 30},
 'Marketing': {'acc': 0.767, 'num': 30},
 'Materials': {'acc': 0.6, 'num': 30},
 'Math': {'acc': 0.7, 'num': 30},
 'Mechanical_Engineering': {'acc': 0.333, 'num': 30},
 'Music': {'acc': 0.4, 'num': 30},
 'Overall': {'acc': 0.523, 'num': 900},
 'Overall-Art and Design': {'acc': 0.483, 'num': 120},
 'Overall-Business': {'acc': 0.673, 'num': 150},
 'Overall-Health and Medicine': {'acc': 0.513, 'num': 150},
 'Overall-Humanities and Social Science': {'acc': 0.492, 'num': 120},
 'Overall-Science': {'acc': 0.5, 'num': 150},
 'Overall-Tech and Engineering': {'acc': 0.481, 'num': 210},
 'Pharmacy': {'acc': 0.6, 'num': 30},
 'Physics': {'acc': 0.7, 'num': 30},
 'Psychology': {'acc': 0.467, 'num': 30},
 'Public_Health': {'acc': 0.733, 'num': 30},
 'Sociology': {'acc': 0.433, 'num': 30}}
eval out saved to ./val_sglang.json
Overall accuracy: 0.523
```
