> ## Documentation Index > Fetch the complete documentation index at: https://docs.sglang.io/llms.txt > Use this file to discover all available pages before exploring further. # Step3-VL-10B export const Step3VL10BDeployment = () => { const options = { hardware: { name: 'hardware', title: 'Hardware Platform', items: [{ id: 'b200', label: 'B200', default: true }, { id: 'h100', label: 'H100', default: false }, { id: 'h200', label: 'H200', default: false }, { id: 'a100', label: 'A100', default: false }, { id: 'mi300x', label: 'MI300X', default: false }, { id: 'mi325x', label: 'MI325X', default: false }, { id: 'mi355x', label: 'MI355X', default: false }] }, modelsize: { name: 'modelsize', title: 'Model Size', items: [{ id: '10b', label: '10B', subtitle: 'Dense', default: true }] }, quantization: { name: 'quantization', title: 'Quantization', items: [{ id: 'bf16', label: 'BF16', default: true }, { id: 'fp8', label: 'FP8', default: false }] }, reasoning: { name: 'reasoning', title: 'Reasoning Parser', items: [{ id: 'disabled', label: 'Disabled', default: true }, { id: 'enabled', label: 'Enabled', default: false }], commandRule: value => value === 'enabled' ? '--reasoning-parser deepseek-r1' : null }, toolcall: { name: 'toolcall', title: 'Tool Call Parser', items: [{ id: 'disabled', label: 'Disabled', default: true }, { id: 'enabled', label: 'Enabled', default: false }], commandRule: value => value === 'enabled' ? '--tool-call-parser hermes' : null } }; const modelConfigs = { '10b': { baseName: '10B', isMOE: false, b200: { tp: 1, bf16: true, fp8: true }, h100: { tp: 1, bf16: true, fp8: true }, h200: { tp: 1, bf16: true, fp8: true }, a100: { tp: 1, bf16: true, fp8: true }, mi300x: { tp: 1, bf16: true, fp8: true }, mi325x: { tp: 1, bf16: true, fp8: true }, mi355x: { tp: 1, bf16: true, fp8: true } } }; const generateCommand = values => { const {hardware, modelsize: modelSize, quantization} = values; const modelSizeConfig = modelConfigs[modelSize]; if (!modelSizeConfig) { return `# Error: Unknown model size: ${modelSize}`; } const hwConfig = modelSizeConfig[hardware]; if (!hwConfig) { return `# Error: Unknown hardware platform: ${hardware}`; } const quantSuffix = quantization === 'fp8' ? '-FP8' : ''; const modelName = `stepfun-ai/Step3-VL-10B${quantSuffix}`; let cmd = 'python -m sglang.launch_server \\\n'; cmd += ` --model ${modelName}`; if (hwConfig.tp > 1) { cmd += ` \\\n --tp ${hwConfig.tp}`; } cmd += ' \\\n --host 0.0.0.0 \\\n --port 30000'; if (hardware === 'mi300x' || hardware === 'mi325x' || hardware === 'mi355x') { cmd += ' \\\n --attention-backend triton'; } cmd += ' \\\n --trust-remote-code'; for (const [key, option] of Object.entries(options)) { if (option.commandRule) { const rule = option.commandRule(values[key]); if (rule) { cmd += ` \\\n ${rule}`; } } } return cmd; }; const getInitialState = () => { const initialState = {}; Object.entries(options).forEach(([key, option]) => { if (option.type === 'checkbox') { initialState[key] = (option.items || []).filter(item => item.default).map(item => item.id); return; } if (option.type === 'text') { initialState[key] = option.default || ''; return; } let items = option.items || []; if (option.getDynamicItems) { const defaultValues = {}; Object.entries(options).forEach(([innerKey, innerOption]) => { if (innerOption.type === 'checkbox') { defaultValues[innerKey] = (innerOption.items || []).filter(item => item.default).map(item => item.id); } else if (innerOption.type === 'text') { defaultValues[innerKey] = innerOption.default || ''; } else if (innerOption.items && innerOption.items.length > 0) { const defaultItem = innerOption.items.find(item => item.default); defaultValues[innerKey] = defaultItem ? defaultItem.id : innerOption.items[0].id; } }); items = option.getDynamicItems(defaultValues); } const defaultItem = items && items.find(item => item.default); initialState[key] = defaultItem ? defaultItem.id : items && items[0] ? items[0].id : ''; }); return initialState; }; const [values, setValues] = useState(getInitialState); const [isDark, setIsDark] = useState(false); useEffect(() => { const checkDarkMode = () => { const html = document.documentElement; const isDarkMode = html.classList.contains('dark') || html.getAttribute('data-theme') === 'dark' || html.style.colorScheme === 'dark'; setIsDark(isDarkMode); }; checkDarkMode(); const observer = new MutationObserver(checkDarkMode); observer.observe(document.documentElement, { attributes: true, attributeFilter: ['class', 'data-theme', 'style'] }); return () => observer.disconnect(); }, []); const handleRadioChange = (optionName, value) => { setValues(prev => ({ ...prev, [optionName]: value })); }; const handleCheckboxChange = (optionName, itemId, isChecked) => { setValues(prev => { const currentValues = prev[optionName] || []; if (isChecked) { return { ...prev, [optionName]: [...currentValues, itemId] }; } return { ...prev, [optionName]: currentValues.filter(id => id !== itemId) }; }); }; const handleTextChange = (optionName, value) => { setValues(prev => ({ ...prev, [optionName]: value })); }; const command = generateCommand(values); const containerStyle = { maxWidth: '900px', margin: '0 auto', display: 'flex', flexDirection: 'column', gap: '4px' }; const cardStyle = { padding: '8px 12px', border: `1px solid ${isDark ? '#374151' : '#e5e7eb'}`, borderLeft: `3px solid ${isDark ? '#E85D4D' : '#D45D44'}`, borderRadius: '4px', display: 'flex', alignItems: 'center', gap: '12px', background: isDark ? '#1f2937' : '#fff' }; const titleStyle = { fontSize: '13px', fontWeight: '600', minWidth: '140px', flexShrink: 0, color: isDark ? '#e5e7eb' : 'inherit' }; const itemsStyle = { display: 'flex', rowGap: '2px', columnGap: '6px', flexWrap: 'wrap', alignItems: 'center', flex: 1 }; const labelBaseStyle = { padding: '4px 10px', border: `1px solid ${isDark ? '#9ca3af' : '#d1d5db'}`, borderRadius: '3px', cursor: 'pointer', display: 'inline-flex', flexDirection: 'column', alignItems: 'center', justifyContent: 'center', fontWeight: '500', fontSize: '13px', transition: 'all 0.2s', userSelect: 'none', minWidth: '45px', textAlign: 'center', flex: 1, background: isDark ? '#374151' : '#fff', color: isDark ? '#e5e7eb' : 'inherit' }; const checkedStyle = { background: '#D45D44', color: 'white', borderColor: '#D45D44' }; const disabledStyle = { cursor: 'not-allowed', opacity: 0.5 }; const subtitleStyle = { display: 'block', fontSize: '9px', marginTop: '1px', lineHeight: '1.1', opacity: 0.7 }; const textInputStyle = { flex: 1, padding: '8px 10px', borderRadius: '4px', border: `1px solid ${isDark ? '#4b5563' : '#d1d5db'}`, background: isDark ? '#111827' : '#fff', color: isDark ? '#e5e7eb' : '#111827', fontSize: '13px' }; const commandDisplayStyle = { flex: 1, padding: '12px 16px', background: isDark ? '#111827' : '#f5f5f5', borderRadius: '6px', fontFamily: "'Menlo', 'Monaco', 'Courier New', monospace", fontSize: '12px', lineHeight: '1.5', color: isDark ? '#e5e7eb' : '#374151', whiteSpace: 'pre-wrap', overflowX: 'auto', margin: 0, border: `1px solid ${isDark ? '#374151' : '#e5e7eb'}` }; return
{Object.entries(options).map(([key, option]) => { if (option.condition && !option.condition(values)) { return null; } const items = option.getDynamicItems ? option.getDynamicItems(values) : option.items || []; return
{option.title}
{option.type === 'text' ? handleTextChange(option.name, event.target.value)} style={textInputStyle} /> : option.type === 'checkbox' ? (option.items || []).map(item => { const isChecked = (values[option.name] || []).includes(item.id); const isDisabled = item.required || typeof item.disabledWhen === 'function' && item.disabledWhen(values); return ; }) : items.map(item => { const isChecked = values[option.name] === item.id; const isDisabled = Boolean(item.disabled); return ; })}
; })}
Run this Command:
{command}
; }; ## 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. ### 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 theme={null} 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 theme={null} 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 theme={null} 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 theme={null} 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 theme={null} 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 theme={null} 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 theme={null} =============== 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 theme={null} 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 theme={null} 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 theme={null} =============== 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". =============== Content ================= Tool Call: get_weather Arguments: {"location": "Beijing"} ``` **Handling Tool Call Results:** ```python Example theme={null} # 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 theme={null} 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 theme={null} 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 theme={null} ============ 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 theme={null} 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 theme={null} ============ 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 theme={null} 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 theme={null} python3 benchmark/mmmu/bench_sglang.py \ --port 30000 \ --concurrency 64 ``` * Result: ```text Output theme={null} 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 ```