> ## 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. # Qwen3.6 export const Qwen36Deployment = () => { const options = { hardware: { name: 'hardware', title: 'Hardware Platform', items: [{ id: 'h100', label: 'H100', default: true }, { id: 'h200', label: 'H200', default: false }, { id: 'b200', label: 'B200', default: false }] }, modelSize: { name: 'modelSize', title: 'Model Size', items: [{ id: '35b-a3b', label: '35B-A3B (MoE)', default: true }, { id: '27b', label: '27B (Dense)', default: false }] }, quantization: { name: 'quantization', title: 'Quantization', items: [{ id: 'fp8', label: 'FP8', default: true }, { id: 'bf16', label: 'BF16', default: false }] }, reasoning: { name: 'reasoning', title: 'Reasoning Parser', items: [{ id: 'disabled', label: 'Disabled', default: false }, { id: 'enabled', label: 'Enabled', default: true }], commandRule: value => value === 'enabled' ? '--reasoning-parser qwen3' : null }, toolcall: { name: 'toolcall', title: 'Tool Call Parser', items: [{ id: 'disabled', label: 'Disabled', default: false }, { id: 'enabled', label: 'Enabled', default: true }], commandRule: value => value === 'enabled' ? '--tool-call-parser qwen3_coder' : null }, speculative: { name: 'speculative', title: 'Speculative Decoding (MTP)', items: [{ id: 'disabled', label: 'Disabled', default: false }, { id: 'enabled', label: 'Enabled', default: true }], commandRule: value => value === 'enabled' ? '--speculative-algorithm EAGLE \\\n --speculative-num-steps 3 \\\n --speculative-eagle-topk 1 \\\n --speculative-num-draft-tokens 4' : null }, mambaCache: { name: 'mambaCache', title: 'Mamba Radix Cache', getDynamicItems: values => { const mtpEnabled = values.speculative === 'enabled'; if (mtpEnabled) { return [{ id: 'v1', label: 'V1', default: false, disabled: true }, { id: 'v2', label: 'V2', default: true }]; } return [{ id: 'v1', label: 'V1', default: true }, { id: 'v2', label: 'V2', default: false }]; }, commandRule: value => value === 'v2' ? '--mamba-scheduler-strategy extra_buffer' : null } }; const modelConfigs = { '35b-a3b': { baseName: '35B-A3B', h100: { bf16: { tp: 1, mem: 0.8 }, fp8: { tp: 1, mem: 0.8 } }, h200: { bf16: { tp: 1, mem: 0.8 }, fp8: { tp: 1, mem: 0.8 } }, b200: { bf16: { tp: 1, mem: 0.8 }, fp8: { tp: 1, mem: 0.8 } } }, '27b': { baseName: '27B', h100: { bf16: { tp: 1, mem: 0.8 }, fp8: { tp: 1, mem: 0.8 } }, h200: { bf16: { tp: 1, mem: 0.8 }, fp8: { tp: 1, mem: 0.8 } }, b200: { bf16: { tp: 1, mem: 0.8 }, fp8: { tp: 1, mem: 0.8 } } } }; const resolveItems = (option, vals) => typeof option.getDynamicItems === 'function' ? option.getDynamicItems(vals) : option.items; const getInitialState = () => { const initialState = {}; for (const [key, option] of Object.entries(options)) { const items = resolveItems(option, initialState); const def = items.find(item => item.default && !item.disabled) || items.find(item => !item.disabled) || items[0]; initialState[key] = def.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(); }, []); useEffect(() => { setValues(prev => { const next = { ...prev }; for (const [key, option] of Object.entries(options)) { if (typeof option.getDynamicItems !== 'function') continue; const items = option.getDynamicItems(next); const current = items.find(item => item.id === next[key]); if (!current || current.disabled) { const fallback = items.find(item => item.default && !item.disabled) || items.find(item => !item.disabled); if (fallback) next[key] = fallback.id; } } return next; }); }, [values.speculative]); const handleRadioChange = (optionName, value) => { setValues(prev => ({ ...prev, [optionName]: value })); }; const generateCommand = () => { const {hardware, modelSize, quantization, speculative} = values; const sizeConfig = modelConfigs[modelSize]; const hwConfig = sizeConfig?.[hardware]?.[quantization]; if (!hwConfig) { return '# Please select a valid hardware and quantization combination'; } const quantSuffix = quantization === 'fp8' ? '-FP8' : ''; const modelName = `Qwen/Qwen3.6-${sizeConfig.baseName}${quantSuffix}`; let cmd = ''; if (speculative === 'enabled') { cmd += 'SGLANG_ENABLE_SPEC_V2=1 '; } cmd += `sglang serve --model-path ${modelName}`; if (hwConfig.tp > 1) { cmd += ` \\\n --tp ${hwConfig.tp}`; } const adjustedValues = { ...values, mambaCache: speculative === 'enabled' ? 'v2' : values.mambaCache }; for (const [key, option] of Object.entries(options)) { if (key === 'quantization' || key === 'hardware' || key === 'modelSize') continue; if (!option.commandRule) continue; const rule = option.commandRule(adjustedValues[key]); if (rule) { cmd += ` \\\n ${rule}`; } } if (hardware === 'b200') { cmd += ` \\\n --attention-backend trtllm_mha`; } cmd += ` \\\n --mem-fraction-static ${hwConfig.mem}`; return cmd; }; 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: 0.4 }; 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]) => { const items = resolveItems(option, values); return
{option.title}
{items.map(item => { const isChecked = values[option.name] === item.id; const isDisabled = !!item.disabled; return ; })}
; })}
Run this Command:
{generateCommand()}
; }; ## 1. Model Introduction The Qwen3.6 series is developed by Alibaba. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, delivering substantial upgrades in agentic coding and thinking preservation. Two size/sparsity variants are released: * [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) — **Sparse MoE** (35B total, 3B active) on a Gated Delta Networks backbone. * [Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) — **Dense** hybrid GDN; smaller weights footprint, single-GPU friendly. Both variants share the same hybrid reasoning, tool-calling, and multimodal interface and natively handle context lengths of up to 262,144 tokens, extensible to over 1M tokens. **Key Features:** * **Agentic Coding**: Handles frontend workflows and repository-level reasoning with greater fluency and precision * **Thinking Preservation**: New option to retain reasoning context from historical messages, streamlining iterative development * **Efficient Hybrid Architecture**: Gated Delta Networks backbone; sparse MoE (35B / 3B active) or dense 27B variant * **Hybrid Reasoning**: Thinking mode enabled by default with step-by-step reasoning, can be disabled for direct responses * **Tool Calling**: Built-in tool calling support with `qwen3_coder` parser * **Multi-Token Prediction (MTP)**: Speculative decoding support for lower latency; both MoE and Dense variants ship `mtp.safetensors` * **Multimodal**: Unified vision-language model supporting text, image, and video inputs **Available Models:**
Model Architecture Weights
Qwen3.6-35B-A3B (BF16) MoE 35B / 3B active [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B)
Qwen3.6-35B-A3B (FP8) MoE 35B / 3B active [Qwen/Qwen3.6-35B-A3B-FP8](https://huggingface.co/Qwen/Qwen3.6-35B-A3B-FP8)
Qwen3.6-27B (BF16) Dense 27B [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B)
Qwen3.6-27B (FP8) Dense 27B [Qwen/Qwen3.6-27B-FP8](https://huggingface.co/Qwen/Qwen3.6-27B-FP8)
**License:** Apache 2.0 ## 2. SGLang Installation SGLang `>=0.5.10` is required for Qwen3.6. You can install from PyPI, from source, or use a Docker image: ```bash Command theme={null} # Install from PyPI uv pip install sglang # Or install from source uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python' # Or use Docker (NVIDIA GPUs) docker pull lmsysorg/sglang:latest ``` For the full Docker setup and other installation methods, please refer to the [official SGLang installation guide](../../../docs/get-started/install). ## 3. Model Deployment This section provides deployment configurations optimized for different hardware platforms and use cases. ### 3.1 Basic Configuration **Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform and capabilities. ### 3.2 Configuration Tips * Speculative decoding (MTP) can significantly reduce latency for interactive use cases. * **Mamba Radix Cache**: Qwen3.6's hybrid Gated Delta Networks architecture supports two mamba scheduling strategies via `--mamba-scheduler-strategy`: * **V1 (`no_buffer`)**: Default. No overlap scheduler, lower memory usage. * **V2 (`extra_buffer`)**: Enables overlap scheduling and branching point caching with `--mamba-scheduler-strategy extra_buffer --page-size 64`. Requires FLA kernel backend (NVIDIA GPUs only). Trades higher mamba state memory for better throughput. * The `--mem-fraction-static` flag is recommended for optimal memory utilization, adjust it based on your hardware and workload. * Context length defaults to 262,144 tokens. If you encounter OOM errors, consider reducing it, but maintain at least 128K to preserve thinking capabilities. * **CUDA IPC Transport**: Add `SGLANG_USE_CUDA_IPC_TRANSPORT=1` as an environment variable to use CUDA IPC for transferring multimodal features, significantly improving TTFT (Time To First Token). Note: this consumes additional memory proportional to image size, so you may need to lower `--mem-fraction-static` or `--max-running-requests`. * **Multimodal Attention Backend**: Use `--mm-attention-backend fa3` on H100/H200 for better vision performance, or `--mm-attention-backend fa4` on B200. * For processing large images or videos, you may need to lower `--mem-fraction-static` to leave room for image feature tensors. * Hardware requirements: * **35B-A3B BF16**: \~70GB for weights. TP=1 fits on all supported hardware. * **35B-A3B FP8**: \~35GB for weights. TP=1 fits on all supported hardware. * **27B BF16**: \~54GB for weights. TP=1 fits on all supported hardware. * **27B FP8**: \~27GB for weights. TP=1 fits on all supported hardware. All Qwen3.6 variants (MoE 35B-A3B and Dense 27B) fit on a single supported GPU at both precisions:
Hardware Memory BF16 TP FP8 TP
H100 80GB 1 1
H200 141GB 1 1
B200 183GB 1 1
## 4. Model Invocation Deploy Qwen3.6 with the following command (H200, all features enabled). Swap `--model-path` to `Qwen/Qwen3.6-27B-FP8` for the dense 27B variant — all other flags carry over: ```shell Command theme={null} SGLANG_ENABLE_SPEC_V2=1 sglang serve \ --model-path Qwen/Qwen3.6-35B-A3B-FP8 \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder \ --speculative-algorithm EAGLE \ --speculative-num-steps 3 \ --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 4 \ --mem-fraction-static 0.8 \ --host 0.0.0.0 \ --port 30000 ``` ### 4.1 Basic Usage For basic API usage and request examples, please refer to: * [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request) ### 4.2 Vision Input Qwen3.6 supports image and video inputs as a unified vision-language model. **Image Input Example:** ```python Example theme={null} from openai import OpenAI client = OpenAI( base_url="http://localhost:30000/v1", api_key="EMPTY" ) response = client.chat.completions.create( model="Qwen/Qwen3.6-35B-A3B-FP8", messages=[ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/CI_Demo/mathv-1327.jpg" } }, { "type": "text", "text": "Describe this image in detail." } ] } ], max_tokens=2048, stream=True ) thinking_started = False has_thinking = False has_answer = False for chunk in response: if chunk.choices and len(chunk.choices) > 0: delta = chunk.choices[0].delta 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) if delta.content: if has_thinking and not has_answer: print("\n=============== Content =================", flush=True) has_answer = True print(delta.content, end="", flush=True) print() ``` **Video Input Example:** ```python Example theme={null} from openai import OpenAI client = OpenAI( base_url="http://localhost:30000/v1", api_key="EMPTY" ) response = client.chat.completions.create( model="Qwen/Qwen3.6-35B-A3B-FP8", messages=[ { "role": "user", "content": [ { "type": "video_url", "video_url": { "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/video/N1cdUjctpG8.mp4" } }, { "type": "text", "text": "Describe what happens in this video." } ] } ], max_tokens=2048, stream=True ) thinking_started = False has_thinking = False has_answer = False for chunk in response: if chunk.choices and len(chunk.choices) > 0: delta = chunk.choices[0].delta 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) if delta.content: if has_thinking and not has_answer: print("\n=============== Content =================", flush=True) has_answer = True print(delta.content, end="", flush=True) print() ``` ### 4.3 Advanced Usage #### 4.3.1 Reasoning Parser Qwen3.6 supports Thinking mode **by default**. Enable the reasoning parser during deployment to separate the thinking and content sections. The thinking process is returned via `reasoning_content` in the streaming response. To disable thinking and use Instruct mode, pass `chat_template_kwargs` at request time: * **Thinking mode** (default): The model performs step-by-step reasoning before answering. No extra parameters needed. * **Instruct mode** (`{"enable_thinking": false}`): The model responds directly without a thinking process. **Example 1: Thinking Mode (Default)** ```python Example theme={null} from openai import OpenAI client = OpenAI( base_url="http://localhost:30000/v1", api_key="EMPTY" ) response = client.chat.completions.create( model="Qwen/Qwen3.6-35B-A3B-FP8", messages=[ {"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"} ], max_tokens=2048, stream=True ) 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 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) if delta.content: if has_thinking and not has_answer: print("\n=============== Content =================", flush=True) has_answer = True print(delta.content, end="", flush=True) print() ``` **Example 2: Instruct Mode (Thinking Off)** To disable thinking and get a direct response, pass `{"enable_thinking": false}` via `chat_template_kwargs`: ```python Example theme={null} from openai import OpenAI client = OpenAI( base_url="http://localhost:30000/v1", api_key="EMPTY" ) response = client.chat.completions.create( model="Qwen/Qwen3.6-35B-A3B-FP8", messages=[ {"role": "user", "content": "What is 15% of 240?"} ], extra_body={"chat_template_kwargs": {"enable_thinking": False}}, max_tokens=2048, stream=True ) for chunk in response: if chunk.choices and len(chunk.choices) > 0: delta = chunk.choices[0].delta if delta.content: print(delta.content, end="", flush=True) print() ``` #### 4.3.2 Thinking Preservation Qwen3.6 has been trained to preserve and leverage thinking traces from historical messages. Enable this for agent scenarios where maintaining full reasoning context improves decision consistency: ```python Example theme={null} from openai import OpenAI client = OpenAI( base_url="http://localhost:30000/v1", api_key="EMPTY" ) response = client.chat.completions.create( model="Qwen/Qwen3.6-35B-A3B-FP8", messages=[ {"role": "user", "content": "Help me plan a web app architecture."} ], extra_body={"chat_template_kwargs": {"preserve_thinking": True}}, max_tokens=2048, stream=True ) thinking_started = False has_thinking = False has_answer = False for chunk in response: if chunk.choices and len(chunk.choices) > 0: delta = chunk.choices[0].delta 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) if delta.content: if has_thinking and not has_answer: print("\n=============== Content =================", flush=True) has_answer = True print(delta.content, end="", flush=True) print() ``` #### 4.3.3 Tool Calling Qwen3.6 supports tool calling capabilities. Enable the tool call parser during deployment. ```python Example theme={null} from openai import OpenAI client = OpenAI( base_url="http://localhost:30000/v1", api_key="EMPTY" ) 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"] } } } ] response = client.chat.completions.create( model="Qwen/Qwen3.6-35B-A3B-FP8", messages=[ {"role": "user", "content": "What's the weather in Beijing?"} ], tools=tools, stream=True ) thinking_started = False has_thinking = False for chunk in response: if chunk.choices and len(chunk.choices) > 0: delta = chunk.choices[0].delta 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) if hasattr(delta, 'tool_calls') and delta.tool_calls: if has_thinking and thinking_started: print("\n=============== Content =================", flush=True) thinking_started = False for tool_call in delta.tool_calls: if tool_call.function: print(f"Tool Call: {tool_call.function.name}") print(f" Arguments: {tool_call.function.arguments}") if delta.content: print(delta.content, end="", flush=True) print() ```