> ## 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.

# Ring-2.6-1T

export const Ring261TDeployment = () => {
  const options = {
    hardware: {
      name: 'hardware',
      title: 'Hardware Platform',
      items: [{
        id: 'gb300',
        label: 'GB300 x4',
        default: true
      }, {
        id: 'b200',
        label: 'B200 x8',
        default: false
      }, {
        id: 'h200',
        label: 'H200 x8',
        default: false
      }]
    },
    toolcall: {
      name: 'toolcall',
      title: 'Tool Call Parser',
      items: [{
        id: 'enabled',
        label: 'Enabled',
        default: true
      }, {
        id: 'disabled',
        label: 'Disabled',
        default: false
      }]
    },
    reasoning: {
      name: 'reasoning',
      title: 'Reasoning Parser',
      items: [{
        id: 'enabled',
        label: 'Enabled',
        default: true
      }, {
        id: 'disabled',
        label: 'Disabled',
        default: false
      }]
    }
  };
  const modelConfigs = {
    gb300: {
      tp: 4,
      memFraction: '0.95'
    },
    b200: {
      tp: 8,
      memFraction: '0.8'
    },
    h200: {
      tp: 8,
      memFraction: '0.95'
    }
  };
  const getInitialState = () => {
    const initialState = {};
    Object.entries(options).forEach(([key, option]) => {
      const defaultItem = option.items.find(item => item.default);
      initialState[key] = defaultItem ? defaultItem.id : option.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 generateCommand = () => {
    const {hardware, toolcall, reasoning} = values;
    const {tp, memFraction} = modelConfigs[hardware];
    let cmd = 'sglang serve \\\n';
    cmd += '  --model-path inclusionAI/Ring-2.6-1T \\\n';
    cmd += `  --tp-size ${tp} \\\n`;
    cmd += '  --trust-remote-code \\\n';
    cmd += '  --host 0.0.0.0 \\\n';
    cmd += '  --port ${PORT} \\\n';
    cmd += `  --mem-fraction-static ${memFraction} \\\n`;
    cmd += '  --model-loader-extra-config \'{"enable_multithread_load":"true","num_threads":64}\'';
    if (toolcall === 'enabled') {
      cmd += ' \\\n  --tool-call-parser glm';
    }
    if (reasoning === 'enabled') {
      cmd += ' \\\n  --reasoning-parser deepseek-r1';
    }
    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 subtitleStyle = {
    display: 'block',
    fontSize: '9px',
    marginTop: '1px',
    lineHeight: '1.1',
    opacity: 0.7
  };
  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 <div style={containerStyle} className="not-prose">
      {Object.entries(options).map(([key, option]) => <div key={key} style={cardStyle}>
          <div style={titleStyle}>{option.title}</div>
          <div style={itemsStyle}>
            {option.items.map(item => {
    const isChecked = values[option.name] === item.id;
    return <label key={item.id} style={{
      ...labelBaseStyle,
      ...isChecked ? checkedStyle : {}
    }}>
                  <input type="radio" name={option.name} value={item.id} checked={isChecked} onChange={() => handleRadioChange(option.name, item.id)} style={{
      display: 'none'
    }} />
                  {item.label}
                  {item.subtitle && <small style={{
      ...subtitleStyle,
      color: isChecked ? 'rgba(255,255,255,0.85)' : 'inherit'
    }}>{item.subtitle}</small>}
                </label>;
  })}
          </div>
        </div>)}
      <div style={cardStyle}>
        <div style={titleStyle}>Run this Command:</div>
        <pre style={commandDisplayStyle}>{generateCommand()}</pre>
      </div>
    </div>;
};

## 1. Model Introduction

[Ring-2.6-1T](https://huggingface.co/inclusionAI/Ring-2.6-1T) is InclusionAI's trillion-parameter flagship reasoning model for real-world complex task execution. It targets agent workflows, engineering development, scientific research analysis, enterprise automation, and other long-horizon settings where the model must plan, use tools, recover from intermediate errors, and keep context across multiple steps.

**Key Features:**

* **Trillion-Scale Reasoning Model**: `BailingMoeV2_5ForCausalLM` with a `bailing_hybrid` architecture, 80 hidden layers, 256 routed experts, 8 selected experts per token, and FP8 compressed-tensors weights.
* **Agent Execution**: Designed for multi-step task decomposition, tool collaboration, context continuation, and long-horizon execution. The model card reports 87.60 on PinchBench, 63.82 on ClawEval, and 95.32 on Tau2-Bench Telecom for the `high` setting.
* **Reasoning Effort**: The model card describes `high` and `xhigh` reasoning-effort modes. In SGLang's OpenAI-compatible chat API, use top-level `reasoning_effort: "high"` for production agent workflows. To request the model-card `xhigh` prompt path, pass it through `chat_template_kwargs.reasoning_effort`.
* **Hybrid Attention**: Uses the Bailing hybrid stack with MLA plus Lightning linear attention kernels in SGLang.
* **Context Length**: Native 128K in the released config. Configure YaRN separately if you need a 256K deployment.

**Available Models:**

* **FP8 (E4M3 compressed-tensors)**: [inclusionAI/Ring-2.6-1T](https://huggingface.co/inclusionAI/Ring-2.6-1T)

**License:** MIT

## 2. SGLang Installation

Ring-2.6-1T requires recent SGLang builds with Bailing hybrid model support. Start with the latest SGLang Docker image when validating this cookbook:

```bash Command theme={null}
docker pull lmsysorg/sglang:latest
```

For other installation methods, please refer to the [official SGLang installation guide](../../../docs/get-started/install).

## 3. Model Deployment

Use the selector below to generate a single-node command for the tested hardware targets.

<Ring261TDeployment />

### Configuration Tips

* `--trust-remote-code` is required for the model's custom Bailing hybrid implementation.
* Use `--tp-size 4` on a single 4-GPU GB300 node.
* Use `--tp-size 8` on a single 8-GPU B200 node.
* Use `--tp-size 8` on a single 8-GPU H200 node.
* Use `--mem-fraction-static 0.95` on GB300 x4. The model uses about 238.5GB/GPU after loading, so lower values can fail during KV-pool initialization.
* Use `--mem-fraction-static 0.8` on B200 x8.
* Use `--mem-fraction-static 0.95` on H200 x8.
* `--model-loader-extra-config '{"enable_multithread_load":"true","num_threads":64}'` is recommended because the model has 175 large safetensors shards.
* Keep `--tool-call-parser glm` enabled by default for OpenAI-compatible tool calls. Ring's template emits XML `<arg_key>/<arg_value>` tool calls, which the `qwen` parser does not convert into `message.tool_calls`.
* Keep `--reasoning-parser deepseek-r1` enabled by default so `<think>...</think>` content is split into `message.reasoning_content`.

## 4. Model Invocation

### 4.1 Basic Usage

For example, launch the server on a single 4-GPU GB300 node:

```bash Command theme={null}
export PORT=30000

sglang serve \
  --model-path inclusionAI/Ring-2.6-1T \
  --tp-size 4 \
  --trust-remote-code \
  --host 0.0.0.0 \
  --port ${PORT} \
  --mem-fraction-static 0.95 \
  --model-loader-extra-config '{"enable_multithread_load":"true","num_threads":64}' \
  --tool-call-parser glm \
  --reasoning-parser deepseek-r1
```

Send a basic chat request:

```bash Command theme={null}
curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "What is the capital of France?"}],
    "max_tokens": 128
  }'
```

### 4.2 Reasoning Effort

Ring-2.6-1T exposes two reasoning-effort levels in the model card: `high` and `xhigh`. In SGLang's OpenAI-compatible chat API, start with top-level `reasoning_effort: "high"` for agent and production workflows:

```bash Command theme={null}
curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "Solve: if 3x + 7 = 52, what is x?"}],
    "reasoning_effort": "high",
    "max_tokens": 512
}'
```

For the model-card `xhigh` path, pass the template value explicitly:

```bash Command theme={null}
curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "Solve: if 3x + 7 = 52, what is x?"}],
    "chat_template_kwargs": {"reasoning_effort": "xhigh"},
    "max_tokens": 512
  }'
```

With the default deployment command, thinking text is separated into `message.reasoning_content` when the model emits `<think>...</think>` blocks.

### 4.3 Tool Calling Example

```bash Command theme={null}
curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "What is the weather in Beijing?"}],
    "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"}
          },
          "required": ["location"]
        }
      }
    }],
    "tool_choice": "auto",
    "max_tokens": 512
  }'
```

For more API examples, see the [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request).

## 5. Benchmark

### 5.1 Speed Benchmark

* Hardware: NVIDIA B200 GPU (8x), NVIDIA H200 GPU (8x), and NVIDIA GB300 GPU (4x)
* Model: `inclusionAI/Ring-2.6-1T`
* Docker image: `lmsysorg/sglang:latest`
* SGLang version tested: `0.5.11`
* Tensor Parallelism: 8 on B200 x8 and H200 x8, 4 on GB300 x4

Use the deployment command from [Section 3](#3-model-deployment), then confirm that the server is healthy before running benchmarks:

```bash Command theme={null}
curl -s http://localhost:${PORT}/health
curl -s http://localhost:${PORT}/v1/models
```

#### 5.1.1 Latency-Sensitive Benchmark

* Test Command:

```bash Command theme={null}
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 \
  --port ${PORT} \
  --model inclusionAI/Ring-2.6-1T \
  --dataset-name random \
  --random-input-len 1024 \
  --random-output-len 1024 \
  --num-prompts 10 \
  --max-concurrency 1 \
  --request-rate inf
```

* Test Results (B200 x8):

```text Output theme={null}
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  207.18
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.05
Input token throughput (tok/s):          29.45
Output token throughput (tok/s):         20.37
Total token throughput (tok/s):          49.82
Mean E2E Latency (ms):                   20715.16
Mean TTFT (ms):                          187.86
Mean TPOT (ms):                          44.65
Mean ITL (ms):                           48.76
==================================================
```

* Test Results (GB300 x4):

```text Output theme={null}
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  62.21
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.16
Input token throughput (tok/s):          98.07
Output token throughput (tok/s):         67.83
Total token throughput (tok/s):          165.91
Mean E2E Latency (ms):                   6218.57
Mean TTFT (ms):                          233.04
Mean TPOT (ms):                          14.21
Mean ITL (ms):                           14.22
==================================================
```

* Test Results (H200 x8):

```text Output theme={null}
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  57.10
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.18
Input token throughput (tok/s):          106.85
Output token throughput (tok/s):         73.91
Total token throughput (tok/s):          180.76
Mean E2E Latency (ms):                   5707.72
Mean TTFT (ms):                          163.35
Mean TPOT (ms):                          13.17
Mean ITL (ms):                           13.17
==================================================
```

#### 5.1.2 Throughput-Sensitive Benchmark

* Test Command:

```bash Command theme={null}
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 \
  --port ${PORT} \
  --model inclusionAI/Ring-2.6-1T \
  --dataset-name random \
  --random-input-len 1024 \
  --random-output-len 1024 \
  --num-prompts 100 \
  --max-concurrency 100 \
  --request-rate inf
```

* Test Results (B200 x8):

```text Output theme={null}
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     100
Benchmark duration (s):                  46.30
Total input tokens:                      50561
Total generated tokens:                  52444
Request throughput (req/s):              2.16
Input token throughput (tok/s):          1092.10
Output token throughput (tok/s):         1132.77
Total token throughput (tok/s):          2224.86
Mean E2E Latency (ms):                   27581.74
Mean TTFT (ms):                          1710.53
Mean TPOT (ms):                          51.27
Mean ITL (ms):                           49.43
==================================================
```

* Test Results (GB300 x4):

```text Output theme={null}
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     100
Benchmark duration (s):                  55.80
Total input tokens:                      50561
Total generated tokens:                  52444
Request throughput (req/s):              1.79
Input token throughput (tok/s):          906.10
Output token throughput (tok/s):         939.84
Total token throughput (tok/s):          1845.94
Mean E2E Latency (ms):                   33736.85
Mean TTFT (ms):                          2156.40
Mean TPOT (ms):                          63.09
Mean ITL (ms):                           60.33
==================================================
```

* Test Results (H200 x8):

```text Output theme={null}
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     100
Benchmark duration (s):                  44.51
Total input tokens:                      50561
Total generated tokens:                  52444
Request throughput (req/s):              2.25
Input token throughput (tok/s):          1135.88
Output token throughput (tok/s):         1178.18
Total token throughput (tok/s):          2314.06
Mean E2E Latency (ms):                   27177.14
Mean TTFT (ms):                          2173.08
Mean TPOT (ms):                          51.11
Mean ITL (ms):                           47.77
==================================================
```

### 5.2 Accuracy Benchmark

#### 5.2.1 GSM8K Benchmark

* Benchmark Command:

```bash Command theme={null}
python3 -m sglang.test.run_eval \
  --eval-name gsm8k \
  --host 127.0.0.1 \
  --port ${PORT} \
  --model auto \
  --num-examples 200 \
  --num-threads 64 \
  --max-tokens 2048 \
  --reasoning-effort high
```

* Test Results (B200 x8):

```text Output theme={null}
Total latency: 100.378 s
Score: 0.990
Output throughput: 627.401 token/s
```

* Test Results (GB300 x4):

```text Output theme={null}
Total latency: 98.386 s
Score: 0.990
Output throughput: 621.469 token/s
```

* Test Results (H200 x8):

```text Output theme={null}
Total latency: 76.849 s
Score: 0.990
Output throughput: 793.125 token/s
```