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1. Model Introduction

Ring-2.5-1T is the world’s first open-source trillion-parameter reasoning model based on hybrid linear attention architecture, developed by InclusionAI. Building on Ring-1T, Ring-2.5-1T demonstrates substantial improvements in generation efficiency, reasoning depth, and long-horizon task execution capabilities. Key Features:
  • Trillion-Scale Model: ~1T total parameters with 63B activation parameters using a hybrid linear attention architecture (1:7 MLA + Lightning Linear Attention)
  • Generation Efficiency: Reduces memory access overhead by over 10x and increases generation throughput by more than 3x for sequences exceeding 32K tokens
  • Deep Reasoning: Achieves gold medal level for both IMO 2025 and CMO 2025, with dense rewards for rigorous reasoning process feedback
  • Long-horizon Task Execution: Enhanced autonomous execution capability through large-scale fully-async agentic RL training
  • Tool Calling: Supports function calling with XML-style tool call format
  • Context Length: 128K -> 256K (YaRN)
Available Models: License: MIT

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2. SGLang Installation

Ring-2.5-1T requires a specific SGLang Docker image: 
# For H200/B200
docker pull lmsysorg/sglang:nightly-dev-20260213-a0ebaa64

# For GB200/GB300
docker pull lmsysorg/sglang:nightly-dev-cu13-20260213-a0ebaa64
For other installation methods, please refer to the official SGLang installation guide.

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3. Model Deployment

This section provides deployment configurations optimized for different hardware platforms.

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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.

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3.2 Configuration Tips

  • The --trust-remote-code flag is required for this model due to custom modeling code.
  • The model uses FP8 quantization (compressed-tensors format).

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4. Model Invocation

Deploy Ring-2.5-1T with the following command (on H200, all features enabled): 
python -m sglang.launch_server \
  --model-path inclusionAI/Ring-2.5-1T \
  --tp 8 \
  --trust-remote-code \
  --host 0.0.0.0 \
  --port 30000

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4.1 Basic Usage

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

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4.2 Advanced Usage

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4.2.1 Reasoning Parser

To enable reasoning output separation, add --reasoning-parser deepseek-r1 when launching the server. The thinking process is returned via reasoning_content in the streaming response. 
python -m sglang.launch_server \
  --model-path inclusionAI/Ring-2.5-1T \
  --tp 8 \
  --trust-remote-code \
  --reasoning-parser deepseek-r1 \
  --host 0.0.0.0 \
  --port 30000

from openai import OpenAI

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

response = client.chat.completions.create(
    model="inclusionAI/Ring-2.5-1T",
    messages=[
        {"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
    ],
    max_tokens=2048,
    stream=True
)

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:
            print(delta.reasoning_content, end="", flush=True)

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

print()

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4.2.2 Tool Calling

To enable tool calling, add --tool-call-parser qwen when launching the server. 
python -m sglang.launch_server \
  --model-path inclusionAI/Ring-2.5-1T \
  --tp 8 \
  --trust-remote-code \
  --tool-call-parser qwen \
  --host 0.0.0.0 \
  --port 30000

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"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="inclusionAI/Ring-2.5-1T",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing?"}
    ],
    tools=tools
)

print(response.choices[0].message.tool_calls)
Output Example: 
[ChatCompletionMessageFunctionToolCall(id='call_770360e31d194ed79d32cd8c', function=Function(arguments='{"location": "Beijing"}', name='get_weather'), type='function', index=0)]

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5. Benchmark

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GSM8K

  • Deployment Command
python3 -m sglang.launch_server \
  --model-path inclusionAI/Ring-2.5-1T \
  --tp-size 8 \
  --trust-remote-code
  • Benchmark Command
python3 benchmark/gsm8k/bench_sglang.py --temperature 1.2 --top-p 0.8 --max-new-tokens 32768 --num-questions 200 --tokenizer-path inclusionAI/Ring-2.5-1T --enable-thinking
  • Test Result
Accuracy: 0.955
Invalid: 0.010
Latency: 615.833 s
Output throughput: 412.360 token/s