915 lines
34 KiB
Plaintext
915 lines
34 KiB
Plaintext
---
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title: GLM-4.6
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metatags:
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description: "Deploy GLM-4.6 with SGLang - 200K context window, superior coding, advanced reasoning, and enhanced agentic capabilities."
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---
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## 1. Model Introduction
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[GLM-4.6](https://huggingface.co/zai-org/GLM-4.6) is a powerful language model developed by Zhipu AI, featuring advanced capabilities in reasoning, function calling, and multi-modal understanding.
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As the latest iteration in the GLM series, GLM-4.6 achieves comprehensive enhancements across multiple domains, including real-world coding, long-context processing, reasoning, searching, writing, and agentic applications. Details are as follows:
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- **Longer context window**: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.
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- **Superior coding performance**: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code, Cline, Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.
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- **Advanced reasoning**: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.
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- **More capable agents**: GLM-4.6 exhibits stronger performance in tool use and search-based agents, and integrates more effectively within agent frameworks.
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- **Refined writing**: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.
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For more details, please refer to the [official GLM-4.6 documentation](https://docs.z.ai/guides/llm/glm-4.6).
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## 2. SGLang Installation
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SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.
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Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
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## 3. Model Deployment
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This section provides deployment configurations optimized for different hardware platforms and use cases.
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### 3.1 Basic Configuration
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**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, quantization method, deployment strategy, and thinking capabilities.
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import { GLM46Deployment } from "/src/snippets/autoregressive/glm-46-deployment.jsx";
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<GLM46Deployment />
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### 3.2 Configuration Tips
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- **EAGLE Speculative Decoding:** Supported for GLM-4.5/4.6. Add `--speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4`. The spec-v2 overlap scheduler is enabled by default; pass `--disable-overlap-schedule` to disable.
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- **Thinking Budget:** Use `--enable-custom-logit-processor` flag and pass `Glm4MoeThinkingBudgetLogitProcessor` in requests to cap the model's thinking token count (see section 4.2.3).
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## 4. Model Invocation
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### 4.1 Basic Usage
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For basic API usage and request examples, please refer to:
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- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)
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### 4.2 Advanced Usage
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#### 4.2.1 Reasoning Parser
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GLM-4.6 supports Thinking mode by default. Enable the reasoning parser during deployment to separate the thinking and the content sections:
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```shell Command
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python -m sglang.launch_server \
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--model zai-org/GLM-4.6 \
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--reasoning-parser glm45 \
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--tp 8 \
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--host 0.0.0.0 \
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--port 8000
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```
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**Streaming with Thinking Process:**
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```python Example
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8000/v1",
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api_key="EMPTY"
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)
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# Enable streaming to see the thinking process in real-time
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response = client.chat.completions.create(
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model="zai-org/GLM-4.6",
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messages=[
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{"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
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],
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temperature=0.7,
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max_tokens=2048,
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stream=True
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)
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# Process the stream
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has_thinking = False
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has_answer = False
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thinking_started = False
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for chunk in response:
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if chunk.choices and len(chunk.choices) > 0:
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delta = chunk.choices[0].delta
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# Print thinking process
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
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if not thinking_started:
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print("=============== Thinking =================", flush=True)
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thinking_started = True
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has_thinking = True
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print(delta.reasoning_content, end="", flush=True)
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# Print answer content
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if delta.content:
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# Close thinking section and add content header
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if has_thinking and not has_answer:
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print("\n=============== Content =================", flush=True)
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has_answer = True
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print(delta.content, end="", flush=True)
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print()
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```
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**Output Example:**
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```text Output
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=============== Thinking =================
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To solve this problem, I need to calculate 15% of 240.
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Step 1: Convert 15% to decimal: 15% = 0.15
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Step 2: Multiply 240 by 0.15
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Step 3: 240 × 0.15 = 36
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=============== Content =================
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The answer is 36. To find 15% of 240, we multiply 240 by 0.15, which equals 36.
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```
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**Note:** The reasoning parser captures the model's step-by-step thinking process, allowing you to see how the model arrives at its conclusions.
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#### 4.2.2 Tool Calling
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<Note>
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**Parser names by model:** GLM-4.5 and GLM-4.6 use `--tool-call-parser glm45`. GLM-4.7 and GLM-4.7-Flash use `--tool-call-parser glm47`. All GLM models use `--reasoning-parser glm45` regardless of generation.
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</Note>
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GLM-4.6 supports tool calling capabilities. Enable the tool call parser:
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```shell Command
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python -m sglang.launch_server \
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--model zai-org/GLM-4.6 \
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--reasoning-parser glm45 \
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--tool-call-parser glm45 \
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--tp 8 \
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--host 0.0.0.0 \
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--port 8000
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```
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**Python Example (with Thinking Process):**
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```python Example
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8000/v1",
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api_key="EMPTY"
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)
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# Define available tools
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Get the current weather for a location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city name"
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "Temperature unit"
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}
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},
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"required": ["location"]
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}
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}
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}
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]
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# Make request with streaming to see thinking process
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response = client.chat.completions.create(
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model="zai-org/GLM-4.6",
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messages=[
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{"role": "user", "content": "What's the weather in Beijing?"}
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],
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tools=tools,
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temperature=0.7,
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stream=True
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)
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# Process streaming response
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thinking_started = False
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has_thinking = False
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for chunk in response:
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if chunk.choices and len(chunk.choices) > 0:
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delta = chunk.choices[0].delta
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# Print thinking process
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
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if not thinking_started:
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print("=============== Thinking =================", flush=True)
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thinking_started = True
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has_thinking = True
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print(delta.reasoning_content, end="", flush=True)
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# Print tool calls
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if hasattr(delta, 'tool_calls') and delta.tool_calls:
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# Close thinking section if needed
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if has_thinking and thinking_started:
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print("\n=============== Content =================", flush=True)
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thinking_started = False
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for tool_call in delta.tool_calls:
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if tool_call.function:
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print(f"🔧 Tool Call: {tool_call.function.name}")
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print(f" Arguments: {tool_call.function.arguments}")
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# Print content
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if delta.content:
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print(delta.content, end="", flush=True)
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print()
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```
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**Output Example:**
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```text Output
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=============== Thinking =================
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The user is asking about the weather in Beijing. I need to use the get_weather function to retrieve this information.
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I should call the function with location="Beijing".
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=============== Content =================
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🔧 Tool Call: get_weather
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Arguments: {"location": "Beijing", "unit": "celsius"}
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```
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**Note:**
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- The reasoning parser shows how the model decides to use a tool
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- Tool calls are clearly marked with the function name and arguments
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- You can then execute the function and send the result back to continue the conversation
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**Handling Tool Call Results:**
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```python Example
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# After getting the tool call, execute the function
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def get_weather(location, unit="celsius"):
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# Your actual weather API call here
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return f"The weather in {location} is 22°{unit[0].upper()} and sunny."
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# Send tool result back to the model
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messages = [
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{"role": "user", "content": "What's the weather in Beijing?"},
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{
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": "call_123",
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"type": "function",
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"function": {
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"name": "get_weather",
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"arguments": '{"location": "Beijing", "unit": "celsius"}'
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}
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}]
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},
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{
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"role": "tool",
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"tool_call_id": "call_123",
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"content": get_weather("Beijing", "celsius")
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}
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]
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final_response = client.chat.completions.create(
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model="zai-org/GLM-4.6",
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messages=messages,
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temperature=0.7
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)
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print(final_response.choices[0].message.content)
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# Output: "The weather in Beijing is currently 22°C and sunny."
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```
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#### 4.2.3 Thinking Budget
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Limit the number of thinking tokens using `CustomLogitProcessor`. Launch with `--enable-custom-logit-processor`:
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```python Example
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import openai
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from sglang.srt.sampling.custom_logit_processor import Glm4MoeThinkingBudgetLogitProcessor
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client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="*")
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response = client.chat.completions.create(
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model="zai-org/GLM-4.6",
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messages=[{"role": "user", "content": "Is Paris the Capital of France?"}],
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max_tokens=1024,
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extra_body={
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"custom_logit_processor": Glm4MoeThinkingBudgetLogitProcessor().to_str(),
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"custom_params": {"thinking_budget": 512},
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},
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)
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print(response)
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```
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## 5. Benchmark
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This section uses **industry-standard configurations** for comparable benchmark results.
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### 5.1 Speed Benchmark
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**Test Environment:**
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- Hardware: NVIDIA B200 GPU (8x), AMD MI300X (8x), AMD MI325X (8x), AMD MI355X (8x)
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- Model: GLM-4.6
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- Tensor Parallelism: 8
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- SGLang Version: 0.5.6.post1
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**Benchmark Methodology:**
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We use industry-standard benchmark configurations to ensure results are comparable across frameworks and hardware platforms.
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#### 5.1.1 Standard Test Scenarios
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Three core scenarios reflect real-world usage patterns:
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<colgroup>
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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</colgroup>
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<thead>
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<tr style={{borderBottom: "2px solid #d55816"}}>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Scenario</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Input Length</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Output Length</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Use Case</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Chat**</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1K</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1K</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Most common conversational AI workload</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Reasoning**</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1K</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>8K</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Long-form generation, complex reasoning tasks</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Summarization**</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>8K</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1K</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Document summarization, RAG retrieval</td>
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</tr>
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</tbody>
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</table>
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#### 5.1.2 Concurrency Levels
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Test each scenario at three concurrency levels to capture the throughput vs. latency tradeoff (Pareto frontier):
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- **Low Concurrency**: `--max-concurrency 1` (Latency-optimized)
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- **Medium Concurrency**: `--max-concurrency 16` (Balanced)
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- **High Concurrency**: `--max-concurrency 100` (Throughput-optimized)
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#### 5.1.3 Number of Prompts
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For each concurrency level, configure `num_prompts` to simulate realistic user loads:
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- **Quick Test**: `num_prompts = concurrency × 1` (minimal test)
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- **Recommended**: `num_prompts = concurrency × 5` (standard benchmark)
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- **Stable Measurements**: `num_prompts = concurrency × 10` (production-grade)
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---
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#### 5.1.4 Benchmark Commands
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**Scenario 1: Chat (1K/1K) - Most Important**
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- **Model Deployment**
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```bash Command
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python -m sglang.launch_server \
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--model zai-org/GLM-4.6 \
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--tp 8
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```
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- Low Concurrency (Latency-Optimized)
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```bash Command
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python -m sglang.bench_serving \
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--backend sglang \
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--model zai-org/GLM-4.6 \
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--dataset-name random \
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--random-input-len 1000 \
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--random-output-len 1000 \
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--num-prompts 10 \
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--max-concurrency 1 \
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--request-rate inf
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```
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```text Output
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============ Serving Benchmark Result ============
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Backend: sglang
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Traffic request rate: inf
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Max request concurrency: 1
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Successful requests: 10
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Benchmark duration (s): 63.82
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Total input tokens: 6101
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Total input text tokens: 6101
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Total input vision tokens: 0
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Total generated tokens: 4210
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Total generated tokens (retokenized): 4209
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Request throughput (req/s): 0.16
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Input token throughput (tok/s): 95.60
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Output token throughput (tok/s): 65.97
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Peak output token throughput (tok/s): 68.00
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Peak concurrent requests: 2
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Total token throughput (tok/s): 161.57
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Concurrency: 1.00
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----------------End-to-End Latency----------------
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Mean E2E Latency (ms): 6379.24
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Median E2E Latency (ms): 5085.00
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---------------Time to First Token----------------
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Mean TTFT (ms): 155.57
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Median TTFT (ms): 149.79
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P99 TTFT (ms): 207.69
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-----Time per Output Token (excl. 1st token)------
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Mean TPOT (ms): 14.81
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Median TPOT (ms): 14.80
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P99 TPOT (ms): 14.84
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---------------Inter-Token Latency----------------
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Mean ITL (ms): 14.82
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Median ITL (ms): 14.82
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P95 ITL (ms): 15.17
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P99 ITL (ms): 15.36
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Max ITL (ms): 25.05
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==================================================
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```
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- Medium Concurrency (Balanced)
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```bash Command
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python -m sglang.bench_serving \
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--backend sglang \
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--model zai-org/GLM-4.6 \
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--dataset-name random \
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--random-input-len 1000 \
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--random-output-len 1000 \
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--num-prompts 80 \
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--max-concurrency 16 \
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--request-rate inf
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```
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```text Output
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============ Serving Benchmark Result ============
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Backend: sglang
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Traffic request rate: inf
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Max request concurrency: 16
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Successful requests: 80
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Benchmark duration (s): 72.06
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Total input tokens: 39668
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Total input text tokens: 39668
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Total input vision tokens: 0
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Total generated tokens: 40725
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Total generated tokens (retokenized): 40672
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Request throughput (req/s): 1.11
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Input token throughput (tok/s): 550.47
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Output token throughput (tok/s): 565.14
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Peak output token throughput (tok/s): 752.00
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Peak concurrent requests: 20
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Total token throughput (tok/s): 1115.61
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Concurrency: 13.71
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----------------End-to-End Latency----------------
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Mean E2E Latency (ms): 12348.93
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Median E2E Latency (ms): 13164.81
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---------------Time to First Token----------------
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Mean TTFT (ms): 196.08
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Median TTFT (ms): 155.22
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P99 TTFT (ms): 377.98
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 24.24
|
||
Median TPOT (ms): 24.55
|
||
P99 TPOT (ms): 30.42
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 23.92
|
||
Median ITL (ms): 21.40
|
||
P95 ITL (ms): 22.49
|
||
P99 ITL (ms): 123.83
|
||
Max ITL (ms): 486.54
|
||
==================================================
|
||
```
|
||
|
||
|
||
- High Concurrency (Throughput-Optimized)
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.6 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 500 \
|
||
--max-concurrency 100 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Traffic request rate: inf
|
||
Max request concurrency: 100
|
||
Successful requests: 500
|
||
Benchmark duration (s): 138.50
|
||
Total input tokens: 249831
|
||
Total input text tokens: 249831
|
||
Total input vision tokens: 0
|
||
Total generated tokens: 252162
|
||
Total generated tokens (retokenized): 251841
|
||
Request throughput (req/s): 3.61
|
||
Input token throughput (tok/s): 1803.78
|
||
Output token throughput (tok/s): 1820.61
|
||
Peak output token throughput (tok/s): 2900.00
|
||
Peak concurrent requests: 107
|
||
Total token throughput (tok/s): 3624.40
|
||
Concurrency: 90.91
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 25183.97
|
||
Median E2E Latency (ms): 23968.49
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 337.77
|
||
Median TTFT (ms): 180.65
|
||
P99 TTFT (ms): 906.14
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 49.97
|
||
Median TPOT (ms): 52.20
|
||
P99 TPOT (ms): 61.81
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 49.36
|
||
Median ITL (ms): 35.05
|
||
P95 ITL (ms): 124.91
|
||
P99 ITL (ms): 187.69
|
||
Max ITL (ms): 440.34
|
||
==================================================
|
||
```
|
||
|
||
**Scenario 2: Reasoning (1K/8K)**
|
||
|
||
- Low Concurrency
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.6 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 8000 \
|
||
--num-prompts 10 \
|
||
--max-concurrency 1 \
|
||
--request-rate inf
|
||
```
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Traffic request rate: inf
|
||
Max request concurrency: 1
|
||
Successful requests: 10
|
||
Benchmark duration (s): 666.64
|
||
Total input tokens: 6101
|
||
Total input text tokens: 6101
|
||
Total input vision tokens: 0
|
||
Total generated tokens: 44452
|
||
Total generated tokens (retokenized): 44387
|
||
Request throughput (req/s): 0.02
|
||
Input token throughput (tok/s): 9.15
|
||
Output token throughput (tok/s): 66.68
|
||
Peak output token throughput (tok/s): 68.00
|
||
Peak concurrent requests: 2
|
||
Total token throughput (tok/s): 75.83
|
||
Concurrency: 1.00
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 66661.35
|
||
Median E2E Latency (ms): 71902.36
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 160.21
|
||
Median TTFT (ms): 140.32
|
||
P99 TTFT (ms): 295.56
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 14.92
|
||
Median TPOT (ms): 14.94
|
||
P99 TPOT (ms): 15.02
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 14.96
|
||
Median ITL (ms): 14.96
|
||
P95 ITL (ms): 15.36
|
||
P99 ITL (ms): 15.57
|
||
Max ITL (ms): 19.06
|
||
==================================================
|
||
```
|
||
|
||
- Medium Concurrency
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.6 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 8000 \
|
||
--num-prompts 80 \
|
||
--max-concurrency 16 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Traffic request rate: inf
|
||
Max request concurrency: 16
|
||
Successful requests: 80
|
||
Benchmark duration (s): 503.30
|
||
Total input tokens: 39668
|
||
Total input text tokens: 39668
|
||
Total input vision tokens: 0
|
||
Total generated tokens: 318226
|
||
Total generated tokens (retokenized): 318025
|
||
Request throughput (req/s): 0.16
|
||
Input token throughput (tok/s): 78.82
|
||
Output token throughput (tok/s): 632.28
|
||
Peak output token throughput (tok/s): 752.00
|
||
Peak concurrent requests: 19
|
||
Total token throughput (tok/s): 711.09
|
||
Concurrency: 13.88
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 87349.22
|
||
Median E2E Latency (ms): 88248.04
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 228.54
|
||
Median TTFT (ms): 142.78
|
||
P99 TTFT (ms): 569.84
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 21.97
|
||
Median TPOT (ms): 22.14
|
||
P99 TPOT (ms): 22.47
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 21.91
|
||
Median ITL (ms): 21.80
|
||
P95 ITL (ms): 22.30
|
||
P99 ITL (ms): 22.78
|
||
Max ITL (ms): 137.19
|
||
==================================================
|
||
```
|
||
|
||
- High Concurrency
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.6 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 8000 \
|
||
--num-prompts 320 \
|
||
--max-concurrency 64 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Traffic request rate: inf
|
||
Max request concurrency: 64
|
||
Successful requests: 320
|
||
Benchmark duration (s): 772.28
|
||
Total input tokens: 158939
|
||
Total input text tokens: 158939
|
||
Total input vision tokens: 0
|
||
Total generated tokens: 1300705
|
||
Total generated tokens (retokenized): 1299924
|
||
Request throughput (req/s): 0.41
|
||
Input token throughput (tok/s): 205.80
|
||
Output token throughput (tok/s): 1684.24
|
||
Peak output token throughput (tok/s): 2112.00
|
||
Peak concurrent requests: 68
|
||
Total token throughput (tok/s): 1890.05
|
||
Concurrency: 56.17
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 135563.36
|
||
Median E2E Latency (ms): 140888.88
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 232.45
|
||
Median TTFT (ms): 145.59
|
||
P99 TTFT (ms): 576.49
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 33.47
|
||
Median TPOT (ms): 34.02
|
||
P99 TPOT (ms): 35.10
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 33.30
|
||
Median ITL (ms): 32.63
|
||
P95 ITL (ms): 34.27
|
||
P99 ITL (ms): 104.39
|
||
Max ITL (ms): 155.65
|
||
==================================================
|
||
```
|
||
|
||
**Scenario 3: Summarization (8K/1K)**
|
||
|
||
- Low
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.6 \
|
||
--dataset-name random \
|
||
--random-input-len 8000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 10 \
|
||
--max-concurrency 1 \
|
||
--request-rate inf
|
||
```
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Traffic request rate: inf
|
||
Max request concurrency: 1
|
||
Successful requests: 10
|
||
Benchmark duration (s): 65.11
|
||
Total input tokens: 41941
|
||
Total input text tokens: 41941
|
||
Total input vision tokens: 0
|
||
Total generated tokens: 4210
|
||
Total generated tokens (retokenized): 4210
|
||
Request throughput (req/s): 0.15
|
||
Input token throughput (tok/s): 644.17
|
||
Output token throughput (tok/s): 64.66
|
||
Peak output token throughput (tok/s): 68.00
|
||
Peak concurrent requests: 2
|
||
Total token throughput (tok/s): 708.83
|
||
Concurrency: 1.00
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 6508.31
|
||
Median E2E Latency (ms): 5263.36
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 189.48
|
||
Median TTFT (ms): 159.23
|
||
P99 TTFT (ms): 304.09
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 15.02
|
||
Median TPOT (ms): 15.03
|
||
P99 TPOT (ms): 15.27
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 15.04
|
||
Median ITL (ms): 15.03
|
||
P95 ITL (ms): 15.46
|
||
P99 ITL (ms): 15.65
|
||
Max ITL (ms): 24.20
|
||
==================================================
|
||
```
|
||
|
||
- Medium Concurrency
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.6 \
|
||
--dataset-name random \
|
||
--random-input-len 8000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 80 \
|
||
--max-concurrency 16 \
|
||
--request-rate inf
|
||
```
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Traffic request rate: inf
|
||
Max request concurrency: 16
|
||
Successful requests: 80
|
||
Benchmark duration (s): 76.43
|
||
Total input tokens: 300020
|
||
Total input text tokens: 300020
|
||
Total input vision tokens: 0
|
||
Total generated tokens: 41589
|
||
Total generated tokens (retokenized): 41577
|
||
Request throughput (req/s): 1.05
|
||
Input token throughput (tok/s): 3925.47
|
||
Output token throughput (tok/s): 544.15
|
||
Peak output token throughput (tok/s): 752.00
|
||
Peak concurrent requests: 19
|
||
Total token throughput (tok/s): 4469.62
|
||
Concurrency: 13.95
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 13329.63
|
||
Median E2E Latency (ms): 14141.09
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 339.88
|
||
Median TTFT (ms): 252.75
|
||
P99 TTFT (ms): 906.54
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 25.37
|
||
Median TPOT (ms): 25.73
|
||
P99 TPOT (ms): 30.94
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 25.04
|
||
Median ITL (ms): 21.68
|
||
P95 ITL (ms): 22.69
|
||
P99 ITL (ms): 146.98
|
||
Max ITL (ms): 483.14
|
||
==================================================
|
||
```
|
||
|
||
|
||
- High Concurrency
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.6 \
|
||
--dataset-name random \
|
||
--random-input-len 8000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 320 \
|
||
--max-concurrency 64 \
|
||
--request-rate inf
|
||
```
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Traffic request rate: inf
|
||
Max request concurrency: 64
|
||
Successful requests: 320
|
||
Benchmark duration (s): 136.24
|
||
Total input tokens: 1273893
|
||
Total input text tokens: 1273893
|
||
Total input vision tokens: 0
|
||
Total generated tokens: 169680
|
||
Total generated tokens (retokenized): 169452
|
||
Request throughput (req/s): 2.35
|
||
Input token throughput (tok/s): 9350.32
|
||
Output token throughput (tok/s): 1245.44
|
||
Peak output token throughput (tok/s): 1984.00
|
||
Peak concurrent requests: 69
|
||
Total token throughput (tok/s): 10595.77
|
||
Concurrency: 58.46
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 24889.40
|
||
Median E2E Latency (ms): 25123.37
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 355.82
|
||
Median TTFT (ms): 268.84
|
||
P99 TTFT (ms): 858.64
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 46.62
|
||
Median TPOT (ms): 49.04
|
||
P99 TPOT (ms): 58.88
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 46.36
|
||
Median ITL (ms): 32.46
|
||
P95 ITL (ms): 135.23
|
||
P99 ITL (ms): 204.27
|
||
Max ITL (ms): 508.14
|
||
==================================================
|
||
```
|
||
|
||
#### 5.1.5 Understanding the Results
|
||
|
||
**Key Metrics:**
|
||
|
||
- **Request Throughput (req/s)**: Number of requests processed per second
|
||
- **Output Token Throughput (tok/s)**: Total tokens generated per second
|
||
- **Mean TTFT (ms)**: Time to First Token - measures responsiveness
|
||
- **Mean TPOT (ms)**: Time Per Output Token - measures generation speed
|
||
- **Mean ITL (ms)**: Inter-Token Latency - measures streaming consistency
|
||
|
||
**Why These Configurations Matter:**
|
||
|
||
- **1K/1K (Chat)**: Represents the most common conversational AI workload. This is the highest priority scenario for most deployments.
|
||
- **1K/8K (Reasoning)**: Tests long-form generation capabilities crucial for complex reasoning, code generation, and detailed explanations.
|
||
- **8K/1K (Summarization)**: Evaluates performance with large context inputs, essential for RAG systems, document Q&A, and summarization tasks.
|
||
- **Variable Concurrency**: Captures the Pareto frontier - the optimal tradeoff between throughput and latency at different load levels. Low concurrency shows best-case latency, high concurrency shows maximum throughput.
|
||
|
||
**Interpreting Results:**
|
||
|
||
- Compare your results against baseline numbers for your hardware
|
||
- Higher throughput at same latency = better performance
|
||
- Lower TTFT = more responsive user experience
|
||
- Lower TPOT = faster generation speed
|
||
|
||
### 5.2 Accuracy Benchmark
|
||
|
||
Document model accuracy on standard benchmarks:
|
||
|
||
#### 5.2.1 GSM8K Benchmark
|
||
|
||
- Benchmark Command
|
||
```bash Command
|
||
python -m sglang.test.few_shot_gsm8k \
|
||
--num-questions 200 \
|
||
--port 30000
|
||
```
|
||
|
||
- Test Result
|
||
```text Output
|
||
Accuracy: 0.975
|
||
Invalid: 0.000
|
||
Latency: 16.574 s
|
||
Output throughput: 1194.637 token/s
|
||
```
|