[Docs] Rename docs_new/ to docs/ (#32123)
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
zijiexia
co-authored by
Claude Opus 4.8
Claude Opus 4.8
parent
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commit
b819d2fb5b
@@ -0,0 +1,829 @@
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---
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title: GLM Glyph
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metatags:
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description: "Deploy GLM-Glyph with SGLang - community contribution guide for Zhipu AI's GLM Glyph model deployment."
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---
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import { GLMGlyphDeployment } from '/src/snippets/autoregressive/glm-glyph-deployment.jsx';
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## 1. Model Introduction
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[Glyph](https://huggingface.co/zai-org/Glyph) is a powerful language model developed by Zhipu AI, featuring advanced capabilities in reasoning, function calling, and multi-modal understanding.
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**Hardware Support:** NVIDIA B200/H100/H200, AMD MI300X/MI325X/MI355X
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**Key Features:**
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- **Advanced Reasoning**: Built-in reasoning capabilities for complex problem-solving
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- **Multiple Quantizations**: BF16 and FP8 variants for different performance/memory trade-offs
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- **High Performance**: Optimized for both throughput and latency scenarios
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**Available Models:**
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- **BF16 (Full precision)**: [zai-org/Glyph](https://huggingface.co/zai-org/Glyph)
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- **FP8 (8-bit quantized)**: [zai-org/Glyph-FP8](https://huggingface.co/zai-org/Glyph-FP8)
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**License:**
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Please refer to the [official Glyph model card](https://huggingface.co/zai-org/Glyph) for license details.
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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, and other options.
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<GLMGlyphDeployment />
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### 3.2 Configuration Tips
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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 the [GLM-4.5 cookbook page](/cookbook/autoregressive/GLM/GLM-4.5) for the full Thinking Budget usage example.
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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 Thinking Mode
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Glyph supports thinking mode for enhanced reasoning. Enable the reasoning parser during deployment to separate the thinking and content sections:
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```shell Command
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python -m sglang.launch_server \
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--model-path zai-org/Glyph \
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--reasoning-parser glm45 \
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--tp 4
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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:30000/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/Glyph",
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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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**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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**Disable Thinking Mode:**
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To disable thinking mode for a specific request:
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```python Example
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response = client.chat.completions.create(
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model="zai-org/Glyph",
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messages=[{"role": "user", "content": "What is the capital of France?"}],
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extra_body={"chat_template_kwargs": {"enable_thinking": False}}
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)
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```
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#### 4.2.2 Tool Calling
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Glyph 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-path zai-org/Glyph \
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--reasoning-parser glm45 \
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--tool-call-parser glm45 \
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--tp 4
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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:30000/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/Glyph",
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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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tool_calls_accumulator = {}
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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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# Accumulate 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 =================\n", flush=True)
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thinking_started = False
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for tool_call in delta.tool_calls:
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index = tool_call.index
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if index not in tool_calls_accumulator:
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tool_calls_accumulator[index] = {
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'name': None,
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'arguments': ''
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}
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if tool_call.function:
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if tool_call.function.name:
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tool_calls_accumulator[index]['name'] = tool_call.function.name
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if tool_call.function.arguments:
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tool_calls_accumulator[index]['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 accumulated tool calls
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for index, tool_call in sorted(tool_calls_accumulator.items()):
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print(f"Tool Call: {tool_call['name']}")
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print(f" Arguments: {tool_call['arguments']}")
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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/Glyph",
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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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## 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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- Model: Glyph
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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 Scenario Benchmark
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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/Glyph \
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--tp 2
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```
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##### 5.1.1.1 Low Concurrency
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- **Benchmark Command**:
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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/Glyph \
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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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- **Test Results**:
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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): 17.03
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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: 4220
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Total generated tokens (retokenized): 4220
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Request throughput (req/s): 0.59
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Input token throughput (tok/s): 358.17
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Output token throughput (tok/s): 247.74
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Peak output token throughput (tok/s): 251.00
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Peak concurrent requests: 3
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Total token throughput (tok/s): 605.91
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Concurrency: 1.00
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----------------End-to-End Latency----------------
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Mean E2E Latency (ms): 1702.14
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Median E2E Latency (ms): 1361.72
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---------------Time to First Token----------------
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Mean TTFT (ms): 22.35
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Median TTFT (ms): 22.61
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P99 TTFT (ms): 23.76
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-----Time per Output Token (excl. 1st token)------
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Mean TPOT (ms): 3.99
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Median TPOT (ms): 3.99
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P99 TPOT (ms): 4.01
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---------------Inter-Token Latency----------------
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Mean ITL (ms): 3.99
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Median ITL (ms): 3.99
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P95 ITL (ms): 4.03
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P99 ITL (ms): 4.12
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Max ITL (ms): 7.46
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==================================================
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```
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##### 5.1.1.2 Medium Concurrency
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- **Benchmark Command**:
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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/Glyph \
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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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- **Test Results**:
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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): 16.27
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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: 40805
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Total generated tokens (retokenized): 40804
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Request throughput (req/s): 4.92
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Input token throughput (tok/s): 2438.06
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Output token throughput (tok/s): 2507.94
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Peak output token throughput (tok/s): 3069.00
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Peak concurrent requests: 26
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Total token throughput (tok/s): 4946.00
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Concurrency: 13.44
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----------------End-to-End Latency----------------
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Mean E2E Latency (ms): 2733.43
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Median E2E Latency (ms): 2892.98
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---------------Time to First Token----------------
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Mean TTFT (ms): 33.10
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Median TTFT (ms): 27.73
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P99 TTFT (ms): 49.34
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-----Time per Output Token (excl. 1st token)------
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Mean TPOT (ms): 5.33
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Median TPOT (ms): 5.39
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P99 TPOT (ms): 5.86
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---------------Inter-Token Latency----------------
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Mean ITL (ms): 5.30
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Median ITL (ms): 4.89
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P95 ITL (ms): 5.54
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P99 ITL (ms): 21.17
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Max ITL (ms): 25.14
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==================================================
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```
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##### 5.1.1.3 High Concurrency
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- **Benchmark Command**:
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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/Glyph \
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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 500 \
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--max-concurrency 100 \
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--request-rate inf
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```
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- **Test Results**:
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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: 100
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Successful requests: 500
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Benchmark duration (s): 25.67
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Total input tokens: 249831
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Total input text tokens: 249831
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Total input vision tokens: 0
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Total generated tokens: 252662
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Total generated tokens (retokenized): 252657
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Request throughput (req/s): 19.48
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Input token throughput (tok/s): 9733.69
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Output token throughput (tok/s): 9843.99
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Peak output token throughput (tok/s): 13398.00
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Peak concurrent requests: 127
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Total token throughput (tok/s): 19577.68
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Concurrency: 89.49
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----------------End-to-End Latency----------------
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Mean E2E Latency (ms): 4593.75
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Median E2E Latency (ms): 4431.03
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---------------Time to First Token----------------
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Mean TTFT (ms): 48.66
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Median TTFT (ms): 35.88
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P99 TTFT (ms): 120.61
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-----Time per Output Token (excl. 1st token)------
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Mean TPOT (ms): 9.10
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Median TPOT (ms): 9.55
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P99 TPOT (ms): 11.00
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---------------Inter-Token Latency----------------
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Mean ITL (ms): 9.01
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Median ITL (ms): 6.51
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P95 ITL (ms): 23.19
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P99 ITL (ms): 25.54
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Max ITL (ms): 52.93
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==================================================
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```
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#### 5.1.2 Reasoning Scenario Benchmark
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##### 5.1.2.1 Low Concurrency
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- **Benchmark Command**:
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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/Glyph \
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--dataset-name random \
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--random-input-len 1000 \
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--random-output-len 8000 \
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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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- **Test Results**:
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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
|
||||
Successful requests: 10
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Benchmark duration (s): 201.53
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Total input tokens: 6101
|
||||
Total input text tokens: 6101
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 44462
|
||||
Total generated tokens (retokenized): 44455
|
||||
Request throughput (req/s): 0.05
|
||||
Input token throughput (tok/s): 30.27
|
||||
Output token throughput (tok/s): 220.63
|
||||
Peak output token throughput (tok/s): 251.00
|
||||
Peak concurrent requests: 2
|
||||
Total token throughput (tok/s): 250.90
|
||||
Concurrency: 1.00
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 20151.45
|
||||
Median E2E Latency (ms): 21576.31
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 2362.23
|
||||
Median TTFT (ms): 23.03
|
||||
P99 TTFT (ms): 21310.14
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 4.00
|
||||
Median TPOT (ms): 4.00
|
||||
P99 TPOT (ms): 4.01
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 4.00
|
||||
Median ITL (ms): 4.00
|
||||
P95 ITL (ms): 4.05
|
||||
P99 ITL (ms): 4.08
|
||||
Max ITL (ms): 5.67
|
||||
==================================================
|
||||
```
|
||||
|
||||
##### 5.1.2.2 Medium Concurrency
|
||||
- **Benchmark Command**:
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model zai-org/Glyph \
|
||||
--dataset-name random \
|
||||
--random-input-len 1000 \
|
||||
--random-output-len 8000 \
|
||||
--num-prompts 80 \
|
||||
--max-concurrency 16 \
|
||||
--request-rate inf
|
||||
```
|
||||
- **Test Results**:
|
||||
```text Output
|
||||
============ Serving Benchmark Result ============
|
||||
Backend: sglang
|
||||
Traffic request rate: inf
|
||||
Max request concurrency: 16
|
||||
Successful requests: 80
|
||||
Benchmark duration (s): 118.67
|
||||
Total input tokens: 39668
|
||||
Total input text tokens: 39668
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 318306
|
||||
Total generated tokens (retokenized): 318270
|
||||
Request throughput (req/s): 0.67
|
||||
Input token throughput (tok/s): 334.27
|
||||
Output token throughput (tok/s): 2682.26
|
||||
Peak output token throughput (tok/s): 3264.00
|
||||
Peak concurrent requests: 19
|
||||
Total token throughput (tok/s): 3016.53
|
||||
Concurrency: 13.74
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 20387.23
|
||||
Median E2E Latency (ms): 20466.09
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 132.47
|
||||
Median TTFT (ms): 27.19
|
||||
P99 TTFT (ms): 583.15
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 5.09
|
||||
Median TPOT (ms): 5.13
|
||||
P99 TPOT (ms): 5.19
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 5.09
|
||||
Median ITL (ms): 5.08
|
||||
P95 ITL (ms): 5.18
|
||||
P99 ITL (ms): 5.57
|
||||
Max ITL (ms): 522.26
|
||||
==================================================
|
||||
```
|
||||
|
||||
##### 5.1.2.3 High Concurrency
|
||||
- **Benchmark Command**:
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model zai-org/Glyph \
|
||||
--dataset-name random \
|
||||
--random-input-len 1000 \
|
||||
--random-output-len 8000 \
|
||||
--num-prompts 320 \
|
||||
--max-concurrency 64 \
|
||||
--request-rate inf
|
||||
```
|
||||
- **Test Results**:
|
||||
```text Output
|
||||
============ Serving Benchmark Result ============
|
||||
Backend: sglang
|
||||
Traffic request rate: inf
|
||||
Max request concurrency: 64
|
||||
Successful requests: 320
|
||||
Benchmark duration (s): 150.00
|
||||
Total input tokens: 158939
|
||||
Total input text tokens: 158939
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 1301025
|
||||
Total generated tokens (retokenized): 1300901
|
||||
Request throughput (req/s): 2.13
|
||||
Input token throughput (tok/s): 1059.59
|
||||
Output token throughput (tok/s): 8673.49
|
||||
Peak output token throughput (tok/s): 11899.00
|
||||
Peak concurrent requests: 71
|
||||
Total token throughput (tok/s): 9733.09
|
||||
Concurrency: 54.71
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 25645.42
|
||||
Median E2E Latency (ms): 26913.26
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 163.75
|
||||
Median TTFT (ms): 93.67
|
||||
P99 TTFT (ms): 426.19
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 6.27
|
||||
Median TPOT (ms): 6.39
|
||||
P99 TPOT (ms): 6.59
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 6.27
|
||||
Median ITL (ms): 0.17
|
||||
P95 ITL (ms): 32.94
|
||||
P99 ITL (ms): 67.89
|
||||
Max ITL (ms): 136.00
|
||||
==================================================
|
||||
```
|
||||
|
||||
#### 5.1.3 Summarization Scenario Benchmark
|
||||
|
||||
#### 5.1.3.1 Low Concurrency
|
||||
- **Benchmark Command**:
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model zai-org/Glyph \
|
||||
--dataset-name random \
|
||||
--random-input-len 8000 \
|
||||
--random-output-len 1000 \
|
||||
--num-prompts 10 \
|
||||
--max-concurrency 1 \
|
||||
--request-rate inf
|
||||
```
|
||||
- **Test Results**:
|
||||
```text Output
|
||||
============ Serving Benchmark Result ============
|
||||
Backend: sglang
|
||||
Traffic request rate: inf
|
||||
Max request concurrency: 1
|
||||
Successful requests: 10
|
||||
Benchmark duration (s): 17.44
|
||||
Total input tokens: 41941
|
||||
Total input text tokens: 41941
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 4220
|
||||
Total generated tokens (retokenized): 4220
|
||||
Request throughput (req/s): 0.57
|
||||
Input token throughput (tok/s): 2405.19
|
||||
Output token throughput (tok/s): 242.00
|
||||
Peak output token throughput (tok/s): 250.00
|
||||
Peak concurrent requests: 2
|
||||
Total token throughput (tok/s): 2647.19
|
||||
Concurrency: 1.00
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 1742.54
|
||||
Median E2E Latency (ms): 1412.47
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 53.48
|
||||
Median TTFT (ms): 45.05
|
||||
P99 TTFT (ms): 98.57
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 4.01
|
||||
Median TPOT (ms): 4.01
|
||||
P99 TPOT (ms): 4.03
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 4.01
|
||||
Median ITL (ms): 4.01
|
||||
P95 ITL (ms): 4.06
|
||||
P99 ITL (ms): 4.09
|
||||
Max ITL (ms): 4.95
|
||||
==================================================
|
||||
```
|
||||
|
||||
##### 5.1.3.2 Medium Concurrency
|
||||
- **Benchmark Command**:
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model zai-org/Glyph \
|
||||
--dataset-name random \
|
||||
--random-input-len 8000 \
|
||||
--random-output-len 1000 \
|
||||
--num-prompts 80 \
|
||||
--max-concurrency 16 \
|
||||
--request-rate inf
|
||||
```
|
||||
- **Test Results**:
|
||||
```text Output
|
||||
============ Serving Benchmark Result ============
|
||||
Backend: sglang
|
||||
Traffic request rate: inf
|
||||
Max request concurrency: 16
|
||||
Successful requests: 80
|
||||
Benchmark duration (s): 16.90
|
||||
Total input tokens: 300020
|
||||
Total input text tokens: 300020
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 41669
|
||||
Total generated tokens (retokenized): 41668
|
||||
Request throughput (req/s): 4.73
|
||||
Input token throughput (tok/s): 17753.58
|
||||
Output token throughput (tok/s): 2465.75
|
||||
Peak output token throughput (tok/s): 3005.00
|
||||
Peak concurrent requests: 25
|
||||
Total token throughput (tok/s): 20219.33
|
||||
Concurrency: 13.68
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 2890.33
|
||||
Median E2E Latency (ms): 3069.55
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 41.46
|
||||
Median TTFT (ms): 31.75
|
||||
P99 TTFT (ms): 93.18
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 5.52
|
||||
Median TPOT (ms): 5.58
|
||||
P99 TPOT (ms): 6.14
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 5.48
|
||||
Median ITL (ms): 5.13
|
||||
P95 ITL (ms): 5.93
|
||||
P99 ITL (ms): 20.76
|
||||
Max ITL (ms): 36.01
|
||||
==================================================
|
||||
```
|
||||
|
||||
##### 5.1.3.3 High Concurrency
|
||||
|
||||
- **Benchmark Command**:
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model zai-org/Glyph \
|
||||
--dataset-name random \
|
||||
--random-input-len 8000 \
|
||||
--random-output-len 1000 \
|
||||
--num-prompts 320 \
|
||||
--max-concurrency 64 \
|
||||
--request-rate inf
|
||||
```
|
||||
- **Test Results**:
|
||||
```text Output
|
||||
============ Serving Benchmark Result ============
|
||||
Backend: sglang
|
||||
Traffic request rate: inf
|
||||
Max request concurrency: 64
|
||||
Successful requests: 320
|
||||
Benchmark duration (s): 35.54
|
||||
Total input tokens: 1273893
|
||||
Total input text tokens: 1273893
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 170000
|
||||
Total generated tokens (retokenized): 169994
|
||||
Request throughput (req/s): 9.01
|
||||
Input token throughput (tok/s): 35848.57
|
||||
Output token throughput (tok/s): 4783.96
|
||||
Peak output token throughput (tok/s): 8396.00
|
||||
Peak concurrent requests: 80
|
||||
Total token throughput (tok/s): 40632.53
|
||||
Concurrency: 59.26
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 6580.96
|
||||
Median E2E Latency (ms): 6248.74
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 345.27
|
||||
Median TTFT (ms): 96.06
|
||||
P99 TTFT (ms): 2823.92
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 12.26
|
||||
Median TPOT (ms): 12.53
|
||||
P99 TPOT (ms): 23.58
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 11.76
|
||||
Median ITL (ms): 6.57
|
||||
P95 ITL (ms): 27.66
|
||||
P99 ITL (ms): 91.24
|
||||
Max ITL (ms): 2609.64
|
||||
==================================================
|
||||
```
|
||||
|
||||
### 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
|
||||
```
|
||||
|
||||
- Test Result
|
||||
|
||||
```text Output
|
||||
Accuracy: 0.890
|
||||
Invalid: 0.000
|
||||
Latency: 3.718 s
|
||||
Output throughput: 5245.606 token/s
|
||||
```
|
||||
Reference in New Issue
Block a user