936 lines
34 KiB
Plaintext
936 lines
34 KiB
Plaintext
---
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title: GLM-4.7-Flash
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metatags:
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description: "Deploy GLM-4.7-Flash 30B-A3B MoE model with SGLang - lightweight, efficient inference optimized for single-GPU deployment."
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---
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## 1. Model Introduction
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[GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) is a lightweight and high-speed model in the GLM-4.7 series developed by Zhipu AI, featuring state-of-the-art capabilities in reasoning, function calling, and efficient local deployment.
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As a compact variant in the GLM-4.7 family, GLM-4.7-Flash is a **30B-A3B MoE** model designed to balance performance and efficiency:
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- **Lightweight Architecture**: 30B total parameters with only 3B active parameters, enabling efficient inference
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- **Enhanced Reasoning**: Inherits the reasoning capabilities from GLM-4.7 with optimized performance
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- **Superior Coding**: Strong code generation and understanding capabilities
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- **Advanced Tool Use**: Robust tool calling and agent capabilities for complex workflows
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- **Optimized for Local Deployment**: Designed for single-GPU deployment scenarios
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For more details, please refer to the [official GLM-4.7 documentation](https://docs.z.ai/guides/llm/glm-4.7).
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**Key Features:**
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- **Efficient MoE Architecture**: 30B-A3B sparse activation for optimal performance/efficiency trade-off
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- **Multiple Quantizations**: BF16 and FP8 variants for different performance/memory trade-offs
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- **Hardware Optimization**: Specifically tuned for NVIDIA H100/H200/B200 GPUs
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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/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash)
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**License:**
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Please refer to the [official GLM-4.7-Flash model card](https://huggingface.co/zai-org/GLM-4.7-Flash) 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, deployment strategy, and thinking capabilities.
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import { GLM47FlashDeployment } from "/src/snippets/autoregressive/glm-47-flash-deployment.jsx";
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<GLM47FlashDeployment />
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### 3.2 Configuration Tips
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- **EAGLE Speculative Decoding:** Supported for GLM-4.7-Flash. 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. Enable via the interactive command generator above.
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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-4.7-Flash 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-4.7-Flash \
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--reasoning-parser glm45 \
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--attention-backend triton \
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--tp 1 \
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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-4.7-Flash",
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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-4.7-Flash 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-4.7-Flash \
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--reasoning-parser glm45 \
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--tool-call-parser glm47 \
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--attention-backend triton \
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--tp 1 \
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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-4.7-Flash",
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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 (tool call deltas may stream in multiple chunks)
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if hasattr(delta, 'tool_calls') and delta.tool_calls:
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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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if tool_calls_accumulator:
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print("\n=============== Tool Calls =================", flush=True)
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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 for the weather in Beijing. I have the get_weather function available which can provide weather information for a location. The required parameter is "location" and the
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user has provided "Beijing". There's an optional parameter "unit" for temperature unit, but the user hasn't specified which unit they prefer, and since it's optional, I should not ask about it or make up a value for it. I'll call the function with just the location parameter.I'll check the current weather in Beijing for you.
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=============== Tool Calls =================
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Tool Call: get_weather
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Arguments: {"location": "Beijing"}
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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-4.7-Flash",
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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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- Hardware: NVIDIA B200 (1x)
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- Model: GLM-4.7-Flash
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- Tensor Parallelism: 1
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- SGLang Version: 0.5.7
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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-4.7-Flash \
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--attention-backend triton \
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--tp 1
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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-4.7-Flash \
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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): 38.94
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Total input tokens: 6101
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Total input text tokens: 6101
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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): 0.26
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Input token throughput (tok/s): 156.67
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Output token throughput (tok/s): 108.37
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Peak output token throughput (tok/s): 125.00
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Peak concurrent requests: 2
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Total token throughput (tok/s): 265.03
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Concurrency: 1.00
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----------------End-to-End Latency----------------
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Mean E2E Latency (ms): 3891.12
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Median E2E Latency (ms): 3061.48
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P90 E2E Latency (ms): 7172.25
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P99 E2E Latency (ms): 9042.62
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---------------Time to First Token----------------
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Mean TTFT (ms): 131.36
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Median TTFT (ms): 94.55
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P99 TTFT (ms): 435.93
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-----Time per Output Token (excl. 1st token)------
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Mean TPOT (ms): 8.75
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Median TPOT (ms): 8.82
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P99 TPOT (ms): 9.39
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---------------Inter-Token Latency----------------
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Mean ITL (ms): 8.93
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Median ITL (ms): 8.98
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P95 ITL (ms): 9.83
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P99 ITL (ms): 10.20
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Max ITL (ms): 18.50
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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-4.7-Flash \
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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): 52.73
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Total input tokens: 39668
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Total input text tokens: 39668
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Total generated tokens: 40805
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Total generated tokens (retokenized): 40775
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Request throughput (req/s): 1.52
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Input token throughput (tok/s): 752.27
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Output token throughput (tok/s): 773.83
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Peak output token throughput (tok/s): 1040.00
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Peak concurrent requests: 21
|
||
Total token throughput (tok/s): 1526.10
|
||
Concurrency: 13.98
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 9217.90
|
||
Median E2E Latency (ms): 9642.50
|
||
P90 E2E Latency (ms): 15147.02
|
||
P99 E2E Latency (ms): 18237.06
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 299.02
|
||
Median TTFT (ms): 105.98
|
||
P99 TTFT (ms): 1109.29
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 18.03
|
||
Median TPOT (ms): 18.00
|
||
P99 TPOT (ms): 26.51
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 17.52
|
||
Median ITL (ms): 16.07
|
||
P95 ITL (ms): 18.14
|
||
P99 ITL (ms): 89.43
|
||
Max ITL (ms): 763.13
|
||
==================================================
|
||
```
|
||
|
||
- High Concurrency (Throughput-Optimized)
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7-Flash \
|
||
--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): 91.48
|
||
Total input tokens: 249831
|
||
Total input text tokens: 249831
|
||
Total generated tokens: 252662
|
||
Total generated tokens (retokenized): 250941
|
||
Request throughput (req/s): 5.47
|
||
Input token throughput (tok/s): 2730.87
|
||
Output token throughput (tok/s): 2761.82
|
||
Peak output token throughput (tok/s): 4199.00
|
||
Peak concurrent requests: 109
|
||
Total token throughput (tok/s): 5492.69
|
||
Concurrency: 90.54
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 16566.04
|
||
Median E2E Latency (ms): 16134.36
|
||
P90 E2E Latency (ms): 30167.60
|
||
P99 E2E Latency (ms): 34034.04
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 433.94
|
||
Median TTFT (ms): 123.26
|
||
P99 TTFT (ms): 1760.09
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 32.26
|
||
Median TPOT (ms): 33.56
|
||
P99 TPOT (ms): 38.78
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 31.99
|
||
Median ITL (ms): 24.06
|
||
P95 ITL (ms): 79.62
|
||
P99 ITL (ms): 103.03
|
||
Max ITL (ms): 1369.20
|
||
==================================================
|
||
```
|
||
|
||
|
||
**Scenario 2: Reasoning (1K/8K)**
|
||
|
||
- Low Concurrency
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7-Flash \
|
||
--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): 525.43
|
||
Total input tokens: 6101
|
||
Total input text tokens: 6101
|
||
Total generated tokens: 44462
|
||
Total generated tokens (retokenized): 44451
|
||
Request throughput (req/s): 0.02
|
||
Input token throughput (tok/s): 11.61
|
||
Output token throughput (tok/s): 84.62
|
||
Peak output token throughput (tok/s): 125.00
|
||
Peak concurrent requests: 2
|
||
Total token throughput (tok/s): 96.23
|
||
Concurrency: 1.00
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 52540.19
|
||
Median E2E Latency (ms): 53694.45
|
||
P90 E2E Latency (ms): 94742.08
|
||
P99 E2E Latency (ms): 101224.18
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 97.45
|
||
Median TTFT (ms): 95.28
|
||
P99 TTFT (ms): 105.64
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 10.94
|
||
Median TPOT (ms): 11.25
|
||
P99 TPOT (ms): 13.09
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 11.80
|
||
Median ITL (ms): 11.51
|
||
P95 ITL (ms): 15.83
|
||
P99 ITL (ms): 16.86
|
||
Max ITL (ms): 19.96
|
||
==================================================
|
||
```
|
||
|
||
- Medium Concurrency
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7-Flash \
|
||
--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): 473.92
|
||
Total input tokens: 39668
|
||
Total input text tokens: 39668
|
||
Total generated tokens: 318306
|
||
Total generated tokens (retokenized): 317860
|
||
Request throughput (req/s): 0.17
|
||
Input token throughput (tok/s): 83.70
|
||
Output token throughput (tok/s): 671.65
|
||
Peak output token throughput (tok/s): 1040.00
|
||
Peak concurrent requests: 19
|
||
Total token throughput (tok/s): 755.35
|
||
Concurrency: 13.80
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 81746.73
|
||
Median E2E Latency (ms): 78508.54
|
||
P90 E2E Latency (ms): 155292.49
|
||
P99 E2E Latency (ms): 166769.99
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 117.50
|
||
Median TTFT (ms): 101.97
|
||
P99 TTFT (ms): 182.88
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 20.36
|
||
Median TPOT (ms): 20.48
|
||
P99 TPOT (ms): 22.63
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 20.52
|
||
Median ITL (ms): 20.42
|
||
P95 ITL (ms): 23.41
|
||
P99 ITL (ms): 26.29
|
||
Max ITL (ms): 90.48
|
||
==================================================
|
||
```
|
||
|
||
- High Concurrency
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7-Flash \
|
||
--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): 714.72
|
||
Total input tokens: 158939
|
||
Total input text tokens: 158939
|
||
Total generated tokens: 1301025
|
||
Total generated tokens (retokenized): 1289431
|
||
Request throughput (req/s): 0.45
|
||
Input token throughput (tok/s): 222.38
|
||
Output token throughput (tok/s): 1820.33
|
||
Peak output token throughput (tok/s): 3200.00
|
||
Peak concurrent requests: 68
|
||
Total token throughput (tok/s): 2042.71
|
||
Concurrency: 55.68
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 124364.58
|
||
Median E2E Latency (ms): 129250.98
|
||
P90 E2E Latency (ms): 219175.80
|
||
P99 E2E Latency (ms): 247741.77
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 149.40
|
||
Median TTFT (ms): 114.78
|
||
P99 TTFT (ms): 288.60
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 30.51
|
||
Median TPOT (ms): 31.75
|
||
P99 TPOT (ms): 33.32
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 30.56
|
||
Median ITL (ms): 30.82
|
||
P95 ITL (ms): 33.20
|
||
P99 ITL (ms): 80.54
|
||
Max ITL (ms): 117.72
|
||
==================================================
|
||
```
|
||
|
||
**Scenario 3: Summarization (8K/1K)**
|
||
|
||
- Low Concurrency
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7-Flash \
|
||
--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): 58.27
|
||
Total input tokens: 41941
|
||
Total input text tokens: 41941
|
||
Total generated tokens: 4220
|
||
Total generated tokens (retokenized): 4220
|
||
Request throughput (req/s): 0.17
|
||
Input token throughput (tok/s): 719.73
|
||
Output token throughput (tok/s): 72.42
|
||
Peak output token throughput (tok/s): 112.00
|
||
Peak concurrent requests: 2
|
||
Total token throughput (tok/s): 792.15
|
||
Concurrency: 1.00
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 5825.08
|
||
Median E2E Latency (ms): 4624.26
|
||
P90 E2E Latency (ms): 12690.22
|
||
P99 E2E Latency (ms): 13177.96
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 296.01
|
||
Median TTFT (ms): 195.59
|
||
P99 TTFT (ms): 717.88
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 12.63
|
||
Median TPOT (ms): 13.07
|
||
P99 TPOT (ms): 16.68
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 13.13
|
||
Median ITL (ms): 13.17
|
||
P95 ITL (ms): 17.02
|
||
P99 ITL (ms): 17.47
|
||
Max ITL (ms): 19.84
|
||
==================================================
|
||
```
|
||
|
||
- Medium Concurrency
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7-Flash \
|
||
--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): 89.59
|
||
Total input tokens: 300020
|
||
Total input text tokens: 300020
|
||
Total generated tokens: 41669
|
||
Total generated tokens (retokenized): 41656
|
||
Request throughput (req/s): 0.89
|
||
Input token throughput (tok/s): 3348.77
|
||
Output token throughput (tok/s): 465.10
|
||
Peak output token throughput (tok/s): 752.00
|
||
Peak concurrent requests: 19
|
||
Total token throughput (tok/s): 3813.87
|
||
Concurrency: 14.39
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 16120.74
|
||
Median E2E Latency (ms): 16246.55
|
||
P90 E2E Latency (ms): 27279.72
|
||
P99 E2E Latency (ms): 34577.93
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 1943.94
|
||
Median TTFT (ms): 382.19
|
||
P99 TTFT (ms): 8980.41
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 27.87
|
||
Median TPOT (ms): 28.26
|
||
P99 TPOT (ms): 40.55
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 27.27
|
||
Median ITL (ms): 21.74
|
||
P95 ITL (ms): 23.32
|
||
P99 ITL (ms): 232.65
|
||
Max ITL (ms): 4282.01
|
||
==================================================
|
||
```
|
||
|
||
- High Concurrency
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7-Flash \
|
||
--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): 167.01
|
||
Total input tokens: 1273893
|
||
Total input text tokens: 1273893
|
||
Total generated tokens: 170000
|
||
Total generated tokens (retokenized): 169226
|
||
Request throughput (req/s): 1.92
|
||
Input token throughput (tok/s): 7627.82
|
||
Output token throughput (tok/s): 1017.93
|
||
Peak output token throughput (tok/s): 1984.00
|
||
Peak concurrent requests: 69
|
||
Total token throughput (tok/s): 8645.75
|
||
Concurrency: 59.68
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 31147.52
|
||
Median E2E Latency (ms): 30603.34
|
||
P90 E2E Latency (ms): 54889.44
|
||
P99 E2E Latency (ms): 67665.30
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 428.87
|
||
Median TTFT (ms): 441.69
|
||
P99 TTFT (ms): 1232.68
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 58.06
|
||
Median TPOT (ms): 62.79
|
||
P99 TPOT (ms): 82.23
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 57.93
|
||
Median ITL (ms): 33.30
|
||
P95 ITL (ms): 247.98
|
||
P99 ITL (ms): 409.63
|
||
Max ITL (ms): 1421.21
|
||
==================================================
|
||
```
|
||
|
||
#### 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
|
||
```
|
||
|
||
- Result
|
||
|
||
```text Output
|
||
Accuracy: 0.845
|
||
Invalid: 0.000
|
||
Latency: 8.431 s
|
||
Output throughput: 2195.387 token/s
|
||
```
|