963 lines
35 KiB
Plaintext
963 lines
35 KiB
Plaintext
---
|
||
title: GLM-4.7
|
||
metatags:
|
||
description: "Deploy GLM-4.7 with SGLang on NVIDIA Blackwell (B200, GB200) and AMD GPUs - state-of-the-art reasoning, robust tool calling, and NVFP4 weights for Blackwell."
|
||
---
|
||
|
||
## 1. Model Introduction
|
||
|
||
[GLM-4.7](https://huggingface.co/zai-org/GLM-4.7) is a powerful language model developed by Zhipu AI, featuring advanced capabilities in reasoning, function calling, and agent workflows.
|
||
|
||
GLM-4.7 brings improvements across all major domains:
|
||
|
||
- **Extended Context Window**: Expanded context window supporting even longer documents and complex multi-turn conversations
|
||
- **Enhanced Reasoning**: Improved reasoning capabilities with better chain-of-thought processing
|
||
- **Superior Coding**: Significantly improved code generation and understanding, with better real-world application performance
|
||
- **Advanced Tool Use**: More robust tool calling and agent capabilities for complex workflows
|
||
- **Optimized Performance**: Better throughput and latency characteristics across all hardware platforms
|
||
|
||
For more details, please refer to the [official GLM-4.7 documentation](https://docs.z.ai/guides/llm/glm-4.7).
|
||
|
||
**Key Features:**
|
||
|
||
- **State-of-the-Art Reasoning**: Enhanced reasoning capabilities for the most complex problem-solving tasks
|
||
- **Multiple Quantizations**: BF16, FP8, and NVFP4 variants for different performance/memory trade-offs
|
||
- **Hardware Optimization**: Tuned for NVIDIA Blackwell (B200, GB200) and AMD MI300X/MI325X/MI355X GPUs
|
||
- **High Performance**: Optimized for both throughput and latency scenarios
|
||
|
||
**Available Models:**
|
||
|
||
- **BF16 (Full precision)**: [zai-org/GLM-4.7](https://huggingface.co/zai-org/GLM-4.7)
|
||
- **FP8 (8-bit quantized)**: [zai-org/GLM-4.7-FP8](https://huggingface.co/zai-org/GLM-4.7-FP8)
|
||
- **NVFP4 (4-bit, NVIDIA Blackwell)**: [nvidia/GLM-4.7-NVFP4](https://huggingface.co/nvidia/GLM-4.7-NVFP4)
|
||
|
||
**License:**
|
||
|
||
Please refer to the [official GLM-4.7 model card](https://huggingface.co/zai-org/GLM-4.7) for license details.
|
||
|
||
## 2. SGLang Installation
|
||
|
||
SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.
|
||
|
||
Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
|
||
|
||
**Docker Images by Hardware Platform:**
|
||
|
||
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
|
||
<thead>
|
||
<tr style={{borderBottom: "2px solid #d55816"}}>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Hardware Platform</th>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Docker Image</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA H100 / H200 / B200</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:v0.5.12`</td>
|
||
</tr>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA GB200 / B300 / GB300 (aarch64)</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:v0.5.12-cu130`</td>
|
||
</tr>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>AMD MI300X / MI325X</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:v0.5.12-rocm720-mi30x`</td>
|
||
</tr>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>AMD MI355X</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:v0.5.12-rocm720-mi35x`</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
|
||
|
||
## 3. Model Deployment
|
||
|
||
This section provides deployment configurations optimized for different hardware platforms and use cases.
|
||
|
||
### 3.1 Basic Configuration
|
||
|
||
**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.
|
||
|
||
import { GLM47Deployment } from "/src/snippets/autoregressive/glm-47-deployment.jsx";
|
||
|
||
<GLM47Deployment />
|
||
|
||
### 3.2 Configuration Tips
|
||
|
||
Pick a weight format by hardware: **NVFP4** on NVIDIA Blackwell (B200, GB200), **FP8** on H100/H200/AMD, **BF16** as the full-precision fallback. The recommended tensor-parallel size per platform:
|
||
|
||
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
|
||
<thead>
|
||
<tr style={{borderBottom: "2px solid #d55816"}}>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Hardware</th>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>NVFP4</th>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>FP8</th>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>BF16</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>B200 (8×, single node)</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=2 / 4 / 8</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=4 / 8</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=8</td>
|
||
</tr>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>GB200 (NVL72, 4× per tray)</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=2 / 4</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=4</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
|
||
</tr>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>H200 (8×)</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=8</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=8</td>
|
||
</tr>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>AMD MI300X / MI325X / MI355X</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=2 / 4 / 8</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=4 / 8</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
|
||
- **EAGLE Speculative Decoding:** Supported for GLM-4.7. 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.
|
||
- **Thinking Budget:** Use `--enable-custom-logit-processor` flag and pass `Glm4MoeThinkingBudgetLogitProcessor` in requests to cap the model's thinking token count (see section 4.2.3).
|
||
|
||
For general GLM-4.x family launch guidance (AMD ROCm notes and more), see [Launch GLM-4.5 / GLM-4.6 / GLM-4.7 with SGLang](/cookbook/autoregressive/GLM/GLM-4.5). Per-hardware bench commands and flags are inline in §5.1 below.
|
||
|
||
## 4. Model Invocation
|
||
|
||
### 4.1 Basic Usage
|
||
|
||
For basic API usage and request examples, please refer to:
|
||
|
||
- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)
|
||
|
||
### 4.2 Advanced Usage
|
||
|
||
#### 4.2.1 Reasoning Parser
|
||
|
||
GLM-4.7 supports Thinking mode by default. Enable the reasoning parser during deployment to separate the thinking and the content sections:
|
||
|
||
```shell Command
|
||
python -m sglang.launch_server \
|
||
--model zai-org/GLM-4.7 \
|
||
--reasoning-parser glm45 \
|
||
--tp 8 \
|
||
--host 0.0.0.0 \
|
||
--port 8000
|
||
```
|
||
|
||
**Streaming with Thinking Process:**
|
||
|
||
```python Example
|
||
from openai import OpenAI
|
||
|
||
client = OpenAI(
|
||
base_url="http://localhost:8000/v1",
|
||
api_key="EMPTY"
|
||
)
|
||
|
||
# Enable streaming to see the thinking process in real-time
|
||
response = client.chat.completions.create(
|
||
model="zai-org/GLM-4.7",
|
||
messages=[
|
||
{"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
|
||
],
|
||
temperature=0.7,
|
||
max_tokens=2048,
|
||
stream=True
|
||
)
|
||
|
||
# Process the stream
|
||
has_thinking = False
|
||
has_answer = False
|
||
thinking_started = False
|
||
|
||
for chunk in response:
|
||
if chunk.choices and len(chunk.choices) > 0:
|
||
delta = chunk.choices[0].delta
|
||
|
||
# Print thinking process
|
||
if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
|
||
if not thinking_started:
|
||
print("=============== Thinking =================", flush=True)
|
||
thinking_started = True
|
||
has_thinking = True
|
||
print(delta.reasoning_content, end="", flush=True)
|
||
|
||
# Print answer content
|
||
if delta.content:
|
||
# Close thinking section and add content header
|
||
if has_thinking and not has_answer:
|
||
print("\n=============== Content =================", flush=True)
|
||
has_answer = True
|
||
print(delta.content, end="", flush=True)
|
||
|
||
print()
|
||
```
|
||
|
||
**Output Example:**
|
||
|
||
```text Output
|
||
=============== Thinking =================
|
||
To solve this problem, I need to calculate 15% of 240.
|
||
Step 1: Convert 15% to decimal: 15% = 0.15
|
||
Step 2: Multiply 240 by 0.15
|
||
Step 3: 240 × 0.15 = 36
|
||
=============== Content =================
|
||
|
||
The answer is 36. To find 15% of 240, we multiply 240 by 0.15, which equals 36.
|
||
```
|
||
|
||
**Note:** The reasoning parser captures the model's step-by-step thinking process, allowing you to see how the model arrives at its conclusions.
|
||
|
||
#### 4.2.2 Tool Calling
|
||
|
||
<Note>
|
||
**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.
|
||
</Note>
|
||
|
||
GLM-4.7 supports tool calling capabilities. Enable the tool call parser:
|
||
|
||
```shell Command
|
||
python -m sglang.launch_server \
|
||
--model zai-org/GLM-4.7 \
|
||
--reasoning-parser glm45 \
|
||
--tool-call-parser glm47 \
|
||
--tp 8 \
|
||
--host 0.0.0.0 \
|
||
--port 8000
|
||
```
|
||
|
||
**Python Example (with Thinking Process):**
|
||
|
||
```python Example
|
||
from openai import OpenAI
|
||
|
||
client = OpenAI(
|
||
base_url="http://localhost:8000/v1",
|
||
api_key="EMPTY"
|
||
)
|
||
|
||
# Define available tools
|
||
tools = [
|
||
{
|
||
"type": "function",
|
||
"function": {
|
||
"name": "get_weather",
|
||
"description": "Get the current weather for a location",
|
||
"parameters": {
|
||
"type": "object",
|
||
"properties": {
|
||
"location": {
|
||
"type": "string",
|
||
"description": "The city name"
|
||
},
|
||
"unit": {
|
||
"type": "string",
|
||
"enum": ["celsius", "fahrenheit"],
|
||
"description": "Temperature unit"
|
||
}
|
||
},
|
||
"required": ["location"]
|
||
}
|
||
}
|
||
}
|
||
]
|
||
|
||
# Make request with streaming to see thinking process
|
||
response = client.chat.completions.create(
|
||
model="zai-org/GLM-4.7",
|
||
messages=[
|
||
{"role": "user", "content": "What's the weather in Beijing?"}
|
||
],
|
||
tools=tools,
|
||
temperature=0.7,
|
||
stream=True
|
||
)
|
||
|
||
# Process streaming response
|
||
thinking_started = False
|
||
has_thinking = False
|
||
|
||
for chunk in response:
|
||
if chunk.choices and len(chunk.choices) > 0:
|
||
delta = chunk.choices[0].delta
|
||
|
||
# Print thinking process
|
||
if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
|
||
if not thinking_started:
|
||
print("=============== Thinking =================", flush=True)
|
||
thinking_started = True
|
||
has_thinking = True
|
||
print(delta.reasoning_content, end="", flush=True)
|
||
|
||
# Print tool calls
|
||
if hasattr(delta, 'tool_calls') and delta.tool_calls:
|
||
# Close thinking section if needed
|
||
if has_thinking and thinking_started:
|
||
print("\n=============== Content =================", flush=True)
|
||
thinking_started = False
|
||
|
||
for tool_call in delta.tool_calls:
|
||
if tool_call.function:
|
||
print(f"Tool Call: {tool_call.function.name}")
|
||
print(f" Arguments: {tool_call.function.arguments}")
|
||
|
||
# Print content
|
||
if delta.content:
|
||
print(delta.content, end="", flush=True)
|
||
|
||
print()
|
||
```
|
||
|
||
**Output Example:**
|
||
|
||
```text Output
|
||
=============== Thinking =================
|
||
The user is asking about the weather in Beijing. I need to use the get_weather function to retrieve this information.
|
||
I should call the function with location="Beijing".
|
||
=============== Content =================
|
||
|
||
Tool Call: get_weather
|
||
Arguments: {"location": "Beijing", "unit": "celsius"}
|
||
```
|
||
|
||
**Note:**
|
||
|
||
- The reasoning parser shows how the model decides to use a tool
|
||
- Tool calls are clearly marked with the function name and arguments
|
||
- You can then execute the function and send the result back to continue the conversation
|
||
|
||
**Handling Tool Call Results:**
|
||
|
||
```python Example
|
||
# After getting the tool call, execute the function
|
||
def get_weather(location, unit="celsius"):
|
||
# Your actual weather API call here
|
||
return f"The weather in {location} is 22°{unit[0].upper()} and sunny."
|
||
|
||
# Send tool result back to the model
|
||
messages = [
|
||
{"role": "user", "content": "What's the weather in Beijing?"},
|
||
{
|
||
"role": "assistant",
|
||
"content": None,
|
||
"tool_calls": [{
|
||
"id": "call_123",
|
||
"type": "function",
|
||
"function": {
|
||
"name": "get_weather",
|
||
"arguments": '{"location": "Beijing", "unit": "celsius"}'
|
||
}
|
||
}]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"tool_call_id": "call_123",
|
||
"content": get_weather("Beijing", "celsius")
|
||
}
|
||
]
|
||
|
||
final_response = client.chat.completions.create(
|
||
model="zai-org/GLM-4.7",
|
||
messages=messages,
|
||
temperature=0.7
|
||
)
|
||
|
||
print(final_response.choices[0].message.content)
|
||
# Output: "The weather in Beijing is currently 22°C and sunny."
|
||
```
|
||
|
||
#### 4.2.3 Thinking Budget
|
||
|
||
Limit the number of thinking tokens using `CustomLogitProcessor`. Launch with `--enable-custom-logit-processor`:
|
||
|
||
```python Example
|
||
import openai
|
||
from sglang.srt.sampling.custom_logit_processor import Glm4MoeThinkingBudgetLogitProcessor
|
||
|
||
client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="*")
|
||
response = client.chat.completions.create(
|
||
model="zai-org/GLM-4.7",
|
||
messages=[{"role": "user", "content": "Is Paris the Capital of France?"}],
|
||
max_tokens=1024,
|
||
extra_body={
|
||
"custom_logit_processor": Glm4MoeThinkingBudgetLogitProcessor().to_str(),
|
||
"custom_params": {"thinking_budget": 512},
|
||
},
|
||
)
|
||
print(response)
|
||
```
|
||
|
||
## 5. Benchmark
|
||
|
||
This section uses **industry-standard configurations** for comparable benchmark results.
|
||
|
||
### 5.1 Speed Benchmark
|
||
|
||
**Test Environment:**
|
||
|
||
- Hardware: NVIDIA B200, NVIDIA GB200, AMD MI300X/MI325X/MI355X (8x)
|
||
- Model: GLM-4.7-NVFP4 on NVIDIA Blackwell; GLM-4.7-FP8 or GLM-4.7 (BF16) on AMD
|
||
- SGLang Version: 0.5.12 (NVIDIA Blackwell), 0.5.6.post1 (AMD)
|
||
- Best per-GPU throughput config on B200: **TP=2 NVFP4 bf16-KV** (NVFP4 weights, no EP). Numbers below come from this config.
|
||
|
||
**Benchmark Methodology:**
|
||
|
||
We use industry-standard benchmark configurations to ensure results are comparable across frameworks and hardware platforms.
|
||
|
||
#### 5.1.1 Standard Test Scenarios
|
||
|
||
Four core scenarios reflect real-world usage patterns:
|
||
|
||
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
|
||
<colgroup>
|
||
<col style={{width: "25%"}} />
|
||
<col style={{width: "25%"}} />
|
||
<col style={{width: "25%"}} />
|
||
<col style={{width: "25%"}} />
|
||
</colgroup>
|
||
<thead>
|
||
<tr style={{borderBottom: "2px solid #d55816"}}>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Scenario</th>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Input Length</th>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Output Length</th>
|
||
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Use Case</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Chat**</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1K</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1K</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Most common conversational AI workload</td>
|
||
</tr>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Reasoning**</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1K</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>8K</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Long-form generation, complex reasoning tasks</td>
|
||
</tr>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Summarization**</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>8K</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1K</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Document summarization, RAG retrieval</td>
|
||
</tr>
|
||
<tr>
|
||
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Throughput**</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>4K</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1K</td>
|
||
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Mixed RAG / agent / multi-turn conversation (used for the inline B200 / GB200 results below)</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
|
||
#### 5.1.2 Concurrency Levels
|
||
|
||
Test each scenario at three concurrency levels to capture the throughput vs. latency tradeoff (Pareto frontier):
|
||
|
||
- **Low Concurrency**: `--max-concurrency 1` (Latency-optimized)
|
||
- **Medium Concurrency**: `--max-concurrency 16` (Balanced)
|
||
- **High Concurrency**: `--max-concurrency 100` (Throughput-optimized) — the Throughput (4K/1K) scenario uses `--max-concurrency 128` to match the inline B200/GB200 results below.
|
||
|
||
#### 5.1.3 Number of Prompts
|
||
|
||
For each concurrency level, configure `num_prompts` to simulate realistic user loads:
|
||
|
||
- **Quick Test**: `num_prompts = concurrency × 1` (minimal test)
|
||
- **Recommended**: `num_prompts = concurrency × 5` (standard benchmark)
|
||
- **Stable Measurements**: `num_prompts = concurrency × 10` (production-grade)
|
||
|
||
---
|
||
|
||
#### 5.1.4 Benchmark Commands
|
||
|
||
**Scenario 1: Chat (1K/1K) - Most Important**
|
||
|
||
- **Model Deployment**
|
||
```bash Command
|
||
python -m sglang.launch_server \
|
||
--model zai-org/GLM-4.7 \
|
||
--tp 8
|
||
```
|
||
|
||
|
||
- Low Concurrency (Latency-Optimized)
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 10 \
|
||
--max-concurrency 1 \
|
||
--request-rate inf
|
||
```
|
||
|
||
- Medium Concurrency (Balanced)
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 80 \
|
||
--max-concurrency 16 \
|
||
--request-rate inf
|
||
```
|
||
|
||
- High Concurrency (Throughput-Optimized)
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 500 \
|
||
--max-concurrency 100 \
|
||
--request-rate inf
|
||
```
|
||
|
||
**Scenario 2: Reasoning (1K/8K)**
|
||
|
||
- Low Concurrency
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 8000 \
|
||
--num-prompts 10 \
|
||
--max-concurrency 1 \
|
||
--request-rate inf
|
||
```
|
||
|
||
- Medium Concurrency
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 8000 \
|
||
--num-prompts 80 \
|
||
--max-concurrency 16 \
|
||
--request-rate inf
|
||
```
|
||
|
||
- High Concurrency
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7 \
|
||
--dataset-name random \
|
||
--random-input-len 1000 \
|
||
--random-output-len 8000 \
|
||
--num-prompts 320 \
|
||
--max-concurrency 64 \
|
||
--request-rate inf
|
||
```
|
||
|
||
**Scenario 3: Summarization (8K/1K)**
|
||
|
||
- Low Concurrency
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7 \
|
||
--dataset-name random \
|
||
--random-input-len 8000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 10 \
|
||
--max-concurrency 1 \
|
||
--request-rate inf
|
||
```
|
||
|
||
- Medium Concurrency
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7 \
|
||
--dataset-name random \
|
||
--random-input-len 8000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 80 \
|
||
--max-concurrency 16 \
|
||
--request-rate inf
|
||
```
|
||
|
||
- High Concurrency
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model zai-org/GLM-4.7 \
|
||
--dataset-name random \
|
||
--random-input-len 8000 \
|
||
--random-output-len 1000 \
|
||
--num-prompts 320 \
|
||
--max-concurrency 64 \
|
||
--request-rate inf
|
||
```
|
||
|
||
**Scenario 4: Throughput (4K/1K) — NVIDIA Blackwell with NVFP4**
|
||
|
||
The remaining sub-sections (§5.1.4.1 NVIDIA B200, §5.1.4.2 NVIDIA GB200) measure this scenario with `nvidia/GLM-4.7-NVFP4` weights and report the full `bench_serving` output verbatim. The same commands apply to other NVIDIA hardware after substituting the deployment line from §3.1.
|
||
|
||
> **Note**: These runs use EOS-enabled generation (no `--disable-ignore-eos`), so generated-token counts reflect natural model behavior rather than a strict fixed-OSL pin. Compare against other EOS-enabled runs at the same workload, not against fixed-output-length benchmarks.
|
||
|
||
#### 5.1.4.1 NVIDIA B200
|
||
|
||
**Model Deployment (NVIDIA B200, TP=2 NVFP4 — max tok/s/gpu config):**
|
||
|
||
```bash Command
|
||
python -m sglang.launch_server \
|
||
--model nvidia/GLM-4.7-NVFP4 \
|
||
--tp-size 2 \
|
||
--mem-fraction-static 0.85 \
|
||
--reasoning-parser glm45 \
|
||
--tool-call-parser glm47
|
||
```
|
||
|
||
- Low Concurrency (Latency-Optimized)
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model nvidia/GLM-4.7-NVFP4 \
|
||
--dataset-name random \
|
||
--random-input-len 4096 \
|
||
--random-output-len 1024 \
|
||
--num-prompts 5 \
|
||
--max-concurrency 1 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Max request concurrency: 1
|
||
Successful requests: 5
|
||
Benchmark duration (s): 25.07
|
||
Total input tokens: 8105
|
||
Total generated tokens: 2674
|
||
Request throughput (req/s): 0.20
|
||
Input token throughput (tok/s): 323.25
|
||
Output token throughput (tok/s): 106.65
|
||
Total token throughput (tok/s): 429.90
|
||
Concurrency: 1.00
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 5011.93
|
||
Median E2E Latency (ms): 6441.44
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 179.61
|
||
Median TTFT (ms): 169.05
|
||
P99 TTFT (ms): 238.01
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 9.05
|
||
Median TPOT (ms): 9.03
|
||
P99 TPOT (ms): 9.16
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 9.05
|
||
Median ITL (ms): 9.05
|
||
==================================================
|
||
```
|
||
|
||
- Medium Concurrency (Balanced)
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model nvidia/GLM-4.7-NVFP4 \
|
||
--dataset-name random \
|
||
--random-input-len 4096 \
|
||
--random-output-len 1024 \
|
||
--num-prompts 80 \
|
||
--max-concurrency 16 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Max request concurrency: 16
|
||
Successful requests: 80
|
||
Benchmark duration (s): 60.60
|
||
Total input tokens: 179772
|
||
Total generated tokens: 39657
|
||
Request throughput (req/s): 1.32
|
||
Input token throughput (tok/s): 2966.39
|
||
Output token throughput (tok/s): 654.37
|
||
Total token throughput (tok/s): 3620.76
|
||
Concurrency: 14.01
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 10615.87
|
||
Median E2E Latency (ms): 9985.45
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 267.39
|
||
Median TTFT (ms): 177.26
|
||
P99 TTFT (ms): 584.29
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 20.98
|
||
Median TPOT (ms): 21.06
|
||
P99 TPOT (ms): 24.88
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 20.92
|
||
Median ITL (ms): 17.93
|
||
==================================================
|
||
```
|
||
|
||
- High Concurrency (Throughput-Optimized)
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model nvidia/GLM-4.7-NVFP4 \
|
||
--dataset-name random \
|
||
--random-input-len 4096 \
|
||
--random-output-len 1024 \
|
||
--num-prompts 640 \
|
||
--max-concurrency 128 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Max request concurrency: 128
|
||
Successful requests: 640
|
||
Benchmark duration (s): 172.95
|
||
Total input tokens: 1453591
|
||
Total generated tokens: 308740
|
||
Request throughput (req/s): 3.70
|
||
Input token throughput (tok/s): 8404.67
|
||
Output token throughput (tok/s): 1785.14
|
||
Total token throughput (tok/s): 10189.80
|
||
Concurrency: 117.85
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 31848.20
|
||
Median E2E Latency (ms): 28554.42
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 1598.40
|
||
Median TTFT (ms): 298.88
|
||
P99 TTFT (ms): 11015.96
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 65.94
|
||
Median TPOT (ms): 65.81
|
||
P99 TPOT (ms): 137.73
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 62.99
|
||
Median ITL (ms): 35.44
|
||
==================================================
|
||
```
|
||
|
||
#### 5.1.4.2 NVIDIA GB200
|
||
|
||
**Model Deployment (NVIDIA GB200, TP=2 NVFP4 — max tok/s/gpu config):**
|
||
|
||
```bash Command
|
||
python -m sglang.launch_server \
|
||
--model nvidia/GLM-4.7-NVFP4 \
|
||
--tp-size 2 \
|
||
--mem-fraction-static 0.85 \
|
||
--reasoning-parser glm45 \
|
||
--tool-call-parser glm47
|
||
```
|
||
|
||
- Low Concurrency (Latency-Optimized)
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model nvidia/GLM-4.7-NVFP4 \
|
||
--dataset-name random \
|
||
--random-input-len 4096 \
|
||
--random-output-len 1024 \
|
||
--num-prompts 5 \
|
||
--max-concurrency 1 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Max request concurrency: 1
|
||
Successful requests: 5
|
||
Benchmark duration (s): 24.74
|
||
Total input tokens: 8105
|
||
Total generated tokens: 2674
|
||
Request throughput (req/s): 0.20
|
||
Input token throughput (tok/s): 327.65
|
||
Output token throughput (tok/s): 108.10
|
||
Total token throughput (tok/s): 435.75
|
||
Concurrency: 1.00
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 4944.47
|
||
Median E2E Latency (ms): 6347.31
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 211.41
|
||
Median TTFT (ms): 207.25
|
||
P99 TTFT (ms): 226.46
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 8.86
|
||
Median TPOT (ms): 8.84
|
||
P99 TPOT (ms): 8.96
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 8.87
|
||
Median ITL (ms): 8.85
|
||
==================================================
|
||
```
|
||
|
||
- Medium Concurrency (Balanced)
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model nvidia/GLM-4.7-NVFP4 \
|
||
--dataset-name random \
|
||
--random-input-len 4096 \
|
||
--random-output-len 1024 \
|
||
--num-prompts 80 \
|
||
--max-concurrency 16 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Max request concurrency: 16
|
||
Successful requests: 80
|
||
Benchmark duration (s): 60.40
|
||
Total input tokens: 179772
|
||
Total generated tokens: 39657
|
||
Request throughput (req/s): 1.32
|
||
Input token throughput (tok/s): 2976.52
|
||
Output token throughput (tok/s): 656.61
|
||
Total token throughput (tok/s): 3633.13
|
||
Concurrency: 13.97
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 10611.51
|
||
Median E2E Latency (ms): 9956.84
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 338.14
|
||
Median TTFT (ms): 215.25
|
||
P99 TTFT (ms): 915.40
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 20.87
|
||
Median TPOT (ms): 21.36
|
||
P99 TPOT (ms): 27.05
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 20.77
|
||
Median ITL (ms): 16.53
|
||
==================================================
|
||
```
|
||
|
||
- High Concurrency (Throughput-Optimized)
|
||
|
||
```bash Command
|
||
python -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--model nvidia/GLM-4.7-NVFP4 \
|
||
--dataset-name random \
|
||
--random-input-len 4096 \
|
||
--random-output-len 1024 \
|
||
--num-prompts 640 \
|
||
--max-concurrency 128 \
|
||
--request-rate inf
|
||
```
|
||
|
||
```text Output
|
||
============ Serving Benchmark Result ============
|
||
Backend: sglang
|
||
Max request concurrency: 128
|
||
Successful requests: 640
|
||
Benchmark duration (s): 181.89
|
||
Total input tokens: 1453591
|
||
Total generated tokens: 309221
|
||
Request throughput (req/s): 3.52
|
||
Input token throughput (tok/s): 7991.59
|
||
Output token throughput (tok/s): 1700.04
|
||
Total token throughput (tok/s): 9691.63
|
||
Concurrency: 118.86
|
||
----------------End-to-End Latency----------------
|
||
Mean E2E Latency (ms): 33690.47
|
||
Median E2E Latency (ms): 30421.55
|
||
---------------Time to First Token----------------
|
||
Mean TTFT (ms): 1353.16
|
||
Median TTFT (ms): 383.52
|
||
P99 TTFT (ms): 8940.53
|
||
-----Time per Output Token (excl. 1st token)------
|
||
Mean TPOT (ms): 69.88
|
||
Median TPOT (ms): 71.77
|
||
P99 TPOT (ms): 131.75
|
||
---------------Inter-Token Latency----------------
|
||
Mean ITL (ms): 67.23
|
||
Median ITL (ms): 33.46
|
||
==================================================
|
||
```
|
||
|
||
#### 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.
|
||
- **4K/1K (Throughput)**: Realistic mixed workload typical of production deployments (RAG context + medium response). Long enough input that prefill matters, long enough output that decode steady-state dominates. Used for the inline B200 / GB200 results above.
|
||
- **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-shots 5 \
|
||
--num-questions 1319 \
|
||
--port 30000
|
||
```
|
||
|
||
- Test Result (NVIDIA B200, TP=2 NVFP4)
|
||
```text Output
|
||
Accuracy: 0.946
|
||
Latency: 178.284 s
|
||
Output throughput: 769.204 token/s
|
||
```
|
||
|
||
- Test Result (NVIDIA GB200, TP=2 NVFP4)
|
||
```text Output
|
||
Accuracy: 0.951
|
||
Latency: 175.190 s
|
||
Invalid: 0.000
|
||
```
|