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---
title: GLM Glyph
metatags:
description: "Deploy GLM-Glyph with SGLang - community contribution guide for Zhipu AI's GLM Glyph model deployment."
---
import { GLMGlyphDeployment } from '/src/snippets/autoregressive/glm-glyph-deployment.jsx';
## 1. Model Introduction
[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.
**Hardware Support:** NVIDIA B200/H100/H200, AMD MI300X/MI325X/MI355X
**Key Features:**
- **Advanced Reasoning**: Built-in reasoning capabilities for complex problem-solving
- **Multiple Quantizations**: BF16 and FP8 variants for different performance/memory trade-offs
- **High Performance**: Optimized for both throughput and latency scenarios
**Available Models:**
- **BF16 (Full precision)**: [zai-org/Glyph](https://huggingface.co/zai-org/Glyph)
- **FP8 (8-bit quantized)**: [zai-org/Glyph-FP8](https://huggingface.co/zai-org/Glyph-FP8)
**License:**
Please refer to the [official Glyph model card](https://huggingface.co/zai-org/Glyph) 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.
## 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, and other options.
<GLMGlyphDeployment />
### 3.2 Configuration Tips
- **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.
## 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 Thinking Mode
Glyph supports thinking mode for enhanced reasoning. Enable the reasoning parser during deployment to separate the thinking and content sections:
```shell Command
python -m sglang.launch_server \
--model-path zai-org/Glyph \
--reasoning-parser glm45 \
--tp 4
```
**Streaming with Thinking Process:**
```python Example
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:30000/v1",
api_key="EMPTY"
)
# Enable streaming to see the thinking process in real-time
response = client.chat.completions.create(
model="zai-org/Glyph",
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()
```
**Note:** The reasoning parser captures the model's step-by-step thinking process, allowing you to see how the model arrives at its conclusions.
**Disable Thinking Mode:**
To disable thinking mode for a specific request:
```python Example
response = client.chat.completions.create(
model="zai-org/Glyph",
messages=[{"role": "user", "content": "What is the capital of France?"}],
extra_body={"chat_template_kwargs": {"enable_thinking": False}}
)
```
#### 4.2.2 Tool Calling
Glyph supports tool calling capabilities. Enable the tool call parser:
```shell Command
python -m sglang.launch_server \
--model-path zai-org/Glyph \
--reasoning-parser glm45 \
--tool-call-parser glm45 \
--tp 4
```
**Python Example (with Thinking Process):**
```python Example
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:30000/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/Glyph",
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
tool_calls_accumulator = {}
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)
# Accumulate 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 =================\n", flush=True)
thinking_started = False
for tool_call in delta.tool_calls:
index = tool_call.index
if index not in tool_calls_accumulator:
tool_calls_accumulator[index] = {
'name': None,
'arguments': ''
}
if tool_call.function:
if tool_call.function.name:
tool_calls_accumulator[index]['name'] = tool_call.function.name
if tool_call.function.arguments:
tool_calls_accumulator[index]['arguments'] += tool_call.function.arguments
# Print content
if delta.content:
print(delta.content, end="", flush=True)
# Print accumulated tool calls
for index, tool_call in sorted(tool_calls_accumulator.items()):
print(f"Tool Call: {tool_call['name']}")
print(f" Arguments: {tool_call['arguments']}")
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/Glyph",
messages=messages,
temperature=0.7
)
print(final_response.choices[0].message.content)
# Output: "The weather in Beijing is currently 22°C and sunny."
```
## 5. Benchmark
This section uses **industry-standard configurations** for comparable benchmark results.
### 5.1 Speed Benchmark
**Test Environment:**
- Model: Glyph
- SGLang Version: 0.5.6.post1
**Benchmark Methodology:**
We use industry-standard benchmark configurations to ensure results are comparable across frameworks and hardware platforms.
#### 5.1.1 Standard Scenario Benchmark
- **Model Deployment**
```bash Command
python -m sglang.launch_server \
--model zai-org/Glyph \
--tp 2
```
##### 5.1.1.1 Low 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 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): 17.03
Total input tokens: 6101
Total input text tokens: 6101
Total input vision tokens: 0
Total generated tokens: 4220
Total generated tokens (retokenized): 4220
Request throughput (req/s): 0.59
Input token throughput (tok/s): 358.17
Output token throughput (tok/s): 247.74
Peak output token throughput (tok/s): 251.00
Peak concurrent requests: 3
Total token throughput (tok/s): 605.91
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 1702.14
Median E2E Latency (ms): 1361.72
---------------Time to First Token----------------
Mean TTFT (ms): 22.35
Median TTFT (ms): 22.61
P99 TTFT (ms): 23.76
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 3.99
Median TPOT (ms): 3.99
P99 TPOT (ms): 4.01
---------------Inter-Token Latency----------------
Mean ITL (ms): 3.99
Median ITL (ms): 3.99
P95 ITL (ms): 4.03
P99 ITL (ms): 4.12
Max ITL (ms): 7.46
==================================================
```
##### 5.1.1.2 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 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): 16.27
Total input tokens: 39668
Total input text tokens: 39668
Total input vision tokens: 0
Total generated tokens: 40805
Total generated tokens (retokenized): 40804
Request throughput (req/s): 4.92
Input token throughput (tok/s): 2438.06
Output token throughput (tok/s): 2507.94
Peak output token throughput (tok/s): 3069.00
Peak concurrent requests: 26
Total token throughput (tok/s): 4946.00
Concurrency: 13.44
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 2733.43
Median E2E Latency (ms): 2892.98
---------------Time to First Token----------------
Mean TTFT (ms): 33.10
Median TTFT (ms): 27.73
P99 TTFT (ms): 49.34
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 5.33
Median TPOT (ms): 5.39
P99 TPOT (ms): 5.86
---------------Inter-Token Latency----------------
Mean ITL (ms): 5.30
Median ITL (ms): 4.89
P95 ITL (ms): 5.54
P99 ITL (ms): 21.17
Max ITL (ms): 25.14
==================================================
```
##### 5.1.1.3 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 1000 \
--num-prompts 500 \
--max-concurrency 100 \
--request-rate inf
```
- **Test Results**:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 100
Successful requests: 500
Benchmark duration (s): 25.67
Total input tokens: 249831
Total input text tokens: 249831
Total input vision tokens: 0
Total generated tokens: 252662
Total generated tokens (retokenized): 252657
Request throughput (req/s): 19.48
Input token throughput (tok/s): 9733.69
Output token throughput (tok/s): 9843.99
Peak output token throughput (tok/s): 13398.00
Peak concurrent requests: 127
Total token throughput (tok/s): 19577.68
Concurrency: 89.49
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 4593.75
Median E2E Latency (ms): 4431.03
---------------Time to First Token----------------
Mean TTFT (ms): 48.66
Median TTFT (ms): 35.88
P99 TTFT (ms): 120.61
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 9.10
Median TPOT (ms): 9.55
P99 TPOT (ms): 11.00
---------------Inter-Token Latency----------------
Mean ITL (ms): 9.01
Median ITL (ms): 6.51
P95 ITL (ms): 23.19
P99 ITL (ms): 25.54
Max ITL (ms): 52.93
==================================================
```
#### 5.1.2 Reasoning Scenario Benchmark
##### 5.1.2.1 Low 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 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): 201.53
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
```
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