GLM Glyph
1. Model Introduction
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
- FP8 (8-bit quantized): zai-org/Glyph-FP8
License:
Please refer to the official Glyph model card 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 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.
Hardware Platform
Quantization
Reasoning Parser
Tool Call Parser
Run this Command:
python -m sglang.launch_server \ --model zai-org/Glyph \ --tp 4 \ --reasoning-parser glm45 \ --tool-call-parser glm45
3.2 Configuration Tips
For more detailed configuration tips, please refer to GLM-4.5/GLM-4.6 Usage.
4. Model Invocation
4.1 Basic Usage
For basic API usage and request examples, please refer to:
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:
python -m sglang.launch_server \
--model-path zai-org/Glyph \
--reasoning-parser glm45 \
--tp 4
Streaming with Thinking Process:
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:
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:
python -m sglang.launch_server \
--model-path zai-org/Glyph \
--reasoning-parser glm45 \
--tool-call-parser glm45 \
--tp 4
Python Example (with Thinking Process):
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:
=============== 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:
# 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
python -m sglang.launch_server \
--model zai-org/Glyph \
--tp 2
5.1.1.1 Low Concurrency
- Benchmark 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:
============ 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:
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:
============ 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:
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:
============ 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:
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:
============ 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:
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:
============ 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:
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:
============ 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:
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:
============ 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:
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:
============ 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:
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:
============ 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
python -m sglang.test.few_shot_gsm8k \
--num-questions 200
- Test Result
Accuracy: 0.890
Invalid: 0.000
Latency: 3.718 s
Output throughput: 5245.606 token/s