---
title: GLM-4.6
metatags:
description: "Deploy GLM-4.6 with SGLang - 200K context window, superior coding, advanced reasoning, and enhanced agentic capabilities."
---
## 1. Model Introduction
[GLM-4.6](https://huggingface.co/zai-org/GLM-4.6) is a powerful language model developed by Zhipu AI, featuring advanced capabilities in reasoning, function calling, and multi-modal understanding.
As the latest iteration in the GLM series, GLM-4.6 achieves comprehensive enhancements across multiple domains, including real-world coding, long-context processing, reasoning, searching, writing, and agentic applications. Details are as follows:
- **Longer context window**: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.
- **Superior coding performance**: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code, Cline, Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.
- **Advanced reasoning**: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.
- **More capable agents**: GLM-4.6 exhibits stronger performance in tool use and search-based agents, and integrates more effectively within agent frameworks.
- **Refined writing**: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.
For more details, please refer to the [official GLM-4.6 documentation](https://docs.z.ai/guides/llm/glm-4.6).
## 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, deployment strategy, and thinking capabilities.
import { GLM46Deployment } from "/src/snippets/autoregressive/glm-46-deployment.jsx";
### 3.2 Configuration Tips
- **EAGLE Speculative Decoding:** Supported for GLM-4.5/4.6. 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.
- **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).
## 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-4.6 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-4.6 \
--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-4.6",
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
**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.
GLM-4.6 supports tool calling capabilities. Enable the tool call parser:
```shell Command
python -m sglang.launch_server \
--model zai-org/GLM-4.6 \
--reasoning-parser glm45 \
--tool-call-parser glm45 \
--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-4.6",
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-4.6",
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-4.6",
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 GPU (8x), AMD MI300X (8x), AMD MI325X (8x), AMD MI355X (8x)
- Model: GLM-4.6
- Tensor Parallelism: 8
- 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 Test Scenarios
Three core scenarios reflect real-world usage patterns:
Scenario
Input Length
Output Length
Use Case
**Chat**
1K
1K
Most common conversational AI workload
**Reasoning**
1K
8K
Long-form generation, complex reasoning tasks
**Summarization**
8K
1K
Document summarization, RAG retrieval
#### 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)
#### 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-4.6 \
--tp 8
```
- Low Concurrency (Latency-Optimized)
```bash Command
python -m sglang.bench_serving \
--backend sglang \
--model zai-org/GLM-4.6 \
--dataset-name random \
--random-input-len 1000 \
--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): 63.82
Total input tokens: 6101
Total input text tokens: 6101
Total input vision tokens: 0
Total generated tokens: 4210
Total generated tokens (retokenized): 4209
Request throughput (req/s): 0.16
Input token throughput (tok/s): 95.60
Output token throughput (tok/s): 65.97
Peak output token throughput (tok/s): 68.00
Peak concurrent requests: 2
Total token throughput (tok/s): 161.57
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 6379.24
Median E2E Latency (ms): 5085.00
---------------Time to First Token----------------
Mean TTFT (ms): 155.57
Median TTFT (ms): 149.79
P99 TTFT (ms): 207.69
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 14.81
Median TPOT (ms): 14.80
P99 TPOT (ms): 14.84
---------------Inter-Token Latency----------------
Mean ITL (ms): 14.82
Median ITL (ms): 14.82
P95 ITL (ms): 15.17
P99 ITL (ms): 15.36
Max ITL (ms): 25.05
==================================================
```
- Medium Concurrency (Balanced)
```bash Command
python -m sglang.bench_serving \
--backend sglang \
--model zai-org/GLM-4.6 \
--dataset-name random \
--random-input-len 1000 \
--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): 72.06
Total input tokens: 39668
Total input text tokens: 39668
Total input vision tokens: 0
Total generated tokens: 40725
Total generated tokens (retokenized): 40672
Request throughput (req/s): 1.11
Input token throughput (tok/s): 550.47
Output token throughput (tok/s): 565.14
Peak output token throughput (tok/s): 752.00
Peak concurrent requests: 20
Total token throughput (tok/s): 1115.61
Concurrency: 13.71
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 12348.93
Median E2E Latency (ms): 13164.81
---------------Time to First Token----------------
Mean TTFT (ms): 196.08
Median TTFT (ms): 155.22
P99 TTFT (ms): 377.98
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 24.24
Median TPOT (ms): 24.55
P99 TPOT (ms): 30.42
---------------Inter-Token Latency----------------
Mean ITL (ms): 23.92
Median ITL (ms): 21.40
P95 ITL (ms): 22.49
P99 ITL (ms): 123.83
Max ITL (ms): 486.54
==================================================
```
- High Concurrency (Throughput-Optimized)
```bash Command
python -m sglang.bench_serving \
--backend sglang \
--model zai-org/GLM-4.6 \
--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): 138.50
Total input tokens: 249831
Total input text tokens: 249831
Total input vision tokens: 0
Total generated tokens: 252162
Total generated tokens (retokenized): 251841
Request throughput (req/s): 3.61
Input token throughput (tok/s): 1803.78
Output token throughput (tok/s): 1820.61
Peak output token throughput (tok/s): 2900.00
Peak concurrent requests: 107
Total token throughput (tok/s): 3624.40
Concurrency: 90.91
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 25183.97
Median E2E Latency (ms): 23968.49
---------------Time to First Token----------------
Mean TTFT (ms): 337.77
Median TTFT (ms): 180.65
P99 TTFT (ms): 906.14
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 49.97
Median TPOT (ms): 52.20
P99 TPOT (ms): 61.81
---------------Inter-Token Latency----------------
Mean ITL (ms): 49.36
Median ITL (ms): 35.05
P95 ITL (ms): 124.91
P99 ITL (ms): 187.69
Max ITL (ms): 440.34
==================================================
```
**Scenario 2: Reasoning (1K/8K)**
- Low Concurrency
```bash Command
python -m sglang.bench_serving \
--backend sglang \
--model zai-org/GLM-4.6 \
--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): 666.64
Total input tokens: 6101
Total input text tokens: 6101
Total input vision tokens: 0
Total generated tokens: 44452
Total generated tokens (retokenized): 44387
Request throughput (req/s): 0.02
Input token throughput (tok/s): 9.15
Output token throughput (tok/s): 66.68
Peak output token throughput (tok/s): 68.00
Peak concurrent requests: 2
Total token throughput (tok/s): 75.83
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 66661.35
Median E2E Latency (ms): 71902.36
---------------Time to First Token----------------
Mean TTFT (ms): 160.21
Median TTFT (ms): 140.32
P99 TTFT (ms): 295.56
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 14.92
Median TPOT (ms): 14.94
P99 TPOT (ms): 15.02
---------------Inter-Token Latency----------------
Mean ITL (ms): 14.96
Median ITL (ms): 14.96
P95 ITL (ms): 15.36
P99 ITL (ms): 15.57
Max ITL (ms): 19.06
==================================================
```
- Medium Concurrency
```bash Command
python -m sglang.bench_serving \
--backend sglang \
--model zai-org/GLM-4.6 \
--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): 503.30
Total input tokens: 39668
Total input text tokens: 39668
Total input vision tokens: 0
Total generated tokens: 318226
Total generated tokens (retokenized): 318025
Request throughput (req/s): 0.16
Input token throughput (tok/s): 78.82
Output token throughput (tok/s): 632.28
Peak output token throughput (tok/s): 752.00
Peak concurrent requests: 19
Total token throughput (tok/s): 711.09
Concurrency: 13.88
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 87349.22
Median E2E Latency (ms): 88248.04
---------------Time to First Token----------------
Mean TTFT (ms): 228.54
Median TTFT (ms): 142.78
P99 TTFT (ms): 569.84
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 21.97
Median TPOT (ms): 22.14
P99 TPOT (ms): 22.47
---------------Inter-Token Latency----------------
Mean ITL (ms): 21.91
Median ITL (ms): 21.80
P95 ITL (ms): 22.30
P99 ITL (ms): 22.78
Max ITL (ms): 137.19
==================================================
```
- High Concurrency
```bash Command
python -m sglang.bench_serving \
--backend sglang \
--model zai-org/GLM-4.6 \
--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): 772.28
Total input tokens: 158939
Total input text tokens: 158939
Total input vision tokens: 0
Total generated tokens: 1300705
Total generated tokens (retokenized): 1299924
Request throughput (req/s): 0.41
Input token throughput (tok/s): 205.80
Output token throughput (tok/s): 1684.24
Peak output token throughput (tok/s): 2112.00
Peak concurrent requests: 68
Total token throughput (tok/s): 1890.05
Concurrency: 56.17
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 135563.36
Median E2E Latency (ms): 140888.88
---------------Time to First Token----------------
Mean TTFT (ms): 232.45
Median TTFT (ms): 145.59
P99 TTFT (ms): 576.49
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 33.47
Median TPOT (ms): 34.02
P99 TPOT (ms): 35.10
---------------Inter-Token Latency----------------
Mean ITL (ms): 33.30
Median ITL (ms): 32.63
P95 ITL (ms): 34.27
P99 ITL (ms): 104.39
Max ITL (ms): 155.65
==================================================
```
**Scenario 3: Summarization (8K/1K)**
- Low
```bash Command
python -m sglang.bench_serving \
--backend sglang \
--model zai-org/GLM-4.6 \
--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): 65.11
Total input tokens: 41941
Total input text tokens: 41941
Total input vision tokens: 0
Total generated tokens: 4210
Total generated tokens (retokenized): 4210
Request throughput (req/s): 0.15
Input token throughput (tok/s): 644.17
Output token throughput (tok/s): 64.66
Peak output token throughput (tok/s): 68.00
Peak concurrent requests: 2
Total token throughput (tok/s): 708.83
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 6508.31
Median E2E Latency (ms): 5263.36
---------------Time to First Token----------------
Mean TTFT (ms): 189.48
Median TTFT (ms): 159.23
P99 TTFT (ms): 304.09
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 15.02
Median TPOT (ms): 15.03
P99 TPOT (ms): 15.27
---------------Inter-Token Latency----------------
Mean ITL (ms): 15.04
Median ITL (ms): 15.03
P95 ITL (ms): 15.46
P99 ITL (ms): 15.65
Max ITL (ms): 24.20
==================================================
```
- Medium Concurrency
```bash Command
python -m sglang.bench_serving \
--backend sglang \
--model zai-org/GLM-4.6 \
--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): 76.43
Total input tokens: 300020
Total input text tokens: 300020
Total input vision tokens: 0
Total generated tokens: 41589
Total generated tokens (retokenized): 41577
Request throughput (req/s): 1.05
Input token throughput (tok/s): 3925.47
Output token throughput (tok/s): 544.15
Peak output token throughput (tok/s): 752.00
Peak concurrent requests: 19
Total token throughput (tok/s): 4469.62
Concurrency: 13.95
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 13329.63
Median E2E Latency (ms): 14141.09
---------------Time to First Token----------------
Mean TTFT (ms): 339.88
Median TTFT (ms): 252.75
P99 TTFT (ms): 906.54
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 25.37
Median TPOT (ms): 25.73
P99 TPOT (ms): 30.94
---------------Inter-Token Latency----------------
Mean ITL (ms): 25.04
Median ITL (ms): 21.68
P95 ITL (ms): 22.69
P99 ITL (ms): 146.98
Max ITL (ms): 483.14
==================================================
```
- High Concurrency
```bash Command
python -m sglang.bench_serving \
--backend sglang \
--model zai-org/GLM-4.6 \
--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): 136.24
Total input tokens: 1273893
Total input text tokens: 1273893
Total input vision tokens: 0
Total generated tokens: 169680
Total generated tokens (retokenized): 169452
Request throughput (req/s): 2.35
Input token throughput (tok/s): 9350.32
Output token throughput (tok/s): 1245.44
Peak output token throughput (tok/s): 1984.00
Peak concurrent requests: 69
Total token throughput (tok/s): 10595.77
Concurrency: 58.46
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 24889.40
Median E2E Latency (ms): 25123.37
---------------Time to First Token----------------
Mean TTFT (ms): 355.82
Median TTFT (ms): 268.84
P99 TTFT (ms): 858.64
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 46.62
Median TPOT (ms): 49.04
P99 TPOT (ms): 58.88
---------------Inter-Token Latency----------------
Mean ITL (ms): 46.36
Median ITL (ms): 32.46
P95 ITL (ms): 135.23
P99 ITL (ms): 204.27
Max ITL (ms): 508.14
==================================================
```
#### 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
```
- Test Result
```text Output
Accuracy: 0.975
Invalid: 0.000
Latency: 16.574 s
Output throughput: 1194.637 token/s
```