---
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";

<GLM46Deployment />

### 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

<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-4.6 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:

<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>
  </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)

#### 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
```
