GLM-4.6

1. Model Introduction

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.

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, deployment strategy, and thinking capabilities.

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:

• SGLang Basic Usage Guide

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:

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:

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:

=============== 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

GLM-4.6 supports tool calling capabilities. Enable the tool call parser:

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):

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:

=============== 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/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."

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

python -m sglang.launch_server \
  --model zai-org/GLM-4.6 \
  --tp 8

• Low Concurrency (Latency-Optimized)

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

============ 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)

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

============ 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)

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

============ 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

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

============ 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

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

============ 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

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

============ 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

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

============ 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

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

============ 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

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

============ 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

python -m sglang.test.few_shot_gsm8k \
  --num-questions 200 \
  --port 30000

• Test Result

Accuracy: 0.975
Invalid: 0.000
Latency: 16.574 s
Output throughput: 1194.637 token/s