minke.yu/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 10 Packages Projects Releases Wiki Activity
Files
b081dd3d234b30029fabba7dc358d76834a40e3a
sglang/docs/cookbook/autoregressive/Qwen/Qwen3.mdx
T

886 lines
32 KiB
Plaintext
Raw Blame History

---
title: Qwen3
metatags:
description: "Deploy Qwen3 series models with SGLang - featuring advanced reasoning, 256K context, and flexible Dense/MoE architectures for edge to cloud."
---
## 1. Model Introduction
[Qwen3 series](https://github.com/QwenLM/Qwen3) are the most powerful vision-language models in the Qwen series to date, featuring advanced capabilities in multi-modal understanding, reasoning, and agentic applications.
This generation delivers comprehensive upgrades across the board:
- **Stronger general intelligence**: Significant improvements in instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage.
- **Broader multilingual knowledge**: Substantial gains in long-tail knowledge coverage across multiple languages.
- **More helpful & aligned responses**: Markedly better alignment with user preferences in subjective and open-ended tasks, enabling higher-quality, more useful text generation.
- **Extended context length**: Enhanced capabilities in understanding and reasoning over 256K-token long contexts.
- **Stronger agent interaction capabilities**: Improved tool use and search-based agent performance.
- **Flexible deployment options**: Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning-enhanced Thinking editions.
For more details, please refer to the [official Qwen3 GitHub Repository](https://github.com/QwenLM/Qwen3).
## 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
The Qwen3 series offers models in various sizes and architectures, optimized for different hardware platforms including NVIDIA GPUs, AMD GPUs, Intel Arc Pro B-Series GPUs(codename: BMG (Battlemage)), and Intel Xeon CPUs. The recommended launch configurations vary by hardware and model size.
**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, model size, quantization method, and thinking capabilities.
import { Qwen3Deployment } from "/src/snippets/autoregressive/qwen3-deployment.jsx";
<Qwen3Deployment />
### 3.2 Configuration Tips
- **Memory Management:** Set lower `--context-length` to conserve memory. A value of `128000` is sufficient for most scenarios, down from the default 262K.
- **Expert Parallelism:** SGLang supports Expert Parallelism (EP) via `--ep`, allowing experts in MoE models to be deployed on separate GPUs for better throughput. One thing to note is that, for quantized models, you need to set `--ep` to a value that satisfies the requirement: `(moe_intermediate_size / moe_tp_size) % weight_block_size_n == 0, where moe_tp_size is equal to tp_size divided by ep_size.` Note that EP may perform worse in low concurrency scenarios due to additional communication overhead. Check out [Expert Parallelism Deployment](../../../docs/advanced_features/expert_parallelism) for more details.
- **Kernel Tuning:** For MoE Triton kernel tuning on your specific hardware, refer to [fused_moe_triton](https://github.com/sgl-project/sglang/tree/main/benchmark/kernels/fused_moe_triton).
- **Speculative Decoding:** Using Speculative Decoding for latency-sensitive scenarios.
- `--speculative-algorithm EAGLE3`: Speculative decoding algorithm
- `--speculative-num-steps 3`: Number of speculative verification rounds
- `--speculative-eagle-topk 1`: Top-k sampling for draft tokens
- `--speculative-num-draft-tokens 4`: Number of draft tokens per step
- `--speculative-draft-model-path`: The path of the draft model weights. This can be a local folder or a Hugging Face repo ID such as [`lmsys/SGLang-EAGLE3-Qwen3-235B-A22B-Instruct-2507-SpecForge-Meituan`](https://huggingface.co/lmsys/SGLang-EAGLE3-Qwen3-235B-A22B-Instruct-2507-SpecForge-Meituan).
- **Xeon CPU service configuration:** Please refer to the `Notes` part in the serving engine launching section in [the SGLang CPU server document](../../../docs/hardware-platforms/cpu_server#launch-of-the-serving-engine) to better understand how to configure the arguments, especially for TP (tensor parallel) and NUMA binding settings.
## 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)
- [SGLang OpenAI Vision API Guide](../../../docs/basic_usage/openai_api_vision)
### 4.2 Advanced Usage
#### 4.2.1 Reasoning Parser
Qwen3-235B-A22B supports reasoning mode. Enable the reasoning parser during deployment to separate the thinking and content sections:
```shell Command
python -m sglang.launch_server \
--model Qwen/Qwen3-235B-A22B-Thinking-2507 \
--reasoning-parser qwen3 \
--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="Qwen/Qwen3-235B-A22B-Thinking-2507",
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 =================
Okay, so I need to figure out what 15% of 240 is. Hmm, percentages can sometimes trip me up, but I think I remember some basics. Let me start by recalling that "percent" means "per hundred," so 15% is the same as 15 per 100, or 15/100. So, maybe I can convert 15% into a decimal first? Yeah, I think that's a common method.
...
So conclusion: The answer is 36.
=============== Content =================
To determine what 15% of 240 is, we can follow a systematic approach that involves converting the percentage to a decimal and then performing multiplication. Here's a step-by-step breakdown of the solution:
....
### Final Answer:
$$
\boxed{36}
$$
Thus, 15% of 240 is **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.
#### 4.2.3 Tool Calling
Qwen3 supports tool calling capabilities. Enable the tool call parser:
```shell Command
python -m sglang.launch_server \
--model Qwen/Qwen3-235B-A22B-Thinking-2507 \
--reasoning-parser qwen3 \
--tool-call-parser qwen25 \
--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="Qwen/Qwen3-235B-A22B-Thinking-2507",
messages=[
{"role": "user", "content": "What's the weather in Beijing?"}
],
tools=tools,
temperature=0.7,
stream=True
)
# Process streaming response
thinking_started = False
has_thinking = False
tool_calls_accumulator = {}
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
# Print thinking process
if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
if not thinking_started:
print("=============== Thinking =================", flush=True)
thinking_started = True
has_thinking = True
print(delta.reasoning_content, end="", flush=True)
# Accumulate tool calls
if hasattr(delta, 'tool_calls') and delta.tool_calls:
# Close thinking section if needed
if has_thinking and thinking_started:
print("\n=============== Content =================\n", flush=True)
thinking_started = False
for tool_call in delta.tool_calls:
index = tool_call.index
if index not in tool_calls_accumulator:
tool_calls_accumulator[index] = {
'name': None,
'arguments': ''
}
if tool_call.function:
if tool_call.function.name:
tool_calls_accumulator[index]['name'] = tool_call.function.name
if tool_call.function.arguments:
tool_calls_accumulator[index]['arguments'] += tool_call.function.arguments
# Print content
if delta.content:
print(delta.content, end="", flush=True)
# Print accumulated tool calls
for index, tool_call in sorted(tool_calls_accumulator.items()):
print(f"🔧 Tool Call: {tool_call['name']}")
print(f" Arguments: {tool_call['arguments']}")
print()
```
**Output Example:**
```text Output
=============== Thinking =================
Okay, the user is asking for the weather in Beijing. Let me check the tools available. There's a function called get_weather that takes location and unit parameters. The location is required, so I need to specify Beijing as the location. The unit is optional and can be either celsius or fahrenheit. Since the user didn't specify the unit, maybe I should default to a common one. In China, they usually use celsius, so I'll set unit to celsius. I'll call the get_weather function with location: Beijing and unit: celsius. That should get the current weather for them.
=============== 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="Qwen/Qwen3-235B-A22B-Thinking-2507",
messages=messages,
temperature=0.7
)
print(final_response.choices[0].message.content)
# Output: "The current weather in Beijing is **22°C** and **sunny**. A perfect day to enjoy outdoor activities! 🌞"
```
## 5. Benchmark
### 5.1 Speed Benchmark
**Test Environment:**
- Hardware: NVIDIA B200 GPU (8x)
- Model: Qwen3-235B-A22B-Instruct-2507
- Tensor Parallelism: 8
- sglang version: 0.5.6
We use SGLang's built-in benchmarking tool to conduct performance evaluation on the [ShareGPT_Vicuna_unfiltered](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) dataset. This dataset contains real conversation data and can better reflect performance in actual use scenarios.
#### 5.1.1 Standard Scenario Benchmark
- Model Deployment Command:
```shell Command
python -m sglang.launch_server \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--tp 8
```
##### 5.1.1.1 Low Concurrency
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 10 \
--max-concurrency 1
```
- Test Results:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 1
Successful requests: 10
Benchmark duration (s): 43.56
Total input tokens: 6101
Total input text tokens: 6101
Total input vision tokens: 0
Total generated tokens: 4210
Total generated tokens (retokenized): 4206
Request throughput (req/s): 0.23
Input token throughput (tok/s): 140.07
Output token throughput (tok/s): 96.65
Peak output token throughput (tok/s): 100.00
Peak concurrent requests: 2
Total token throughput (tok/s): 236.72
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 4353.63
Median E2E Latency (ms): 3475.79
---------------Time to First Token----------------
Mean TTFT (ms): 99.03
Median TTFT (ms): 92.18
P99 TTFT (ms): 166.05
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 10.12
Median TPOT (ms): 10.12
P99 TPOT (ms): 10.15
---------------Inter-Token Latency----------------
Mean ITL (ms): 10.13
Median ITL (ms): 10.12
P95 ITL (ms): 10.49
P99 ITL (ms): 10.70
Max ITL (ms): 13.45
==================================================
```
##### 5.1.1.2 Medium Concurrency
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 80 \
--max-concurrency 16
```
- Test Results:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 16
Successful requests: 80
Benchmark duration (s): 48.95
Total input tokens: 39668
Total input text tokens: 39668
Total input vision tokens: 0
Total generated tokens: 40725
Total generated tokens (retokenized): 40716
Request throughput (req/s): 1.63
Input token throughput (tok/s): 810.44
Output token throughput (tok/s): 832.04
Peak output token throughput (tok/s): 1151.00
Peak concurrent requests: 21
Total token throughput (tok/s): 1642.48
Concurrency: 13.61
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 8326.72
Median E2E Latency (ms): 8827.86
---------------Time to First Token----------------
Mean TTFT (ms): 215.70
Median TTFT (ms): 88.82
P99 TTFT (ms): 727.08
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 16.36
Median TPOT (ms): 16.12
P99 TPOT (ms): 24.09
---------------Inter-Token Latency----------------
Mean ITL (ms): 15.96
Median ITL (ms): 14.52
P95 ITL (ms): 16.04
P99 ITL (ms): 67.69
Max ITL (ms): 457.52
==================================================
```
##### 5.1.1.3 High Concurrency
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 500 \
--max-concurrency 100
```
- Test Results:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 100
Successful requests: 500
Benchmark duration (s): 92.07
Total input tokens: 249831
Total input text tokens: 249831
Total input vision tokens: 0
Total generated tokens: 252162
Total generated tokens (retokenized): 251124
Request throughput (req/s): 5.43
Input token throughput (tok/s): 2713.46
Output token throughput (tok/s): 2738.78
Peak output token throughput (tok/s): 4400.00
Peak concurrent requests: 110
Total token throughput (tok/s): 5452.24
Concurrency: 90.50
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 16665.09
Median E2E Latency (ms): 16060.10
---------------Time to First Token----------------
Mean TTFT (ms): 260.55
Median TTFT (ms): 122.68
P99 TTFT (ms): 863.11
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 32.94
Median TPOT (ms): 34.04
P99 TPOT (ms): 41.19
---------------Inter-Token Latency----------------
Mean ITL (ms): 32.59
Median ITL (ms): 23.54
P95 ITL (ms): 69.79
P99 ITL (ms): 119.09
Max ITL (ms): 577.70
==================================================
```
#### 5.1.2 Reasoning Scenario Benchmark
- Model Deployment Command:
```shell Command
python -m sglang.launch_server \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--tp 8
```
##### 5.1.2.1 Low Concurrency
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 8000 \
--num-prompts 10 \
--max-concurrency 1
```
- Test Results:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 1
Successful requests: 10
Benchmark duration (s): 457.45
Total input tokens: 6101
Total input text tokens: 6101
Total input vision tokens: 0
Total generated tokens: 44452
Total generated tokens (retokenized): 44059
Request throughput (req/s): 0.02
Input token throughput (tok/s): 13.34
Output token throughput (tok/s): 97.17
Peak output token throughput (tok/s): 100.00
Peak concurrent requests: 2
Total token throughput (tok/s): 110.51
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 45742.42
Median E2E Latency (ms): 49266.87
---------------Time to First Token----------------
Mean TTFT (ms): 110.60
Median TTFT (ms): 109.36
P99 TTFT (ms): 167.43
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 10.23
Median TPOT (ms): 10.24
P99 TPOT (ms): 10.32
---------------Inter-Token Latency----------------
Mean ITL (ms): 10.27
Median ITL (ms): 10.26
P95 ITL (ms): 10.71
P99 ITL (ms): 10.97
Max ITL (ms): 15.79
==================================================
```
##### 5.1.2.2 Medium Concurrency
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 8000 \
--num-prompts 80 \
--max-concurrency 16
```
- Test Results:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 16
Successful requests: 80
Benchmark duration (s): 340.17
Total input tokens: 39668
Total input text tokens: 39668
Total input vision tokens: 0
Total generated tokens: 318226
Total generated tokens (retokenized): 318104
Request throughput (req/s): 0.24
Input token throughput (tok/s): 116.61
Output token throughput (tok/s): 935.49
Peak output token throughput (tok/s): 1120.00
Peak concurrent requests: 19
Total token throughput (tok/s): 1052.10
Concurrency: 13.85
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 58885.30
Median E2E Latency (ms): 59238.70
---------------Time to First Token----------------
Mean TTFT (ms): 169.71
Median TTFT (ms): 101.61
P99 TTFT (ms): 455.71
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 14.82
Median TPOT (ms): 14.91
P99 TPOT (ms): 15.20
---------------Inter-Token Latency----------------
Mean ITL (ms): 14.76
Median ITL (ms): 14.63
P95 ITL (ms): 15.46
P99 ITL (ms): 16.62
Max ITL (ms): 104.94
==================================================
```
##### 5.1.2.3 High Concurrency
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 8000 \
--num-prompts 320 \
--max-concurrency 64
```
- Test Results:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 64
Successful requests: 320
Benchmark duration (s): 544.83
Total input tokens: 158939
Total input text tokens: 158939
Total input vision tokens: 0
Total generated tokens: 1300705
Total generated tokens (retokenized): 1293015
Request throughput (req/s): 0.59
Input token throughput (tok/s): 291.72
Output token throughput (tok/s): 2387.34
Peak output token throughput (tok/s): 3008.00
Peak concurrent requests: 68
Total token throughput (tok/s): 2679.06
Concurrency: 56.35
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 95937.70
Median E2E Latency (ms): 99362.32
---------------Time to First Token----------------
Mean TTFT (ms): 265.03
Median TTFT (ms): 129.11
P99 TTFT (ms): 823.85
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 23.66
Median TPOT (ms): 24.07
P99 TPOT (ms): 24.97
---------------Inter-Token Latency----------------
Mean ITL (ms): 23.54
Median ITL (ms): 23.07
P95 ITL (ms): 25.92
P99 ITL (ms): 63.87
Max ITL (ms): 408.30
==================================================
```
#### 5.1.3 Summarization Scenario Benchmark
##### 5.1.3.1 Low Concurrency
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--dataset-name random \
--random-input-len 8000 \
--random-output-len 1000 \
--num-prompts 10 \
--max-concurrency 1
```
- Test Results:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 1
Successful requests: 10
Benchmark duration (s): 44.82
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): 0.22
Input token throughput (tok/s): 935.86
Output token throughput (tok/s): 93.94
Peak output token throughput (tok/s): 99.00
Peak concurrent requests: 2
Total token throughput (tok/s): 1029.80
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 4479.60
Median E2E Latency (ms): 3622.99
---------------Time to First Token----------------
Mean TTFT (ms): 139.90
Median TTFT (ms): 114.85
P99 TTFT (ms): 225.17
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 10.31
Median TPOT (ms): 10.33
P99 TPOT (ms): 10.51
---------------Inter-Token Latency----------------
Mean ITL (ms): 10.33
Median ITL (ms): 10.33
P95 ITL (ms): 10.73
P99 ITL (ms): 10.93
Max ITL (ms): 14.48
==================================================
```
##### 5.1.3.2 Medium Concurrency
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--dataset-name random \
--random-input-len 8000 \
--random-output-len 1000 \
--num-prompts 80 \
--max-concurrency 16
```
- Test Results:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 16
Successful requests: 80
Benchmark duration (s): 50.68
Total input tokens: 300020
Total input text tokens: 300020
Total input vision tokens: 0
Total generated tokens: 41589
Total generated tokens (retokenized): 41578
Request throughput (req/s): 1.58
Input token throughput (tok/s): 5920.41
Output token throughput (tok/s): 820.69
Peak output token throughput (tok/s): 1200.00
Peak concurrent requests: 20
Total token throughput (tok/s): 6741.10
Concurrency: 13.90
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 8805.54
Median E2E Latency (ms): 9368.79
---------------Time to First Token----------------
Mean TTFT (ms): 284.29
Median TTFT (ms): 168.48
P99 TTFT (ms): 1027.21
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 16.81
Median TPOT (ms): 16.66
P99 TPOT (ms): 27.18
---------------Inter-Token Latency----------------
Mean ITL (ms): 16.42
Median ITL (ms): 13.68
P95 ITL (ms): 17.23
P99 ITL (ms): 90.75
Max ITL (ms): 574.64
==================================================
```
##### 5.1.3.3 High Concurrency
- Benchmark Command:
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--model Qwen/Qwen3-235B-A22B-Instruct-2507 \
--dataset-name random \
--random-input-len 8000 \
--random-output-len 1000 \
--num-prompts 320 \
--max-concurrency 64
```
- Test Results:
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 64
Successful requests: 320
Benchmark duration (s): 94.77
Total input tokens: 1273893
Total input text tokens: 1273893
Total input vision tokens: 0
Total generated tokens: 169680
Total generated tokens (retokenized): 169640
Request throughput (req/s): 3.38
Input token throughput (tok/s): 13441.86
Output token throughput (tok/s): 1790.43
Peak output token throughput (tok/s): 2687.00
Peak concurrent requests: 70
Total token throughput (tok/s): 15232.28
Concurrency: 58.63
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 17364.14
Median E2E Latency (ms): 17495.95
---------------Time to First Token----------------
Mean TTFT (ms): 238.22
Median TTFT (ms): 203.27
P99 TTFT (ms): 510.48
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 32.50
Median TPOT (ms): 34.27
P99 TPOT (ms): 40.59
---------------Inter-Token Latency----------------
Mean ITL (ms): 32.36
Median ITL (ms): 22.50
P95 ITL (ms): 97.81
P99 ITL (ms): 151.55
Max ITL (ms): 352.79
==================================================
```
### 5.2 Accuracy Benchmark
#### 5.2.1 GSM8K Benchmark
- **Benchmark Command:**
```shell Command
python3 -m sglang.test.few_shot_gsm8k --num-questions 200
```
- **Results**:
- Qwen/Qwen3-235B-A22B-Instruct-2507
```text Output
Accuracy: 0.945
Invalid: 0.000
Latency: 11.980 s
Output throughput: 2358.105 token/s
```
Reference in New Issue View Git Blame Copy Permalink