[Docs] Rename docs_new/ to docs/ (#32123)
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
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zijiexia
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Claude Opus 4.8
Claude Opus 4.8
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---
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title: DeepSeek-V3.1
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metatags:
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description: "Deploy DeepSeek-V3.1 MoE model with SGLang - hybrid reasoning, improved tool calling, and agentic behavior for complex multi-step tasks."
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---
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## 1. Model Introduction
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[DeepSeek V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) is an advanced Mixture-of-Experts (MoE) large language model developed by DeepSeek, representing a major capability and usability upgrade over DeepSeek V3. As a refined iteration in the DeepSeek V3 family, DeepSeek V3.1 introduces a hybrid reasoning paradigm that supports both fast non-thinking responses and explicit multi-step reasoning, alongside significantly improved tool calling and agentic behavior. The model demonstrates strong performance across reasoning, mathematics, coding, long-context understanding, and real-world agent workflows, benefiting from continued training, alignment optimization, and inference-time refinements. DeepSeek V3.1 is designed to serve as a robust general-purpose foundation model, well suited for conversational AI, structured tool invocation, search-augmented generation, and complex multi-step tasks, while maintaining high efficiency through its sparse MoE architecture.
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**[DeepSeek-V3.1-Terminus](https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus)** is an experimental version designed for general conversations and long-context processing. It features hybrid thinking capabilities, allowing you to toggle between "Think" mode for deliberate reasoning and "Non-Think" mode for faster responses. Recommended for general conversations, long-context processing, and experimental use cases.
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## 2. SGLang Installation
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SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.
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Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
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For SGLang CPU installation, please refer to the [CPU version installation guide](../../../docs/hardware-platforms/cpu_server#installation).
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## 3. Model Deployment
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This section provides a progressive guide from quick deployment to performance optimization, suitable for users at different levels.
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### 3.1 Basic Configuration
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**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, model variant, deployment strategy, and thinking capabilities.
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import { DeepSeekV31Deployment } from "/src/snippets/autoregressive/deepseek-v31-deployment.jsx";
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<DeepSeekV31Deployment />
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### 3.2 Configuration Tips
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DeepSeek-V3.1 shares the same model architecture as DeepSeek-V3, so the same hardware and optimization recommendations apply.
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**Recommended GPU configurations by weight type:**
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<table style={{width: "100%", borderCollapse: "collapse"}}>
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<thead>
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<tr style={{borderBottom: "2px solid #d55816"}}>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, backgroundColor: "rgba(255,255,255,0.02)"}}>Weight Type</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, backgroundColor: "rgba(255,255,255,0.05)"}}>Supported Hardware</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><strong>FP8</strong> (recommended)</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>8× H200, 8× B200, 8× MI300X, 2×8× H100/H800/H20</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><strong>BF16</strong> (upcast from FP8)</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>2×8× H200, 2×8× MI300X, 4×8× H100/H800, 4×8× A100/A800</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><strong>INT8</strong></td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>16× A100/A800, 32× L40S, Xeon 6980P CPU, 4× Atlas 800I A3</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><strong>W4A8 / AWQ / MXFP4 / NVFP4</strong></td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>8× H20/H100, 4× H200; 8× H100/A100; 8/4× MI355X/MI350X; 8/4× B200</td>
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</tr>
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</tbody>
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</table>
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> The official DeepSeek-V3.1 checkpoint is already in FP8 format — do **not** add `--quantization fp8` when serving it.
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**DeepGEMM precompilation (NVIDIA Hopper / Blackwell):** Precompile GEMM kernels before the first server run to avoid JIT overhead (~10 min):
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```bash
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python3 -m sglang.compile_deep_gemm --model deepseek-ai/DeepSeek-V3.1 --tp 8 --trust-remote-code
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```
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DeepGEMM is enabled by default on Hopper/Blackwell and can be disabled with `SGLANG_ENABLE_JIT_DEEPGEMM=0`.
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**Data Parallelism Attention (`--enable-dp-attention`):** Recommended for high-throughput scenarios with large batch sizes. Reduces KV-cache duplication across TP ranks. Use `--enable-dp-attention --tp 8 --dp 8` on a single 8-GPU node. Not recommended for low-latency, small-batch workloads.
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**NCCL timeout:** If model loading is slow and you hit an NCCL timeout, increase it: `--dist-timeout 3600`.
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**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.
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## 4. Model Invocation
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### 4.1 Basic Usage
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For basic API usage and request examples, please refer to:
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- [Basic API Usage](../../../docs/get-started/quickstart)
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### 4.2 Advanced Usage
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#### 4.2.1 Reasoning Parser
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DeepSeek-V3.1 supports reasoning mode. Enable the reasoning parser during deployment to separate the thinking and content sections:
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```shell Command
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python -m sglang.launch_server \
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--model deepseek-ai/DeepSeek-V3.1-Terminus \
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--reasoning-parser deepseek-v3 \
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--tp 8 \
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--host 0.0.0.0 \
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--port 8000
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```
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**Streaming with Thinking Process:**
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```python Example
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8000/v1",
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api_key="EMPTY"
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)
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# Enable streaming to see the thinking process in real-time
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response = client.chat.completions.create(
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model="deepseek-ai/DeepSeek-V3.1-Terminus",
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messages=[
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{"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
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],
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temperature=0.7,
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max_tokens=2048,
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extra_body = {"chat_template_kwargs": {"thinking": True}},
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stream=True
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)
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# Process the stream
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has_thinking = False
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has_answer = False
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thinking_started = False
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for chunk in response:
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if chunk.choices and len(chunk.choices) > 0:
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delta = chunk.choices[0].delta
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# Print thinking process
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
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if not thinking_started:
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print("=============== Thinking =================", flush=True)
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thinking_started = True
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has_thinking = True
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print(delta.reasoning_content, end="", flush=True)
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# Print answer content
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if delta.content:
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# Close thinking section and add content header
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if has_thinking and not has_answer:
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print("\n=============== Content =================", flush=True)
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has_answer = True
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print(delta.content, end="", flush=True)
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print()
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```
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**Output Example:**
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```text Output
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=============== Thinking =================
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First, the problem is asking for 15% of 240. Percent means per hundred, so 15% is the same as 15 out of 100, or 15/100.
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To find a percentage of a number, I can multiply the number by the percentage expressed as a decimal. So, I need to convert 15% to a decimal. To do that, I divide 15 by 100, which gives me 0.15.
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Now, I multiply 0.15 by 240. So, the calculation is 0.15 × 240.
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I can compute this step by step. First, I know that 15% of 100 is 15, but since 240 is larger, I need to adjust. Alternatively, I can think of 10% of 240, which is easy because 10% is just 240 divided by 10, which is 24. Then, 5% is half of 10%, so half of 24 is 12. Therefore, 15% is 10% plus 5%, so 24 plus 12, which equals 36.
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I should also do the multiplication to confirm. 0.15 × 240. I can break it down: 0.15 × 200 = 30, and 0.15 × 40 = 6, so 30 + 6 = 36. Same answer.
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So, 15% of 240 is 36.
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The problem says "step by step," so I should present it clearly.
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=============== Content =================
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To find 15% of 240, follow these steps:
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1. Understand that "percent" means "per hundred," so 15% is equivalent to \( \frac{15}{100} \).
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2. Convert 15% to a decimal by dividing by 100: \( 15\% = \frac{15}{100} = 0.15 \).
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3. Multiply the decimal by 240: \( 0.15 \times 240 \).
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4. Perform the multiplication:
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- \( 0.15 \times 200 = 30 \)
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- \( 0.15 \times 40 = 6 \)
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- Add the results: \( 30 + 6 = 36 \).
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Alternatively, you can find 15% by breaking it into parts:
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- 10% of 240 is \( \frac{10}{100} \times 240 = 0.10 \times 240 = 24 \).
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- 5% of 240 is half of 10%, so \( \frac{24}{2} = 12 \).
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- Add 10% and 5%: \( 24 + 12 = 36 \).
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Thus, 15% of 240 is 36.
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```
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**Note:** The reasoning parser captures the model's step-by-step thinking process, allowing you to see how the model arrives at its conclusions.
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#### 4.2.2 Tool Calling
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DeepSeek-V3.1 and DeepSeek-V3.1-Terminus support tool calling capabilities. Enable the tool call parser:
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**Deployment Command:**
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```shell Command
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python -m sglang.launch_server \
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--model deepseek-ai/DeepSeek-V3.1-Terminus \
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--tool-call-parser deepseekv31 \
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--reasoning-parser deepseek-v3 \
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--chat-template ./examples/chat_template/tool_chat_template_deepseekv31.jinja \
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--tp 8 \
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--host 0.0.0.0 \
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--port 8000
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```
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For DeepSeek-V3.1, use `--tool-call-parser deepseekv31` as well.
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**Python Example (with Thinking Process):**
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```python Example
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8000/v1",
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api_key="EMPTY"
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)
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# Define available tools
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Get the current weather for a location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city name"
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "Temperature unit"
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}
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},
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"required": ["location"]
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}
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}
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}
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]
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# Make request with streaming to see thinking process
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response = client.chat.completions.create(
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model="deepseek-ai/DeepSeek-V3.1-Terminus",
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messages=[
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{"role": "user", "content": "What's the weather in Beijing?"}
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],
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tools=tools,
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extra_body = {"chat_template_kwargs": {"thinking": True}},
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temperature=0.7,
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stream=True
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)
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# Process streaming response
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thinking_started = False
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has_thinking = False
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tool_calls_accumulator = {}
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for chunk in response:
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if chunk.choices and len(chunk.choices) > 0:
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delta = chunk.choices[0].delta
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# Print thinking process
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
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if not thinking_started:
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print("=============== Thinking =================", flush=True)
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thinking_started = True
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has_thinking = True
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print(delta.reasoning_content, end="", flush=True)
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# Accumulate tool calls
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if hasattr(delta, 'tool_calls') and delta.tool_calls:
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# Close thinking section if needed
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if has_thinking and thinking_started:
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print("\n=============== Content =================\n", flush=True)
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thinking_started = False
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for tool_call in delta.tool_calls:
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index = tool_call.index
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if index not in tool_calls_accumulator:
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tool_calls_accumulator[index] = {
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'name': None,
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'arguments': ''
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}
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if tool_call.function:
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if tool_call.function.name:
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tool_calls_accumulator[index]['name'] = tool_call.function.name
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if tool_call.function.arguments:
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tool_calls_accumulator[index]['arguments'] += tool_call.function.arguments
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# Print content
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if delta.content:
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print(delta.content, end="", flush=True)
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# Print accumulated tool calls
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for index, tool_call in sorted(tool_calls_accumulator.items()):
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print(f"🔧 Tool Call: {tool_call['name']}")
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print(f" Arguments: {tool_call['arguments']}")
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print()
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```
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**Output Example:**
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```text Output
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=============== Thinking =================
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Hmm, the user is asking for the weather in Beijing. This is a straightforward request that matches exactly what the weather tool can provide.
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I need to call the get_weather function with Beijing as the location parameter. The user didn't specify a temperature unit, so I'll default to Celsius since that's commonly used in most parts of the world.
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The tool call format needs to be precise - just the city name and unit selection. Once I get the weather data back, I'll present it clearly to the user.I'll check the weather in Beijing for you.
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=============== Content =================
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🔧 Tool Call: get_weather
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Arguments: {"location": "Beijing", "unit": "celsius"}
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```
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**Note:**
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- The reasoning parser shows how the model decides to use a tool
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- Tool calls are clearly marked with the function name and arguments
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- You can then execute the function and send the result back to continue the conversation
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**Handling Tool Call Results:**
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Please attach the code blocks below to the previous Python script.
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```python Example
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# After getting the tool call, execute the function
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def get_weather(location, unit="celsius"):
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# Your actual weather API call here
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return f"The weather in {location} is 22°{unit[0].upper()} and sunny."
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# Send tool result back to the model
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messages = [
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{"role": "user", "content": "What's the weather in Beijing?"},
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{
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": "call_123",
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"type": "function",
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"function": {
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"name": "get_weather",
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"arguments": '{"location": "Beijing", "unit": "celsius"}'
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}
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}]
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},
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{
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"role": "tool",
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"tool_call_id": "call_123",
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"content": get_weather("Beijing", "celsius")
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}
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]
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final_response = client.chat.completions.create(
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model="deepseek-ai/DeepSeek-V3.1-Terminus",
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messages=messages,
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temperature=0.7
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)
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print(final_response.choices[0].message.content)
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# Output: "Currently, it is **22°C and sunny** in Beijing."
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```
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#### 4.2.3 Multi-Token Prediction (EAGLE Speculative Decoding)
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DeepSeek-V3.1 shares the same architecture as DeepSeek-V3 and supports the same EAGLE-based MTP speculative decoding path. Refer to [DeepSeek-V3 §4.2.3](/cookbook/autoregressive/DeepSeek/DeepSeek-V3#4-2-3-multi-token-prediction-eagle-speculative-decoding) for the full configuration, tuning guidance, and `bench_speculative.py` reference. The `--speculative-num-steps`, `--speculative-eagle-topk`, and `--max-running-requests` recommendations apply equally to V3.1.
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## 5. Benchmark
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### 5.1 Speed Benchmark
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**Test Environment:**
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- Hardware: AMD MI300X GPU (8x)
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- Model: DeepSeek-V3.1-Terminus
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- Tensor Parallelism: 8
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- sglang version: 0.5.7
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**Benchmark Methodology:**
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We use industry-standard benchmark configurations to ensure results are comparable across frameworks and hardware platforms.
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#### 5.1.1 Standard Test Scenarios
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Three core scenarios reflect real-world usage patterns:
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<colgroup>
|
||||
<col style={{width: "25%"}} />
|
||||
<col style={{width: "25%"}} />
|
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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</colgroup>
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<thead>
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<tr style={{borderBottom: "2px solid #d55816"}}>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Scenario</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Input Length</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Output Length</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Use Case</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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||||
<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 different concurrency levels to capture the throughput vs. latency trade-off:
|
||||
|
||||
- **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-path deepseek-ai/DeepSeek-V3.1 \
|
||||
--tp 8
|
||||
```
|
||||
|
||||
- Low Concurrency (Latency-Optimized)
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-V3.1 \
|
||||
--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): 106.24
|
||||
Total input tokens: 6101
|
||||
Total input text tokens: 6101
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 4220
|
||||
Total generated tokens (retokenized): 4201
|
||||
Request throughput (req/s): 0.09
|
||||
Input token throughput (tok/s): 57.43
|
||||
Output token throughput (tok/s): 39.72
|
||||
Peak output token throughput (tok/s): 43.00
|
||||
Peak concurrent requests: 2
|
||||
Total token throughput (tok/s): 97.15
|
||||
Concurrency: 1.00
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 10620.29
|
||||
Median E2E Latency (ms): 8868.09
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 557.85
|
||||
Median TTFT (ms): 213.58
|
||||
P99 TTFT (ms): 1625.28
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 23.84
|
||||
Median TPOT (ms): 23.90
|
||||
P99 TPOT (ms): 24.03
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 23.90
|
||||
Median ITL (ms): 23.92
|
||||
P95 ITL (ms): 24.15
|
||||
P99 ITL (ms): 24.25
|
||||
Max ITL (ms): 25.44
|
||||
==================================================
|
||||
```
|
||||
|
||||
- Medium Concurrency (Balanced)
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-V3.1 \
|
||||
--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): 107.71
|
||||
Total input tokens: 39668
|
||||
Total input text tokens: 39668
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 40805
|
||||
Total generated tokens (retokenized): 40625
|
||||
Request throughput (req/s): 0.74
|
||||
Input token throughput (tok/s): 368.28
|
||||
Output token throughput (tok/s): 378.84
|
||||
Peak output token throughput (tok/s): 508.00
|
||||
Peak concurrent requests: 19
|
||||
Total token throughput (tok/s): 747.12
|
||||
Concurrency: 13.72
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 18473.65
|
||||
Median E2E Latency (ms): 19558.42
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 607.91
|
||||
Median TTFT (ms): 191.32
|
||||
P99 TTFT (ms): 2135.13
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 35.50
|
||||
Median TPOT (ms): 35.99
|
||||
P99 TPOT (ms): 43.62
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 35.10
|
||||
Median ITL (ms): 32.18
|
||||
P95 ITL (ms): 33.03
|
||||
P99 ITL (ms): 159.99
|
||||
Max ITL (ms): 453.99
|
||||
==================================================
|
||||
```
|
||||
|
||||
- High Concurrency (Throughput-Optimized)
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-V3.1 \
|
||||
--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): 207.65
|
||||
Total input tokens: 249831
|
||||
Total input text tokens: 249831
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 252662
|
||||
Total generated tokens (retokenized): 251238
|
||||
Request throughput (req/s): 2.41
|
||||
Input token throughput (tok/s): 1203.15
|
||||
Output token throughput (tok/s): 1216.79
|
||||
Peak output token throughput (tok/s): 2100.00
|
||||
Peak concurrent requests: 106
|
||||
Total token throughput (tok/s): 2419.94
|
||||
Concurrency: 91.02
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 37800.20
|
||||
Median E2E Latency (ms): 35921.56
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 835.15
|
||||
Median TTFT (ms): 236.88
|
||||
P99 TTFT (ms): 2868.52
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 73.33
|
||||
Median TPOT (ms): 76.35
|
||||
P99 TPOT (ms): 97.63
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 73.30
|
||||
Median ITL (ms): 50.82
|
||||
P95 ITL (ms): 180.67
|
||||
P99 ITL (ms): 186.83
|
||||
Max ITL (ms): 1661.39
|
||||
==================================================
|
||||
```
|
||||
|
||||
**Scenario 2: Reasoning (1K/8K)**
|
||||
|
||||
- Low Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-V3.1 \
|
||||
--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): 1097.29
|
||||
Total input tokens: 6101
|
||||
Total input text tokens: 6101
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 44462
|
||||
Total generated tokens (retokenized): 44313
|
||||
Request throughput (req/s): 0.01
|
||||
Input token throughput (tok/s): 5.56
|
||||
Output token throughput (tok/s): 40.52
|
||||
Peak output token throughput (tok/s): 43.00
|
||||
Peak concurrent requests: 2
|
||||
Total token throughput (tok/s): 46.08
|
||||
Concurrency: 1.00
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 109725.52
|
||||
Median E2E Latency (ms): 117748.67
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 156.67
|
||||
Median TTFT (ms): 156.19
|
||||
P99 TTFT (ms): 159.87
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 24.41
|
||||
Median TPOT (ms): 24.51
|
||||
P99 TPOT (ms): 24.96
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 24.65
|
||||
Median ITL (ms): 24.58
|
||||
P95 ITL (ms): 25.68
|
||||
P99 ITL (ms): 25.93
|
||||
Max ITL (ms): 29.80
|
||||
==================================================
|
||||
```
|
||||
|
||||
- Medium Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-V3.1 \
|
||||
--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): 775.02
|
||||
Total input tokens: 39668
|
||||
Total input text tokens: 39668
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 318306
|
||||
Total generated tokens (retokenized): 317426
|
||||
Request throughput (req/s): 0.10
|
||||
Input token throughput (tok/s): 51.18
|
||||
Output token throughput (tok/s): 410.70
|
||||
Peak output token throughput (tok/s): 512.00
|
||||
Peak concurrent requests: 18
|
||||
Total token throughput (tok/s): 461.89
|
||||
Concurrency: 13.86
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 134236.65
|
||||
Median E2E Latency (ms): 135181.28
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 214.35
|
||||
Median TTFT (ms): 194.12
|
||||
P99 TTFT (ms): 300.27
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 33.72
|
||||
Median TPOT (ms): 34.00
|
||||
P99 TPOT (ms): 34.75
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 33.69
|
||||
Median ITL (ms): 33.71
|
||||
P95 ITL (ms): 34.50
|
||||
P99 ITL (ms): 34.92
|
||||
Max ITL (ms): 164.76
|
||||
==================================================
|
||||
```
|
||||
|
||||
- High Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-V3.1 \
|
||||
--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): 1231.97
|
||||
Total input tokens: 158939
|
||||
Total input text tokens: 158939
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 1301025
|
||||
Total generated tokens (retokenized): 1296845
|
||||
Request throughput (req/s): 0.26
|
||||
Input token throughput (tok/s): 129.01
|
||||
Output token throughput (tok/s): 1056.05
|
||||
Peak output token throughput (tok/s): 1472.00
|
||||
Peak concurrent requests: 67
|
||||
Total token throughput (tok/s): 1185.07
|
||||
Concurrency: 56.17
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 216256.25
|
||||
Median E2E Latency (ms): 224192.84
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 317.68
|
||||
Median TTFT (ms): 235.28
|
||||
P99 TTFT (ms): 649.39
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 53.30
|
||||
Median TPOT (ms): 55.10
|
||||
P99 TPOT (ms): 56.58
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 53.13
|
||||
Median ITL (ms): 52.95
|
||||
P95 ITL (ms): 56.23
|
||||
P99 ITL (ms): 181.04
|
||||
Max ITL (ms): 208.61
|
||||
==================================================
|
||||
```
|
||||
|
||||
**Scenario 3: Summarization (8K/1K)**
|
||||
|
||||
- Low Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-V3.1 \
|
||||
--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): 114.47
|
||||
Total input tokens: 41941
|
||||
Total input text tokens: 41941
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 4220
|
||||
Total generated tokens (retokenized): 4194
|
||||
Request throughput (req/s): 0.09
|
||||
Input token throughput (tok/s): 366.39
|
||||
Output token throughput (tok/s): 36.87
|
||||
Peak output token throughput (tok/s): 42.00
|
||||
Peak concurrent requests: 2
|
||||
Total token throughput (tok/s): 403.26
|
||||
Concurrency: 1.00
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 11442.86
|
||||
Median E2E Latency (ms): 9508.87
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 883.78
|
||||
Median TTFT (ms): 481.38
|
||||
P99 TTFT (ms): 2217.45
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 24.93
|
||||
Median TPOT (ms): 25.05
|
||||
P99 TPOT (ms): 26.11
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 25.08
|
||||
Median ITL (ms): 25.08
|
||||
P95 ITL (ms): 26.18
|
||||
P99 ITL (ms): 26.28
|
||||
Max ITL (ms): 27.41
|
||||
==================================================
|
||||
```
|
||||
|
||||
- Medium Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-V3.1 \
|
||||
--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): 162.33
|
||||
Total input tokens: 300020
|
||||
Total input text tokens: 300020
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 41669
|
||||
Total generated tokens (retokenized): 41443
|
||||
Request throughput (req/s): 0.49
|
||||
Input token throughput (tok/s): 1848.27
|
||||
Output token throughput (tok/s): 256.70
|
||||
Peak output token throughput (tok/s): 467.00
|
||||
Peak concurrent requests: 19
|
||||
Total token throughput (tok/s): 2104.97
|
||||
Concurrency: 14.52
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 29456.89
|
||||
Median E2E Latency (ms): 27628.16
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 1784.30
|
||||
Median TTFT (ms): 1347.21
|
||||
P99 TTFT (ms): 5384.54
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 53.65
|
||||
Median TPOT (ms): 52.09
|
||||
P99 TPOT (ms): 74.39
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 53.23
|
||||
Median ITL (ms): 34.52
|
||||
P95 ITL (ms): 35.81
|
||||
P99 ITL (ms): 513.25
|
||||
Max ITL (ms): 2865.73
|
||||
==================================================
|
||||
```
|
||||
|
||||
- High Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-V3.1 \
|
||||
--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): 282.55
|
||||
Total input tokens: 1273893
|
||||
Total input text tokens: 1273893
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 170000
|
||||
Total generated tokens (retokenized): 169081
|
||||
Request throughput (req/s): 1.13
|
||||
Input token throughput (tok/s): 4508.6
|
||||
Output token throughput (tok/s): 601.67
|
||||
Peak output token throughput (tok/s): 1216
|
||||
Peak concurrent requests: 68
|
||||
Total token throughput (tok/s): 5110.27
|
||||
Concurrency: 59.81
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 52810.32
|
||||
Median E2E Latency (ms): 50981.81
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 786.69
|
||||
Median TTFT (ms): 499.38
|
||||
P99 TTFT (ms): 2925.98
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 97.93
|
||||
Median TPOT (ms): 103.45
|
||||
P99 TPOT (ms): 157.84
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 98.11
|
||||
Median ITL (ms): 55.7
|
||||
P95 ITL (ms): 240.71
|
||||
P99 ITL (ms): 1114.36
|
||||
==================================================
|
||||
```
|
||||
|
||||
#### 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 trade-off 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
|
||||
python3 benchmark/gsm8k/bench_sglang.py \
|
||||
--num-shots 8 \
|
||||
--num-questions 1316 \
|
||||
--parallel 1316
|
||||
```
|
||||
|
||||
**Test Results:**
|
||||
|
||||
```text Output
|
||||
Accuracy: 0.959
|
||||
Invalid: 0.000
|
||||
Latency: 29.185 s
|
||||
Output throughput: 4854.672 token/s
|
||||
```
|
||||
Reference in New Issue
Block a user