[Docs] Rename docs_new/ to docs/ (#32123)
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
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Claude Opus 4.8
Claude Opus 4.8
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commit
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---
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title: DeepSeek-R1
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metatags:
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description: "Deploy DeepSeek-R1 reasoning model with SGLang - advanced step-by-step reasoning with FP8/FP4 quantization for NVIDIA and AMD GPUs."
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---
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import { DeepSeekR1BasicDeployment } from '/src/snippets/autoregressive/deepseek-r1-basic-deployment.jsx';
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import { DeepSeekR1AdvancedDeployment } from '/src/snippets/autoregressive/deepseek-r1-advanced-deployment.jsx';
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## 1. Model Introduction
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[DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) is DeepSeek's advanced reasoning model that combines powerful language understanding with step-by-step reasoning capabilities. The model is available in multiple quantization formats optimized for different hardware platforms.
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**Key Features:**
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- **Advanced Reasoning**: Built-in reasoning capabilities for complex problem-solving
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- **Multiple Quantizations**: FP8 and FP4 variants for different performance/memory trade-offs
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- **Hardware Optimization**: Specifically tuned for NVIDIA B200 (Blackwell) and H200 (Hopper) GPUs, AMD MI300X, MI325X and MI355X GPUs, as well as Intel Xeon CPUs
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- **High Performance**: Optimized for both throughput and latency scenarios
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**Available Models:**
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- **FP8 (8-bit quantized)**: [deepseek-ai/DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) - Recommended for H200 and MI300X
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- **FP4 (4-bit quantized)**: [nvidia/DeepSeek-R1-0528-FP4-v2](https://huggingface.co/nvidia/DeepSeek-R1-0528-FP4-v2) - Recommended for B200 and MI355X
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- **BF16 (upcast from FP8)**: [unsloth/DeepSeek-R1-0528-BF16](https://huggingface.co/unsloth/DeepSeek-R1-0528-BF16)
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- **INT8 (channel-wise)**: [meituan/DeepSeek-R1-Channel-INT8](https://huggingface.co/meituan/DeepSeek-R1-Channel-INT8)
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- **W4A8**: [novita/Deepseek-R1-0528-W4AFP8](https://huggingface.co/novita/Deepseek-R1-0528-W4AFP8)
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- **AWQ (4-bit)**: [QuixiAI/DeepSeek-R1-0528-AWQ](https://huggingface.co/QuixiAI/DeepSeek-R1-0528-AWQ)
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- **MXFP4**: [amd/DeepSeek-R1-MXFP4](https://huggingface.co/amd/DeepSeek-R1-MXFP4)
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**License:**
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To use DeepSeek-R1, you must agree to DeepSeek's Community License. See [LICENSE](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528/blob/main/LICENSE) for details.
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For more details, please refer to the [official DeepSeek-R1 repository](https://github.com/deepseek-ai/DeepSeek-R1).
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## 2. SGLang Installation
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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 deployment configurations optimized for different hardware platforms and use cases.
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### 3.1 Basic Configuration
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**Interactive Command Generator**: Use the configuration selector below to automatically generate a basic deployment command for your hardware platform, quantization method, and deployment strategy.
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<DeepSeekR1BasicDeployment />
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### 3.2 Optimal Configurations
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Pareto-optimal configurations for B200, H200, MI300X, MI325X, and MI355X hardware.
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<DeepSeekR1AdvancedDeployment />
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### 3.3 Configuration Tips
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DeepSeek-R1 shares the same MoE 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-R1 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 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-R1 --tp 8 --trust-remote-code
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```
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**Data Parallelism Attention (`--enable-dp-attention`):** Recommended for high-throughput scenarios. Use `--enable-dp-attention --tp 8 --dp 8` on a single 8-GPU node.
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**NCCL timeout:** If model loading is slow, increase: `--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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- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)
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### 4.2 Advanced Usage
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#### 4.2.1 Reasoning Parser
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DeepSeek-R1 supports advanced reasoning capabilities with built-in thinking process. 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-path deepseek-ai/DeepSeek-R1-0528 \
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--reasoning-parser deepseek-r1 \
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--tp 8
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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:30000/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-R1-0528",
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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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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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To solve this problem, I need to calculate 15% of 240.
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Step 1: Convert 15% to decimal: 15% = 0.15
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Step 2: Multiply 240 by 0.15
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Step 3: 240 × 0.15 = 36
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=============== Content =================
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The answer is 36. To find 15% of 240, we multiply 240 by 0.15, which equals 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-R1 supports tool calling capabilities. Enable the tool call parser:
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```shell Command
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python -m sglang.launch_server \
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--model-path deepseek-ai/DeepSeek-R1-0528 \
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--reasoning-parser deepseek-r1 \
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--tool-call-parser deepseekv3 \
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--chat-template examples/chat_template/tool_chat_template_deepseekr1.jinja \
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--tp 8
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```
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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:30000/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-R1-0528",
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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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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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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 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 =================", flush=True)
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thinking_started = False
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for tool_call in delta.tool_calls:
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if tool_call.function:
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print(f"🔧 Tool Call: {tool_call.function.name}")
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print(f" 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()
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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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The user is asking about the weather in Beijing. I need to use the get_weather function to retrieve this information.
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I should call the function with location="Beijing".
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=============== Content =================
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🔧 Tool Call: get_weather
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Arguments:
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🔧 Tool Call: None
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Arguments: {"location": "Beijing"}
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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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```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-R1-0528",
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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: "The weather in Beijing is currently 22°C and sunny."
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```
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#### 4.2.3 Multi-Token Prediction (EAGLE Speculative Decoding)
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DeepSeek-R1 supports EAGLE-based Multi-Token Prediction (MTP), the same mechanism as DeepSeek-V3. Refer to [DeepSeek-V3 §4.2.3](/cookbook/autoregressive/DeepSeek/DeepSeek-V3#4-2-3-multi-token-prediction-eagle-speculative-decoding) for the complete launch command, flag reference, tuning guidance (`--speculative-num-steps`, `--speculative-eagle-topk`, `--max-running-requests`), and `bench_speculative.py` link. R1's speed benchmark commands that include `--speculative-*` flags use this mechanism.
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#### 4.2.4 Thinking Budget
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Limit the model's thinking token budget using `CustomLogitProcessor`. Launch with `--enable-custom-logit-processor`:
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```shell Command
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python3 -m sglang.launch_server \
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--model deepseek-ai/DeepSeek-R1 \
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--tp 8 \
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--port 30000 \
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--reasoning-parser deepseek-r1 \
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--enable-custom-logit-processor
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```
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```python Example
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import openai
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from sglang.srt.sampling.custom_logit_processor import DeepSeekR1ThinkingBudgetLogitProcessor
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client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="*")
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response = client.chat.completions.create(
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model="deepseek-ai/DeepSeek-R1",
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messages=[{"role": "user", "content": "Is Paris the Capital of France?"}],
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max_tokens=1024,
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extra_body={
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"custom_logit_processor": DeepSeekR1ThinkingBudgetLogitProcessor().to_str(),
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"custom_params": {"thinking_budget": 512},
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},
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)
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print(response)
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```
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## 5. Benchmark
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This section uses **industry-standard configurations** for comparable benchmark results.
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### 5.1 Speed Benchmark
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**Test Environment:**
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- Hardware: B200 GPU (8x)
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- Model: DeepSeek-R1-0528
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- Tensor Parallelism: 8
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- SGLang Version: 0.5.6.post1
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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%"}} />
|
||||
<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>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1K</td>
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<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-R1-0528 \
|
||||
--tp 8
|
||||
```
|
||||
|
||||
- Low Concurrency (Latency-Optimized)
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-R1-0528 \
|
||||
--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): 40.00
|
||||
Total input tokens: 6101
|
||||
Total input text tokens: 6101
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 4210
|
||||
Total generated tokens (retokenized): 4205
|
||||
Request throughput (req/s): 0.25
|
||||
Input token throughput (tok/s): 152.52
|
||||
Output token throughput (tok/s): 105.24
|
||||
Peak output token throughput (tok/s): 110.00
|
||||
Peak concurrent requests: 2
|
||||
Total token throughput (tok/s): 257.76
|
||||
Concurrency: 1.00
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 3998.40
|
||||
Median E2E Latency (ms): 3207.53
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 153.00
|
||||
Median TTFT (ms): 140.76
|
||||
P99 TTFT (ms): 214.66
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 9.16
|
||||
Median TPOT (ms): 9.15
|
||||
P99 TPOT (ms): 9.21
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 9.16
|
||||
Median ITL (ms): 9.15
|
||||
P95 ITL (ms): 9.47
|
||||
P99 ITL (ms): 9.63
|
||||
Max ITL (ms): 15.45
|
||||
==================================================
|
||||
```
|
||||
|
||||
- Medium Concurrency (Balanced)
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-R1-0528 \
|
||||
--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): 51.21
|
||||
Total input tokens: 39668
|
||||
Total input text tokens: 39668
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 40725
|
||||
Total generated tokens (retokenized): 40458
|
||||
Request throughput (req/s): 1.56
|
||||
Input token throughput (tok/s): 774.66
|
||||
Output token throughput (tok/s): 795.30
|
||||
Peak output token throughput (tok/s): 1088.00
|
||||
Peak concurrent requests: 21
|
||||
Total token throughput (tok/s): 1569.96
|
||||
Concurrency: 13.93
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 8918.33
|
||||
Median E2E Latency (ms): 9466.16
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 273.51
|
||||
Median TTFT (ms): 131.71
|
||||
P99 TTFT (ms): 839.57
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 17.56
|
||||
Median TPOT (ms): 17.46
|
||||
P99 TPOT (ms): 28.68
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 17.02
|
||||
Median ITL (ms): 14.70
|
||||
P95 ITL (ms): 16.41
|
||||
P99 ITL (ms): 112.38
|
||||
Max ITL (ms): 461.90
|
||||
==================================================
|
||||
```
|
||||
|
||||
- High Concurrency (Throughput-Optimized)
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-R1-0528 \
|
||||
--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): 110.46
|
||||
Total input tokens: 249831
|
||||
Total input text tokens: 249831
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 252162
|
||||
Total generated tokens (retokenized): 251441
|
||||
Request throughput (req/s): 4.53
|
||||
Input token throughput (tok/s): 2261.80
|
||||
Output token throughput (tok/s): 2282.90
|
||||
Peak output token throughput (tok/s): 3900.00
|
||||
Peak concurrent requests: 109
|
||||
Total token throughput (tok/s): 4544.71
|
||||
Concurrency: 92.26
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 20380.71
|
||||
Median E2E Latency (ms): 19391.65
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 563.14
|
||||
Median TTFT (ms): 147.62
|
||||
P99 TTFT (ms): 2632.11
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 40.11
|
||||
Median TPOT (ms): 41.98
|
||||
P99 TPOT (ms): 50.10
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 39.37
|
||||
Median ITL (ms): 26.36
|
||||
P95 ITL (ms): 98.16
|
||||
P99 ITL (ms): 150.08
|
||||
Max ITL (ms): 2052.85
|
||||
==================================================
|
||||
```
|
||||
|
||||
**Scenario 2: Reasoning (1K/8K)**
|
||||
|
||||
- Low Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-R1-0528 \
|
||||
--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): 411.34
|
||||
Total input tokens: 6101
|
||||
Total input text tokens: 6101
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 44452
|
||||
Total generated tokens (retokenized): 44390
|
||||
Request throughput (req/s): 0.02
|
||||
Input token throughput (tok/s): 14.83
|
||||
Output token throughput (tok/s): 108.07
|
||||
Peak output token throughput (tok/s): 110.00
|
||||
Peak concurrent requests: 2
|
||||
Total token throughput (tok/s): 122.90
|
||||
Concurrency: 1.00
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 41132.04
|
||||
Median E2E Latency (ms): 44288.71
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 125.76
|
||||
Median TTFT (ms): 126.19
|
||||
P99 TTFT (ms): 137.69
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 9.21
|
||||
Median TPOT (ms): 9.20
|
||||
P99 TPOT (ms): 9.27
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 9.23
|
||||
Median ITL (ms): 9.22
|
||||
P95 ITL (ms): 9.64
|
||||
P99 ITL (ms): 9.86
|
||||
Max ITL (ms): 15.18
|
||||
==================================================
|
||||
```
|
||||
|
||||
- Medium Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-R1-0528 \
|
||||
--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): 348.93
|
||||
Total input tokens: 39668
|
||||
Total input text tokens: 39668
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 318226
|
||||
Total generated tokens (retokenized): 317630
|
||||
Request throughput (req/s): 0.23
|
||||
Input token throughput (tok/s): 113.69
|
||||
Output token throughput (tok/s): 912.02
|
||||
Peak output token throughput (tok/s): 1088.00
|
||||
Peak concurrent requests: 19
|
||||
Total token throughput (tok/s): 1025.70
|
||||
Concurrency: 14.07
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 61360.70
|
||||
Median E2E Latency (ms): 62071.20
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 176.02
|
||||
Median TTFT (ms): 153.75
|
||||
P99 TTFT (ms): 268.44
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 15.42
|
||||
Median TPOT (ms): 15.59
|
||||
P99 TPOT (ms): 16.07
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 15.39
|
||||
Median ITL (ms): 15.17
|
||||
P95 ITL (ms): 16.62
|
||||
P99 ITL (ms): 18.13
|
||||
Max ITL (ms): 226.59
|
||||
==================================================
|
||||
```
|
||||
|
||||
- High Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-R1-0528 \
|
||||
--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): 589.31
|
||||
Total input tokens: 158939
|
||||
Total input text tokens: 158939
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 1300705
|
||||
Total generated tokens (retokenized): 1297658
|
||||
Request throughput (req/s): 0.54
|
||||
Input token throughput (tok/s): 269.70
|
||||
Output token throughput (tok/s): 2207.16
|
||||
Peak output token throughput (tok/s): 2944.00
|
||||
Peak concurrent requests: 68
|
||||
Total token throughput (tok/s): 2476.86
|
||||
Concurrency: 57.03
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 105032.36
|
||||
Median E2E Latency (ms): 108229.09
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 223.91
|
||||
Median TTFT (ms): 158.15
|
||||
P99 TTFT (ms): 474.86
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 25.94
|
||||
Median TPOT (ms): 26.72
|
||||
P99 TPOT (ms): 27.99
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 25.79
|
||||
Median ITL (ms): 25.37
|
||||
P95 ITL (ms): 26.70
|
||||
P99 ITL (ms): 105.49
|
||||
Max ITL (ms): 237.91
|
||||
==================================================
|
||||
```
|
||||
|
||||
**Scenario 3: Summarization (8K/1K)**
|
||||
|
||||
- Low Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-R1-0528 \
|
||||
--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): 40.65
|
||||
Total input tokens: 41941
|
||||
Total input text tokens: 41941
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 4210
|
||||
Total generated tokens (retokenized): 4195
|
||||
Request throughput (req/s): 0.25
|
||||
Input token throughput (tok/s): 1031.65
|
||||
Output token throughput (tok/s): 103.56
|
||||
Peak output token throughput (tok/s): 110.00
|
||||
Peak concurrent requests: 2
|
||||
Total token throughput (tok/s): 1135.20
|
||||
Concurrency: 1.00
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 4063.62
|
||||
Median E2E Latency (ms): 3296.13
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 165.91
|
||||
Median TTFT (ms): 154.96
|
||||
P99 TTFT (ms): 240.92
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 9.26
|
||||
Median TPOT (ms): 9.27
|
||||
P99 TPOT (ms): 9.42
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 9.28
|
||||
Median ITL (ms): 9.28
|
||||
P95 ITL (ms): 9.66
|
||||
P99 ITL (ms): 9.83
|
||||
Max ITL (ms): 14.06
|
||||
==================================================
|
||||
```
|
||||
|
||||
- Medium Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-R1-0528 \
|
||||
--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): 56.71
|
||||
Total input tokens: 300020
|
||||
Total input text tokens: 300020
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 41589
|
||||
Total generated tokens (retokenized): 41490
|
||||
Request throughput (req/s): 1.41
|
||||
Input token throughput (tok/s): 5290.75
|
||||
Output token throughput (tok/s): 733.41
|
||||
Peak output token throughput (tok/s): 1024.00
|
||||
Peak concurrent requests: 20
|
||||
Total token throughput (tok/s): 6024.16
|
||||
Concurrency: 14.25
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 10098.99
|
||||
Median E2E Latency (ms): 10623.46
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 486.80
|
||||
Median TTFT (ms): 189.59
|
||||
P99 TTFT (ms): 2138.73
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 19.06
|
||||
Median TPOT (ms): 19.23
|
||||
P99 TPOT (ms): 30.69
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 18.53
|
||||
Median ITL (ms): 15.63
|
||||
P95 ITL (ms): 16.64
|
||||
P99 ITL (ms): 109.71
|
||||
Max ITL (ms): 1471.36
|
||||
==================================================
|
||||
```
|
||||
|
||||
- High Concurrency
|
||||
|
||||
```bash Command
|
||||
python -m sglang.bench_serving \
|
||||
--backend sglang \
|
||||
--model deepseek-ai/DeepSeek-R1-0528 \
|
||||
--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): 115.55
|
||||
Total input tokens: 1273893
|
||||
Total input text tokens: 1273893
|
||||
Total input vision tokens: 0
|
||||
Total generated tokens: 169680
|
||||
Total generated tokens (retokenized): 169275
|
||||
Request throughput (req/s): 2.77
|
||||
Input token throughput (tok/s): 11024.93
|
||||
Output token throughput (tok/s): 1468.50
|
||||
Peak output token throughput (tok/s): 2254.00
|
||||
Peak concurrent requests: 70
|
||||
Total token throughput (tok/s): 12493.43
|
||||
Concurrency: 59.45
|
||||
----------------End-to-End Latency----------------
|
||||
Mean E2E Latency (ms): 21465.98
|
||||
Median E2E Latency (ms): 20686.26
|
||||
---------------Time to First Token----------------
|
||||
Mean TTFT (ms): 913.93
|
||||
Median TTFT (ms): 224.92
|
||||
P99 TTFT (ms): 6257.83
|
||||
-----Time per Output Token (excl. 1st token)------
|
||||
Mean TPOT (ms): 39.93
|
||||
Median TPOT (ms): 40.99
|
||||
P99 TPOT (ms): 60.91
|
||||
---------------Inter-Token Latency----------------
|
||||
Mean ITL (ms): 38.83
|
||||
Median ITL (ms): 26.29
|
||||
P95 ITL (ms): 113.81
|
||||
P99 ITL (ms): 176.94
|
||||
Max ITL (ms): 5521.53
|
||||
==================================================
|
||||
```
|
||||
|
||||
#### 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