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RUNTIME / ADVANCED

SGLang

Multi-request GPU serving with prefix caching and speculative decoding

一手来源
DifficultyAdvancedSetup profile
Platforms4nvidia · amd · intel · cpu
APIOpenAI-compatible + native runtime API:30000
01 / INSTALL & RUN

安装方式取决于操作系统。以下命令是示例或模板,请先替换占位符并核对量化版本、文件格式、驱动和运行时支持;内存适配不保证部署成功。

  1. 1
    Installpip install uv && uv pip install --prerelease=allow sglang
  2. 2
    Start a modelpython3 -m sglang.launch_server --model-path <verified-checkpoint> --port 30000
  3. 3
    Connect your app

    OpenAI-compatible + native runtime API · :30000. Keep the service bound to localhost unless you add authentication and network controls.

注意

Each platform is a separate lane with its own floors: the NVIDIA lane requires CUDA 13, and 0.5.19 was the last CUDA 12 release. ROCm, Intel XPU, Xeon CPU and Apple Metal are documented separately, so support on one lane is not proof for another.

02 / COMPATIBLE MODELS

代表性开放权重模型

Alibaba Qwen

Qwen3.6 27B

27BQ4 / NVFP4 estimate
估算内存
~18.5 GB
上下文
262K native
Alibaba Qwen

Qwen3.8 27B

27BQ4 estimate
估算内存
~19.5 GB
上下文
262K native
Alibaba Qwen

Qwen3.6 35B-A3B

35B3B active · Q4 / NVFP4 MoE
估算内存
~25 GB
上下文
262K native
DeepSeek

DeepSeek V4 Flash

284B13B active · Official FP4 + FP8 mixed
估算内存
~176 GB
上下文
1M native
Google

Gemma 3 1B

1BINT4 / GGUF Q4
估算内存
~1.4 GB
上下文
32K
Meta

Llama 3.2 3B

3BGGUF Q4
估算内存
~2.8 GB
上下文
128K