Sglang Installer

SGLang Installer

This skill provides comprehensive guidance for installing, configuring, and debugging SGLang on NVIDIA GPUs with CUDA 12.x.

When to Use This Skill

• Installing SGLang from source on NVIDIA GPUs
• Debugging SGLang installation errors (missing libraries, version conflicts)
• Configuring tensor parallelism for different model architectures
• Setting up environment variables for CUDA and NVIDIA libraries
• Starting and testing SGLang inference server
• Fixing common runtime errors (cuDNN, cusparseLt, NCCL issues)

Version Information (as of v0.5.8)

What's New in v0.5.8

• 1.5x faster diffusion models across the board
• Chunked Pipeline Parallelism for million-token context (near-linear scaling)
• EPD Disaggregation for Vision-Language Models (elastic encoder scaling)
• GLM4-MoE optimization: 65% faster TTFT
• New models: GLM 4.7 Flash, LFM2, Qwen3-VL-Embedding/Reranker, DeepSeek V3.2 NVFP4, FLUX.2-klein-9B

What's New in v0.5.7

• Model Gateway v0.3.0 release
• Scalable Pipeline Parallelism with dynamic chunking for ultra-long contexts
• Encoder Disaggregation for multi-modal models
• Diffusion improvements: --dit-layerwise-offload true reduces peak VRAM by 30GB
• New models: Mimo-V2-Flash, Nemotron-Nano-v3, LLaDA 2.0, EAGLE 3 speculative decoding
• Hardware support: AMD/4090/5090 for diffusion

Mooncake Transfer Engine

Mooncake is required for prefill-decode disaggregation mode, which separates prefill and decode phases across different nodes for production deployments.

Installing Mooncake

pip install --break-system-packages mooncake-transfer-engine==0.3.8.post1

Verifying Mooncake

python3 -c "from mooncake.engine import TransferEngine; print('Mooncake OK')"

When is Mooncake Needed?

Mooncake is required when using:

• --disaggregation-mode prefill or --disaggregation-mode decode
• Multi-node deployments with KV cache transfer
• Production DeepSeek-V3/R1 deployments with prefill-decode separation

Note: For single-node testing without disaggregation, Mooncake is not required.

NIXL Transfer Engine (Recommended)

NIXL (NVIDIA Inference Xfer Library) is an alternative to Mooncake that uses DMA-BUF instead of nvidia_peermem. It's the recommended choice when:

• Using NVIDIA Open Kernel Module (nvidia_peermem won't load)
• nvidia_peermem fails with "Invalid argument" error
• You want a more portable solution that doesn't depend on kernel modules

Installing NIXL

pip install --break-system-packages nixl==0.9.0

# IMPORTANT: NIXL may downgrade NVIDIA libraries, reinstall correct versions:
pip install nvidia-nccl-cu12==2.28.3 --force-reinstall --no-deps
pip install nvidia-cudnn-cu12==9.16.0.29 --force-reinstall --no-deps

注意: NIXL 还会安装 nvidia-nvshmem-cu12==3.4.5，这个 pip 包不会被使用。 DeepEP 使用的是自编译的 NVSHMEM 3.5.19（带 IBGDA 支持），通过 unified-env.sh 中的 LD_PRELOAD 加载。

Verifying NIXL

python3 -c "import nixl; print('NIXL OK')"

Using NIXL for Disaggregation

Add --disaggregation-transfer-backend nixl to your launch command:

python3 -m sglang.launch_server \
    --model-path deepseek-ai/DeepSeek-V3 \
    --disaggregation-mode prefill \
    --disaggregation-transfer-backend nixl \  # Use NIXL instead of Mooncake
    --tp-size 8 \
    ...

NIXL vs Mooncake

Recommendation: Use NIXL for new deployments, especially on systems with NVIDIA Open Kernel Module.

Installation Workflow

Pre-requisites (Ubuntu 24.04)

Ubuntu 24.04 doesn't include pip by default. Install it first:

sudo apt-get update
sudo apt-get install -y python3-pip

Step 1: Environment Setup

To set up the environment, ensure CUDA is properly configured:

export CUDA_HOME=/usr/local/cuda
export PATH=$CUDA_HOME/bin:$PATH
export BUILD_TYPE=blackwell  # or "all" for general, "hopper" for H100

Step 2: Clone and Install

To install SGLang from source:

mkdir -p /sgl-workspace && cd /sgl-workspace

# Clone specific version
git clone -b v0.5.8 --depth 1 https://github.com/sgl-project/sglang.git
cd sglang

# Install sgl-kernel first (for CUDA 12.9)
pip install sgl-kernel==0.3.21

# Install SGLang with blackwell support
pip install -e "python[blackwell]" --extra-index-url https://download.pytorch.org/whl/cu129

Step 3: Install Additional Dependencies

To install required NVIDIA libraries and NIXL:

# NVIDIA libraries (required)
pip install nvidia-nccl-cu12==2.28.3 --force-reinstall --no-deps
pip install nvidia-cudnn-cu12==9.16.0.29 --force-reinstall --no-deps

# NIXL for KV cache transfer (RECOMMENDED for disaggregation mode)
pip install --break-system-packages nixl==0.9.0
# Re-install NVIDIA libs after NIXL (NIXL may downgrade them)
pip install nvidia-nccl-cu12==2.28.3 --force-reinstall --no-deps
pip install nvidia-cudnn-cu12==9.16.0.29 --force-reinstall --no-deps

⚠️ Important: NIXL is required for prefill-decode disaggregation mode. If you skip NIXL, you'll need nvidia_peermem kernel module (often fails on NVIDIA Open driver).

Step 4: Configure LD_LIBRARY_PATH

To fix library loading issues, run scripts/setup_env.sh or manually set:

# Collect all nvidia pip package lib paths
NVIDIA_LIB_PATHS=""
for d in /usr/local/lib/python3.12/dist-packages/nvidia/*/lib; do
    [ -d "$d" ] && NVIDIA_LIB_PATHS="${d}:${NVIDIA_LIB_PATHS}"
done
for d in $HOME/.local/lib/python3.12/site-packages/nvidia/*/lib; do
    [ -d "$d" ] && NVIDIA_LIB_PATHS="${d}:${NVIDIA_LIB_PATHS}"
done
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${NVIDIA_LIB_PATHS}${LD_LIBRARY_PATH}

Common Errors and Fixes

Error: DeepSeek-V3 FP8 outputs garbage on Blackwell (B200)

Symptom:

Model outputs garbage characters like "################" or random symbols

Log Warning:

WARNING model_config.py:872: DeepGemm is enabled but the scale_fmt of checkpoint is not ue8m0. This might cause accuracy degradation on Blackwell.

Diagnosis: The DeepSeek-V3 official FP8 checkpoint uses mscale format which is incompatible with DeepGEMM on Blackwell (B200) GPUs. DeepGEMM expects ue8m0 scale format.

Workaround: Use vLLM instead of SGLang for DeepSeek-V3 on Blackwell:

# vLLM handles the FP8 scale format correctly
vllm serve deepseek-ai/DeepSeek-V3 --tensor-parallel-size 8 --port 8000 --trust-remote-code

Status: Known issue as of SGLang 0.5.8 on Blackwell GPUs. Works correctly on H100/A100.

Error: Cannot uninstall typing_extensions (Ubuntu 24.04)

Symptom:

ERROR: Cannot uninstall typing_extensions 4.10.0, RECORD file not found.
Hint: The package was installed by debian.

Diagnosis: Ubuntu 24.04 installs typing_extensions as a system package managed by apt.

Fix:

pip install -e "python[blackwell]" --ignore-installed typing_extensions --break-system-packages

Error: sgl-kernel ABI mismatch (PyTorch user/system conflict)

Symptom:

ImportError: .../sgl_kernel/sm100/common_ops.abi3.so: undefined symbol: _ZN3c104cuda29c10_cuda_check_implementationEiPKcS2_ib

Diagnosis: PyTorch installed in both user (~/.local/lib/python3.12/site-packages/) and system (/usr/local/lib/python3.12/dist-packages/) directories with different versions.

Check:

pip3 show torch | grep -E "Version|Location"
ls /usr/local/lib/python3.12/dist-packages/ | grep torch
ls ~/.local/lib/python3.12/site-packages/ | grep torch

Fix: Remove user-installed torch to use system version:

pip3 uninstall torch torchvision torchaudio -y --break-system-packages
python3 -c "import torch; print(torch.__version__)"  # Should show system version

Error: libcudnn.so.9 not found

Symptom:

ImportError: libcudnn.so.9: cannot open shared object file

Fix:

pip install nvidia-cudnn-cu12==9.16.0.29 --force-reinstall --no-deps
# Then set LD_LIBRARY_PATH as described above

Error: libcusparseLt.so.0 not found

Symptom:

ImportError: libcusparseLt.so.0: cannot open shared object file

Fix:

pip install nvidia-cusparselt-cu12
# Then set LD_LIBRARY_PATH as described above

Error: assert self.total_num_heads % tp_size == 0

Symptom:

AssertionError: assert self.total_num_heads % tp_size == 0

Diagnosis: The model's attention head count is not divisible by the tensor parallelism size.

Fix: Choose a --tp value that divides the model's attention head count:

To find the attention head count for any model:

python3 -c "from transformers import AutoConfig; c = AutoConfig.from_pretrained('MODEL_NAME'); print(f'Attention heads: {c.num_attention_heads}')"

Error: NCCL errors or timeouts

Fix:

pip install nvidia-nccl-cu12==2.28.3 --force-reinstall --no-deps

Error: sgl-kernel version mismatch

Symptom: SGLang installs an older sgl-kernel version than expected.

Note: As of v0.5.6.post2, SGLang's dependencies pin sgl-kernel to 0.3.19, so even if you pre-install 0.3.21, it will be downgraded during SGLang installation. This is expected behavior and 0.3.19 works correctly.

If you need a specific version: Install sgl-kernel AFTER SGLang:

pip install -e "python[blackwell]" ...
pip install sgl-kernel==0.3.21 --force-reinstall --no-deps  # if needed

Error: sgl-kernel ABI incompatibility (undefined symbol)

Symptom:

ImportError: .../sgl_kernel/sm100/common_ops.abi3.so: undefined symbol: _ZN3c104cuda29c10_cuda_check_implementationEiPKcS2_ib

Diagnosis: sgl-kernel was compiled against a different PyTorch version than currently installed. This commonly happens when:

1. FlashInfer installation upgrades PyTorch to 2.10
2. vLLM installation changes PyTorch to 2.9.1
3. Manual PyTorch version changes

Fix:

1. First, check your current PyTorch version:

   python3 -c "import torch; print(torch.__version__)"
   

2. For vLLM compatibility (PyTorch 2.9.1), reinstall sgl-kernel:

   pip install torch==2.9.1+cu129 --index-url https://download.pytorch.org/whl/cu129 --force-reinstall
   pip install sgl-kernel==0.3.21 --force-reinstall --no-deps
   pip install nvidia-nccl-cu12==2.28.3 nvidia-cudnn-cu12==9.16.0.29 --force-reinstall --no-deps
   

3. For standalone SGLang (no vLLM), use the PyTorch version from SGLang installation.

Root Cause: The sgl-kernel binary is compiled against specific PyTorch CUDA APIs. When PyTorch version changes, the ABI symbols may not match.

Error: FlashInfer changes PyTorch version

Symptom: After installing FlashInfer, other packages fail with version conflicts.

Diagnosis: flashinfer-python and flashinfer-cubin have their own PyTorch dependencies that may override your installed version.

Fix: After FlashInfer installation, always reinstall the correct PyTorch and NVIDIA libraries:

# For vLLM compatibility
pip install torch==2.9.1+cu129 --index-url https://download.pytorch.org/whl/cu129 --force-reinstall
pip install nvidia-nccl-cu12==2.28.3 nvidia-cudnn-cu12==9.16.0.29 --force-reinstall --no-deps
pip install sgl-kernel==0.3.21 --force-reinstall --no-deps

Error: num_max_dispatch_tokens_per_rank assertion

Symptom:

assert self.num_max_dispatch_tokens_per_rank <= 1024
AssertionError

Diagnosis: SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK is set to a value > 1024.

Fix: Set the value to 1024 or less:

export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=1024

Error: Mooncake not installed

Symptom:

ModuleNotFoundError: No module named 'mooncake'
ImportError: Please install mooncake by following the instructions...

Diagnosis: Using --disaggregation-mode prefill/decode without Mooncake installed.

Fix:

pip install --break-system-packages mooncake-transfer-engine==0.3.8.post1

Error: NIXL library version mismatch

Symptom: Installing NIXL downgrades NVIDIA libraries, causing import errors.

NIXL 会降级以下库:

• nvidia-nccl-cu12: 2.28.3 → 2.27.5
• nvidia-cudnn-cu12: 9.16.0.29 → 9.10.2.21
• nvidia-nvshmem-cu12: 安装 3.4.5 (pip 包版本)

Fix: After installing NIXL, reinstall NVIDIA libraries:

pip install nvidia-nccl-cu12==2.28.3 --force-reinstall --no-deps
pip install nvidia-cudnn-cu12==9.16.0.29 --force-reinstall --no-deps

关于 nvidia-nvshmem-cu12: NIXL 安装的 nvidia-nvshmem-cu12 (3.4.5) 是 pip 包版本，不会影响 DeepEP。 DeepEP 使用自编译的 NVSHMEM (3.5.19，带 IBGDA 支持)，通过 unified-env.sh 中的 LD_PRELOAD 强制加载。

Error: DeepGEMM compile_deep_gemm OOM (CUDA out of memory)

Symptom:

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 7.00 GiB. GPU 0 has a total capacity of 178.35 GiB of which 2.31 GiB is free.

Diagnosis: compile_deep_gemm 默认在单卡上加载整个模型。DeepSeek-V3 (671B) 远超单卡 183GB 显存。

Fix: 必须指定 --tp 参数，与后续 launch_server 的 --tp-size 一致：

# 错误: 不加 --tp，单卡加载 671B 模型 → OOM
python3 -m sglang.compile_deep_gemm --model-path deepseek-ai/DeepSeek-V3

# 正确: 用 TP=8 分布到 8 卡
python3 -m sglang.compile_deep_gemm --model-path deepseek-ai/DeepSeek-V3 --tp 8

注意: cubin 缓存路径是 ~/.cache/deep_gemm/cache/（不是 ~/.deep_gemm/cache/），预期产出 ~692 个文件。

Error: DeepGEMM JIT compilation causes DeepEP dispatch timeout

Symptom:

RuntimeError: DeepEP error: timeout (dispatch CPU)
# 或者 server warmup 阶段长时间卡住

Diagnosis: MoE 模型首次启动时，DeepGEMM JIT 编译 ~692 个 cubin 文件耗时 10-20 分钟。在多 DP rank 或 PD disaggregation 场景下，不同 rank 编译速度不同步，导致 DeepEP all-to-all 通信超时。

Fix: 在启动 server 前预编译 DeepGEMM：

source /opt/deepep/unified-env.sh
export HF_HOME=/lssd/huggingface
# IMPORTANT: --tp 必须与 server 启动时的 --tp-size 一致，避免单卡 OOM
python3 -m sglang.compile_deep_gemm --model-path deepseek-ai/DeepSeek-V3 --tp 8
# 编译完成后再启动 server

Error: Direct request to Prefill node (bootstrap_room assertion)

Symptom:

AssertionError: bootstrap_room should not be None

Diagnosis: 直接向 Prefill 节点发送请求，而非通过 sglang-router。

Fix: 请求必须通过 sglang-router 路由：

# 错误: curl http://prefill-ip:30000/v1/chat/completions ...
# 正确: curl http://router-ip:30080/v1/chat/completions ...

Error: Direct request to Decode node (bootstrap room id)

Symptom:

400 Bad Request: Disaggregated request received without bootstrap room id

Diagnosis: 直接向 Decode 节点发送请求，而非通过 sglang-router。

Fix: 同上，请求必须通过 sglang-router。PD disaggregation 模式下，Prefill 和 Decode 不接受直接客户端请求。

Error: dist-init-addr bind failure on Decode node

Symptom:

zmq.error.ZMQError: No such device (addr: tcp://10.8.0.25:5757)

Diagnosis: --dist-init-addr 使用了 Prefill 节点的特定 IP，Decode 节点无法 bind 该地址。

Fix: 使用 0.0.0.0 代替特定 IP：

# 错误: --dist-init-addr 10.8.0.25:5757
# 正确: --dist-init-addr 0.0.0.0:5757

Error: Deprecated environment variable warning

Symptom:

UserWarning: Environment variable SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK is deprecated

Fix: Use the new variable name:

# Old (deprecated)
export SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK=1

# New
export SGLANG_DISABLE_TP_MEMORY_INBALANCE_CHECK=1

DeepGEMM Precompilation (MoE Models)

MoE 模型（DeepSeek-V3/R1 等）使用 DeepGEMM 进行 FP8 矩阵乘法。DeepGEMM 采用 JIT 编译，首次启动时会编译 ~692 个 cubin 文件。

问题: 在 PD disaggregation 或多 DP rank 场景下，JIT 编译耗时 10-20 分钟，导致 DP rank 间不同步，触发 DeepEP dispatch 超时错误。

解决方案: 在启动 server 前预编译 DeepGEMM：

source /opt/deepep/unified-env.sh
export HF_HOME=/lssd/huggingface

# 预编译 DeepGEMM cubin（针对特定模型的 GEMM 维度）
# IMPORTANT: --tp 必须与 server 启动时的 --tp-size 一致
# DeepSeek-V3 (671B) 单卡放不下，不加 --tp 会 OOM
python3 -m sglang.compile_deep_gemm --model-path deepseek-ai/DeepSeek-V3 --tp 8

# 验证 cubin 缓存
ls ~/.cache/deep_gemm/cache/ | wc -l
# 预期: ~692 个文件（DeepSeek-V3 架构）

注意:

• --tp 参数: 必须与后续 launch_server 的 --tp-size 一致。不加 --tp 默认单卡加载，DeepSeek-V3 (671B) 会触发 CUDA OOM
• DeepGEMM 编译结果是半模型特定的：基于模型的 GEMM 维度（M/N/K），相同架构（如 DeepSeek-V3 和 DeepSeek-R1）可共享缓存
• 不同架构的模型（如 Qwen-MoE vs DeepSeek）需要重新编译
• cubin 缓存目录: ~/.cache/deep_gemm/cache/（注意不是 ~/.deep_gemm/cache/）
• 预编译后缓存持久化在磁盘上，重启后无需重新编译

Starting the Server

To start the SGLang server:

# IMPORTANT: 必须先加载 DeepEP 环境（设置 LD_PRELOAD 等）
source /opt/deepep/unified-env.sh

# 设置 HuggingFace 缓存目录（可选，使用 LSSD 加速）
export HF_HOME=/lssd/huggingface

# MoE 模型: 先预编译 DeepGEMM（避免 warmup 超时）
# --tp 必须与 launch_server 的 --tp-size 一致，避免单卡 OOM
python3 -m sglang.compile_deep_gemm --model-path deepseek-ai/DeepSeek-V3 --tp 8

# Start server (adjust tp based on model architecture)
python3 -m sglang.launch_server \
    --model-path Qwen/Qwen2.5-7B-Instruct \
    --port 30000 \
    --host 0.0.0.0 \
    --tp 4 \
    --trust-remote-code

注意: 如果不 source unified-env.sh，DeepEP 会因为找不到正确的 NVSHMEM 库而报错。

Disaggregation Mode (Prefill-Decode Separation)

For production DeepSeek-V3/R1 deployments, SGLang supports prefill-decode disaggregation where prefill and decode phases run on separate nodes.

Prerequisites

1. Transfer backend - one of:
   • NIXL (recommended): pip install nixl==0.9.0
   • Mooncake: pip install mooncake-transfer-engine==0.3.8.post1 (requires nvidia_peermem)
2. DeepEP for MoE all-to-all communication
3. DeepEP config files for expert placement
4. sglang-router for request routing (必须):

   pip install --break-system-packages sglang-router
   

Architecture

PD disaggregation 的请求流程：

Client → sglang-router → Prefill Node (port 30000)
                       → Decode Node  (port 30001)

IMPORTANT: 客户端请求必须发送到 sglang-router，不能直接发送到 Prefill 或 Decode 节点。

• 直接请求 Prefill: AssertionError: bootstrap_room should not be None
• 直接请求 Decode: 400 Disaggregated request received without bootstrap room id

DeepSeek-V3 Prefill Node Example

source /opt/deepep/unified-env.sh

export HF_TOKEN=your_token_here

# IMPORTANT: Must be <= 1024, otherwise assertion error
SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=1024 \
MC_TE_METRIC=true \
SGLANG_DISAGGREGATION_HEARTBEAT_MAX_FAILURE=100000 \
SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=100000 \
SGLANG_DISAGGREGATION_WAITING_TIMEOUT=100000 \
SGLANG_MOONCAKE_CUSTOM_MEM_POOL=false \
SGLANG_LOCAL_IP_NIC=enp0s19 \
GLOO_SOCKET_IFNAME=enp0s19 \
NCCL_SOCKET_IFNAME=enp0s19 \
NCCL_MNNVL_ENABLE=1 \
NCCL_CUMEM_ENABLE=1 \
SGLANG_USE_MESSAGE_QUEUE_BROADCASTER=0 \
SGLANG_DISABLE_TP_MEMORY_INBALANCE_CHECK=1 \
python3 -m sglang.launch_server \
    --model-path deepseek-ai/DeepSeek-V3 \
    --download-dir /lssd/huggingface/hub \
    --trust-remote-code \
    --disaggregation-mode prefill \
    --dist-init-addr 0.0.0.0:5757 \
    --nnodes 1 \
    --node-rank 0 \
    --tp-size 8 \
    --dp-size 8 \
    --enable-dp-attention \
    --host 0.0.0.0 \
    --context-length 2176 \
    --disable-radix-cache \
    --moe-dense-tp-size 1 \
    --enable-dp-lm-head \
    --disable-shared-experts-fusion \
    --ep-num-redundant-experts 32 \
    --eplb-algorithm deepseek \
    --deepep-config /path/to/deepep_config.json \
    --attention-backend cutlass_mla \
    --watchdog-timeout 1000000 \
    --init-expert-location /path/to/prefill_in4096.json \
    --disable-cuda-graph \
    --chunked-prefill-size 16384 \
    --max-total-tokens 32768 \
    --moe-a2a-backend deepep \
    --deepep-mode normal \
    --ep-dispatch-algorithm dynamic

Key Configuration Files

1. deepep_config.json - DeepEP SM configuration:

{
    "n_sms": 128,
    "normal_dispatch": {"num_sms": 128},
    "normal_combine": {"num_sms": 128}
}

2. prefill_in4096.json - Expert placement statistics for EPLB

Environment Variables

RDMA Memory Registration Errors (Mooncake)

When running with Mooncake, you may see RDMA memory registration errors:

RdmaTransport: Failed to register memory: addr 0x... length 37896192

Root Cause: nvidia_peermem module is not loaded or incompatible with your driver.

Diagnosis:

# Check if nvidia_peermem loads
sudo modprobe nvidia_peermem
# If you see: "could not insert 'nvidia_peermem': Invalid argument"
# This means you're using NVIDIA Open Kernel Module, which is incompatible

Solutions (in order of preference):

1. Switch to NIXL backend (recommended):

   pip install --break-system-packages nixl==0.9.0
   # Add to launch command:
   --disaggregation-transfer-backend nixl
   

2. Use TCP fallback (slower): Mooncake will automatically fall back to TCP, but this significantly impacts multi-node performance.

3. Load nvidia_peermem (only works with proprietary driver):

   sudo modprobe nvidia_peermem
   

Note: NIXL uses DMA-BUF which is built into the Linux kernel and doesn't require nvidia_peermem.

1P1D Quick Start (Minimal DeepSeek-V3 Example)

验证过的最小化 1P1D 部署步骤（2 节点 B200）：

Step 1: 两节点预编译 DeepGEMM

# 在 Prefill 和 Decode 节点上都执行
source /opt/deepep/unified-env.sh
export HF_HOME=/lssd/huggingface
# --tp 8 必须加，否则单卡加载 671B 模型会 OOM
python3 -m sglang.compile_deep_gemm --model-path deepseek-ai/DeepSeek-V3 --tp 8
# 验证: ls ~/.cache/deep_gemm/cache/ | wc -l  → 预期 ~692

Step 2: Prefill Node (e.g. 10.8.0.25)

source /opt/deepep/unified-env.sh
export HF_HOME=/lssd/huggingface

SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=1024 \
NCCL_SOCKET_IFNAME=enp0s19 \
GLOO_SOCKET_IFNAME=enp0s19 \
python3 -m sglang.launch_server \
    --model-path deepseek-ai/DeepSeek-V3 \
    --port 30000 \
    --host 0.0.0.0 \
    --tp-size 8 \
    --trust-remote-code \
    --disaggregation-mode prefill \
    --disaggregation-transfer-backend nixl \
    --dist-init-addr 0.0.0.0:5757 \
    --moe-dense-tp-size 1 \
    --enable-dp-attention \
    --moe-a2a-backend deepep \
    --deepep-mode normal \
    --disable-cuda-graph \
    --watchdog-timeout 1000000

Step 3: Decode Node (e.g. 10.8.0.71)

source /opt/deepep/unified-env.sh
export HF_HOME=/lssd/huggingface

SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=1024 \
NCCL_SOCKET_IFNAME=enp0s19 \
GLOO_SOCKET_IFNAME=enp0s19 \
python3 -m sglang.launch_server \
    --model-path deepseek-ai/DeepSeek-V3 \
    --port 30001 \
    --host 0.0.0.0 \
    --tp-size 8 \
    --trust-remote-code \
    --disaggregation-mode decode \
    --disaggregation-transfer-backend nixl \
    --dist-init-addr 0.0.0.0:5757 \
    --moe-dense-tp-size 1 \
    --enable-dp-attention \
    --moe-a2a-backend deepep \
    --deepep-mode normal \
    --disable-cuda-graph \
    --watchdog-timeout 1000000

Step 4: sglang-router (在 Prefill 节点或独立节点上)

pip install --break-system-packages sglang-router

python3 -m sglang_router.launch_router \
    --pd-disaggregation \
    --mini-lb \
    --prefill http://10.8.0.25:30000 \
    --decode http://10.8.0.71:30001 \
    --host 0.0.0.0 \
    --port 30080

Step 5: 测试

# 请求必须发送到 router (port 30080)，不能直接发到 Prefill/Decode
curl http://10.8.0.25:30080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-V3",
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 50
  }'

dist-init-addr 注意事项

CRITICAL: --dist-init-addr 必须使用 0.0.0.0:5757，不能使用特定 IP（如 10.8.0.25:5757）。

原因：SGLang 内部使用 dist-init-addr 进行 ZMQ TCP bind。如果使用特定 IP，Decode 节点会尝试 bind 到 Prefill 节点的 IP，导致 bind 失败。

Testing the Server

To verify the server is working:

# Health check
curl http://localhost:30000/health

# Chat completion test
curl http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-7B-Instruct",
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 50
  }'

Diagnostic Script

To diagnose installation issues, run scripts/diagnose.py:

python3 scripts/diagnose.py

This script checks:

• CUDA installation and version
• PyTorch CUDA compatibility
• SGLang and sgl-kernel versions
• Required library availability
• GPU detection and memory
• LD_LIBRARY_PATH configuration
• DeepEP installation (required for MoE models)

DeepEP Dependency

DeepEP (DeepSeek Expert Parallelism) is required for running MoE (Mixture of Experts) models:

• DeepSeek-V3
• DeepSeek-R1
• Mixtral (with Expert Parallelism)

Detecting DeepEP

The diagnostic script automatically checks for DeepEP. If not installed, it will prompt:

⚠ DeepEP: Not installed
  DeepEP is required for MoE models (DeepSeek-V3, DeepSeek-R1)
  To install DeepEP, use the deepep-installer skill:
    /deepep-installer

Installing DeepEP

If DeepEP is not installed, use the deepep-installer skill:

# Option 1: Use the deepep-installer skill (recommended)
/deepep-installer

# Option 2: Run the installation script directly
bash /path/to/gpu-tpu-pedia/gpu/deepep/install.sh

The deepep-installer skill handles:

• CUDA and gdrcopy setup
• NVSHMEM with IBGDA support
• DeepEP compilation for your GPU architecture
• Environment variable configuration

Workflow Integration

When diagnosing SGLang installation, if DeepEP is missing and the user wants to run MoE models:

1. Detect DeepEP is not installed via diagnose.py
2. Prompt user to run /deepep-installer skill
3. After DeepEP installation, re-run SGLang diagnostic
4. Proceed with SGLang server startup

Recommended Installation Order

For MoE models (DeepSeek, Qwen-MoE), the recommended installation order is:

1. DeepEP first (if needed for MoE models)

   • gdrcopy → NVSHMEM → DeepEP
   • Use the deepep-installer skill

2. Then SGLang

   • SGLang installation script will detect and use DeepEP if available

This ensures DeepEP is properly configured before SGLang tries to use it.

SGLang/vLLM Coexistence

SGLang and vLLM can be installed on the same system, but they have some dependency version conflicts (grpcio, timm, xgrammar, etc.). For production use:

• Recommended: Use separate Python virtual environments
• Alternative: Accept the version mismatches (usually works for basic inference)

Common conflicts when both are installed:

• grpcio: SGLang wants 1.75.1, vLLM may install 1.76.0
• timm: SGLang wants 1.0.16, vLLM may install 1.0.24
• xgrammar: SGLang wants 0.1.27, vLLM may install 0.1.29

Pre-downloading DeepSeek Weights (Optional)

For faster DeepSeek-V3/R1 model loading, you can pre-download weights from GCS instead of HuggingFace:

# Check if already downloaded
DEEPSEEK_PATH="/lssd/huggingface/hub/models--deepseek-ai--DeepSeek-V3"

if [ -d "$DEEPSEEK_PATH" ]; then
    echo "✓ DeepSeek-V3 weights already exist: $DEEPSEEK_PATH"
    du -sh "$DEEPSEEK_PATH"
else
    echo "Downloading DeepSeek-V3 weights from GCS..."
    gcloud storage cp -r gs://chrisya-gpu-pg-ase1/huggingface /lssd/
    echo "✓ DeepSeek-V3 weights downloaded"
fi

Notes:

• GCS bucket gs://chrisya-gpu-pg-ase1/huggingface contains pre-cached DeepSeek-V3 FP8 weights
• Downloading from GCS is much faster than HuggingFace (same-region high bandwidth)
• Weights are ~600GB, including complete safetensors files
• Requires LSSD to be mounted first (use /lssd-mounter skill)

Resources

• scripts/diagnose.py - Diagnostic script for installation issues
• scripts/setup_env.sh - Environment variable setup script
• references/version_matrix.md - Version compatibility matrix
• references/troubleshooting.md - Extended troubleshooting guide

Unified Environment Script

After installing DeepEP + SGLang + vLLM, use the unified environment script:

source /opt/deepep/unified-env.sh

This script sets up all necessary environment variables for DeepEP, NVSHMEM, gdrcopy, and NVIDIA libraries.

Recommended Multi-Framework Installation Order

When installing SGLang alongside DeepEP and vLLM:

1. /lssd-mounter     → Mount high-speed local SSD
2. /deepep-installer → Install DeepEP (已使用 PyTorch 2.9.1)
3. /sglang-installer → Install SGLang (this skill)
4. /vllm-installer   → Install vLLM (可选)

注意: DeepEP 现在默认使用 PyTorch 2.9.1 编译，与 SGLang 0.5.8 保持一致，无需重新编译。

Post-Installation Verification:

source /opt/deepep/unified-env.sh
python3 -c "
import torch; print(f'PyTorch: {torch.__version__}')
import deep_ep; print('DeepEP: OK')
import sglang; print(f'SGLang: {sglang.__version__}')
import sgl_kernel; print(f'sgl-kernel: {sgl_kernel.__version__}')
import vllm; print(f'vLLM: {vllm.__version__}')
"

Version History

• 2026-02-09: DeepGEMM 预编译实战修复 (b3 安装验证)

  • CRITICAL: compile_deep_gemm 必须加 --tp 8，否则单卡加载 DeepSeek-V3 (671B) 会 OOM
  • FIX: DeepGEMM cubin 缓存路径为 ~/.cache/deep_gemm/cache/（非 ~/.deep_gemm/cache/）
  • NEW: 新增 "DeepGEMM compile_deep_gemm OOM" 错误条目
  • 更新所有 compile_deep_gemm 命令示例添加 --tp 8 和 export HF_HOME

• 2026-02-08: PD disaggregation 实战经验 (b2+b3 1P1D 验证)

  • NEW: DeepGEMM 预编译节 (python3 -m sglang.compile_deep_gemm)
  • NEW: sglang-router 安装和使用（PD disaggregation 必须组件）
  • NEW: 1P1D Quick Start 完整部署示例（Prefill + Decode + Router）
  • CRITICAL: --dist-init-addr 必须使用 0.0.0.0:5757，不能使用特定 IP
  • CRITICAL: 客户端请求必须发到 router，直接发到 Prefill/Decode 会报错
  • NEW: 4 个新错误条目（DeepGEMM JIT 超时、bootstrap_room、bootstrap room id、dist-init-addr bind）
  • FIX: 生产 PD disaggregation 示例中的 dist-init-addr 从 <MASTER_IP> 改为 0.0.0.0
  • 添加 sglang-router 到版本表

• 2026-01-29: DeepSeek-V3 FP8 Blackwell compatibility issue

  • CRITICAL: Documented DeepSeek-V3 FP8 outputs garbage on Blackwell (B200) GPUs
  • ROOT CAUSE: FP8 scale format mismatch (mscale vs ue8m0 expected by DeepGEMM)
  • WORKAROUND: Use vLLM instead of SGLang for DeepSeek-V3 on Blackwell
  • Works correctly on H100/A100

• 2026-01-29: Installation experience updates

  • VERSION: Corrected flashinfer version to 0.6.1 (SGLang 0.5.8 requires flashinfer_python==0.6.1)
  • FIX: Added --ignore-installed typing_extensions for Ubuntu 24.04 (system package conflict)
  • FIX: Documented PyTorch user/system directory conflict when mixing pip and sudo pip
  • NOTE: When vLLM is installed after SGLang, flashinfer is downgraded to 0.5.3 (acceptable for inference)

• 2026-01-29: Added PyTorch/sgl-kernel ABI compatibility fixes

  • CRITICAL: Added sgl-kernel ABI incompatibility error and fix
  • CRITICAL: Documented FlashInfer changing PyTorch version issue
  • NEW: Added unified environment script reference
  • NEW: Added recommended multi-framework installation order
  • NEW: Added post-installation verification command

• 2026-01-29: Added GCS DeepSeek weights pre-download

  • NEW: Added "Pre-downloading DeepSeek Weights" section
  • GCS source: gs://chrisya-gpu-pg-ase1/huggingface
  • Faster than HuggingFace download (same-region bandwidth)

• 2026-01-29: Updated to SGLang v0.5.8

  • VERSION BUMP: SGLang 0.5.6.post2 → 0.5.8
  • NEW: Added v0.5.8 highlights (1.5x faster diffusion, chunked pipeline parallelism, EPD)
  • NEW: Added v0.5.7 highlights (Model Gateway v0.3.0, encoder disaggregation)
  • NEW: Added NIXL to Step 3 as recommended dependency
  • Updated git clone command to v0.5.8

• 2026-01-29: Added NIXL transfer backend support

  • NEW: Added NIXL as recommended transfer backend (uses DMA-BUF, no nvidia_peermem needed)
  • NEW: NIXL installation and configuration instructions
  • NEW: NIXL vs Mooncake comparison table
  • NEW: RDMA memory registration error diagnosis and solutions
  • Updated prerequisites to include NIXL as preferred option
  • Added warning about NIXL downgrading NVIDIA libraries

• 2026-01-29: Major update for DeepSeek-V3 disaggregation mode

  • NEW: Added Mooncake Transfer Engine installation instructions
  • NEW: Added prefill-decode disaggregation mode documentation
  • NEW: Added DeepSeek-V3 prefill node deployment example
  • NEW: Added SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK error fix (must be <= 1024)
  • NEW: Added deprecated environment variable warning fix
  • Clarified sgl-kernel version behavior (0.3.19 is pinned by SGLang dependencies)
  • Added note about NVIDIA library reinstallation after install

• 2026-01-28: Updated based on installation experience

  • Added pip installation for Ubuntu 24.04
  • Added recommended installation order (DeepEP first for MoE)
  • Documented SGLang/vLLM dependency conflicts