---
title: "Multi-Node Deployment"
metatags:
    description: "SGLang multi-node: Llama 405B on 2 nodes, DeepSeek V3/R1, SLURM cluster deployment examples."
---
## Llama 3.1 405B

**Run 405B (fp16) on Two Nodes**

```bash Command
# replace 172.16.4.52:20000 with your own node ip address and port of the first node

python3 -m sglang.launch_server \
  --model-path meta-llama/Meta-Llama-3.1-405B-Instruct \
  --tp 16 \
  --dist-init-addr 172.16.4.52:20000 \
  --nnodes 2 \
  --node-rank 0

python3 -m sglang.launch_server \
  --model-path meta-llama/Meta-Llama-3.1-405B-Instruct \
  --tp 16 \
  --dist-init-addr 172.16.4.52:20000 \
  --nnodes 2 \
  --node-rank 1
```

Note that LLama 405B (fp8) can also be launched on a single node.

```bash Command
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 --tp 8
```

## DeepSeek V3/R1

Please refer to [DeepSeek documents for reference](../../basic_usage/deepseek_v3#running-examples-on-multi-node).

## Multi-Node Inference on SLURM

This example showcases how to serve SGLang server across multiple nodes by SLURM. Submit the following job to the SLURM cluster.

```bash Command
#!/bin/bash -l

#SBATCH -o SLURM_Logs/%x_%j_master.out
#SBATCH -e SLURM_Logs/%x_%j_master.err
#SBATCH -D ./
#SBATCH -J Llama-405B-Online-Inference-TP16-SGL

#SBATCH --nodes=2
#SBATCH --ntasks=2
#SBATCH --ntasks-per-node=1  # Ensure 1 task per node
#SBATCH --cpus-per-task=18
#SBATCH --mem=224GB
#SBATCH --partition="lmsys.org"
#SBATCH --gres=gpu:8
#SBATCH --time=12:00:00

echo "[INFO] Activating environment on node $SLURM_PROCID"
if ! source ENV_FOLDER/bin/activate; then
    echo "[ERROR] Failed to activate environment" >&2
    exit 1
fi

# Define parameters
model=MODEL_PATH
tp_size=16

echo "[INFO] Running inference"
echo "[INFO] Model: $model"
echo "[INFO] TP Size: $tp_size"

# Set NCCL initialization address using the hostname of the head node
HEAD_NODE=$(scontrol show hostname "$SLURM_NODELIST" | head -n 1)
NCCL_INIT_ADDR="${HEAD_NODE}:8000"
echo "[INFO] NCCL_INIT_ADDR: $NCCL_INIT_ADDR"

# Launch the model server on each node using SLURM
srun --ntasks=2 --nodes=2 --output="SLURM_Logs/%x_%j_node$SLURM_NODEID.out" \
    --error="SLURM_Logs/%x_%j_node$SLURM_NODEID.err" \
    python3 -m sglang.launch_server \
    --model-path "$model" \
    --grammar-backend "xgrammar" \
    --tp "$tp_size" \
    --dist-init-addr "$NCCL_INIT_ADDR" \
    --nnodes 2 \
    --node-rank "$SLURM_NODEID" &

# Wait for the NCCL server to be ready on port 30000
while ! nc -z "$HEAD_NODE" 30000; do
    sleep 1
    echo "[INFO] Waiting for $HEAD_NODE:30000 to accept connections"
done

echo "[INFO] $HEAD_NODE:30000 is ready to accept connections"

# Keep the script running until the SLURM job times out
wait
```

Then, you can test the server by sending requests following other [documents](../../basic_usage/openai_api_completions).

Thanks for [aflah02](https://github.com/aflah02) for providing the example, based on his [blog post](https://aflah02.substack.com/p/multi-node-llm-inference-with-sglang).
