InternVL3

Deploy InternVL3 on Spheron GPU instances using vLLM. InternVL3 is a vision-language model series from 1B to 78B parameters with strong performance on visual question answering and multimodal reasoning benchmarks.

Recommended hardware

Model	Recommended GPU	Instance Type	Notes
InternVL3-8B	RTX 4090 (24GB)	Dedicated or Spot	Single-GPU
InternVL3-14B	A100 40GB	Dedicated	Full precision
InternVL3-38B	A100 80GB	Dedicated	Single-GPU
InternVL3-78B	2× A100 80GB	Dedicated / Cluster	`--tensor-parallel-size 2`

Manual setup

Use these steps to set up the server manually after SSH-ing into your instance. This works on any provider regardless of cloud-init support.

Step 1: Connect to your instance

ssh <user>@<ipAddress>

Replace <user> with the username shown in the instance details panel (e.g., ubuntu for Spheron AI instances) and <ipAddress> with your instance's public IP.

Step 2: Install vLLM

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

Step 3: Start the server

Run the server in the foreground to verify it works:

python3 -m vllm.entrypoints.openai.api_server \
  --model OpenGVLab/InternVL3-8B \
  --port 8000 \
  --dtype auto \
  --trust-remote-code

Press Ctrl+C to stop. For InternVL3-78B on 2× A100, add --tensor-parallel-size 2.

Step 4: Run as a background service

To keep the server running after you close your SSH session, create a systemd service:

sudo tee /etc/systemd/system/vllm-internvl.service > /dev/null << 'EOF'
[Unit]
Description=InternVL3 vLLM Inference Server
After=network.target
 
[Service]
Type=simple
ExecStart=/usr/bin/python3 -m vllm.entrypoints.openai.api_server \
  --model OpenGVLab/InternVL3-8B \
  --port 8000 \
  --dtype auto \
  --trust-remote-code
Restart=on-failure
RestartSec=10
 
[Install]
WantedBy=multi-user.target
EOF
 
sudo systemctl daemon-reload
sudo systemctl enable vllm-internvl
sudo systemctl start vllm-internvl

Accessing the server

SSH tunnel

ssh -L 8000:localhost:8000 <user>@<ipAddress>

Usage example: image input

import base64
from openai import OpenAI
 
client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
 
with open("image.jpg", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()
 
response = client.chat.completions.create(
    model="OpenGVLab/InternVL3-8B",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}},
                {"type": "text", "text": "Describe what you see in this image."},
            ],
        }
    ],
)
print(response.choices[0].message.content)

Check server logs

journalctl -u vllm-internvl -f

Cloud-init startup script (optional)

If your provider supports cloud-init, you can paste this into the Startup Script field when deploying to automate the setup above.

#cloud-config
runcmd:
  - apt-get update -y
  - apt-get install -y python3-pip
  - pip install vllm
  - |
    cat > /etc/systemd/system/vllm-internvl.service << 'EOF'
    [Unit]
    Description=InternVL3 vLLM Inference Server
    After=network.target
 
    [Service]
    Type=simple
    ExecStart=/usr/bin/python3 -m vllm.entrypoints.openai.api_server \
      --model OpenGVLab/InternVL3-8B \
      --port 8000 \
      --dtype auto \
      --trust-remote-code
    Restart=on-failure
    RestartSec=10
 
    [Install]
    WantedBy=multi-user.target
    EOF
  - systemctl daemon-reload
  - systemctl enable vllm-internvl
  - systemctl start vllm-internvl

For InternVL3-78B on 2× A100, add --tensor-parallel-size 2 to the ExecStart command.

What's next

Multimodal Models: Other vision-language model guides
vLLM Inference Server: vLLM configuration details
Networking: SSH tunneling and port access
Instance Types: Multi-GPU setup for large VLMs