Mistral & Mixtral

Deploy Mistral AI models on Spheron GPU instances using vLLM. Includes Mistral 7B for single-GPU deployment, Mixtral 8x7B MoE (requires ~90 GB VRAM in bfloat16, needs 2× A100 80GB), and Mistral Small 3.1 24B.

Recommended hardware

Model	Recommended GPU	Instance Type	Notes
Mistral-7B-Instruct-v0.3	RTX 4090 (24GB)	Dedicated or Spot	Fits in 16GB VRAM
Mixtral-8x7B-Instruct-v0.1	2× A100 80GB	Dedicated	MoE, ~90 GB VRAM in bfloat16
Mistral-Small-3.1-24B	A100 80GB	Dedicated	Full precision (~55GB VRAM)

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 mistralai/Mistral-7B-Instruct-v0.3 \
  --port 8000 \
  --dtype bfloat16

Press Ctrl+C to stop.

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-mistral.service > /dev/null << 'EOF'
[Unit]
Description=Mistral vLLM Inference Server
After=network.target
 
[Service]
Type=simple
ExecStart=/usr/bin/python3 -m vllm.entrypoints.openai.api_server \
  --model mistralai/Mistral-7B-Instruct-v0.3 \
  --port 8000 \
  --dtype bfloat16
Restart=on-failure
RestartSec=10
 
[Install]
WantedBy=multi-user.target
EOF
 
sudo systemctl daemon-reload
sudo systemctl enable vllm-mistral
sudo systemctl start vllm-mistral

Mixtral 8x7B (2× A100 80GB)

For the Mixtral MoE model, replace the ExecStart command with:

/usr/bin/python3 -m vllm.entrypoints.openai.api_server \
  --model mistralai/Mixtral-8x7B-Instruct-v0.1 \
  --port 8000 \
  --dtype bfloat16 \
  --tensor-parallel-size 2

Accessing the server

SSH tunnel

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

Usage example: function calling

from openai import OpenAI
import json
 
client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
 
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for information",
            "parameters": {
                "type": "object",
                "properties": {"query": {"type": "string"}},
                "required": ["query"],
            },
        },
    }
]
 
response = client.chat.completions.create(
    model="mistralai/Mistral-7B-Instruct-v0.3",
    messages=[{"role": "user", "content": "Search for the latest GPU benchmarks."}],
    tools=tools,
    tool_choice="auto",
)
 
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

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.

Mistral 7B (RTX 4090)

#cloud-config
runcmd:
  - apt-get update -y
  - apt-get install -y python3-pip
  - pip install vllm
  - |
    cat > /etc/systemd/system/vllm-mistral.service << 'EOF'
    [Unit]
    Description=Mistral vLLM Inference Server
    After=network.target
 
    [Service]
    Type=simple
    ExecStart=/usr/bin/python3 -m vllm.entrypoints.openai.api_server \
      --model mistralai/Mistral-7B-Instruct-v0.3 \
      --port 8000 \
      --dtype bfloat16
    Restart=on-failure
    RestartSec=10
 
    [Install]
    WantedBy=multi-user.target
    EOF
  - systemctl daemon-reload
  - systemctl enable vllm-mistral
  - systemctl start vllm-mistral

Mixtral 8x7B (2× A100 80GB)

Replace the ExecStart line with:

/usr/bin/python3 -m vllm.entrypoints.openai.api_server \
  --model mistralai/Mixtral-8x7B-Instruct-v0.1 \
  --port 8000 \
  --dtype bfloat16 \
  --tensor-parallel-size 2

What's next

vLLM Inference Server: vLLM configuration details
Llama 3.1 / 3.2 / 3.3: Meta Llama model guides
Instance Types: GPU selection for MoE models
Cost Optimization: Spot instances for Mistral 7B