How to Connect Your GPU-Hosted LLM to OpenClaw.ai

How to Connect Your GPU-Hosted LLM to OpenClaw.ai

Running your own LLM on a GPU is empowering. Connecting it safely to real tools is the hard part. This guide shows you how to connect a GPU-hosted LLM to OpenClaw—so your model can execute actions, call tools, and stay under your control.

Why Connect Your Own LLM to OpenClaw?

If you're hosting an LLM on a GPU (AWS, GCP, Lambda Labs, on-prem, or DGX), you probably want:

• Full control over models, weights, and prompts
• Predictable latency and cost
• The ability to execute tools (CLI, APIs, workflows)
• A secure boundary between reasoning and execution

OpenClaw doesn't replace your model—it wraps it with structure, permissions, and execution safety.

Architecture Overview

You're splitting responsibilities:

• Your GPU → reasoning, planning, text generation
• OpenClaw → tool execution, safety, permissions, observability

Flow:

1. User or system prompt hits your LLM
2. LLM decides what to do (returns a tool_calls response)
3. OpenClaw decides whether it's allowed
4. Tool executes in a sandbox
5. Result flows back to the LLM

Step 1: Run Your LLM with an OpenAI-Compatible API

Modern inference servers expose OpenAI-compatible endpoints out of the box. No custom wrappers needed.

vLLM (recommended for throughput):

vllm serve meta-llama/Llama-3.1-70B-Instruct \
  --enable-auto-tool-choice \
  --tool-call-parser llama3_json

Ollama (simple + local):

ollama serve  # Exposes OpenAI-compatible API at localhost:11434/v1

Both support native tool calling via the tools parameter—models like Llama 3.1, Mistral, and Command-R+ return structured tool_calls responses automatically.

Step 2: Install OpenClaw

OpenClaw runs as a separate service (local or remote):

curl -fsSL https://openclaw.ai/install.sh | bash

This gives you a secure execution runtime, policy engine, tool registry, and audit logs.

Want to skip the setup? Clawctl deploys a production-ready OpenClaw instance in 60 seconds—with SSL, auth, and security policies pre-configured.

Step 3: Register Your LLM as a Reasoning Engine

Tell OpenClaw where your model lives:

llm:
  name: gpu-llm
  type: openai-compatible
  base_url: http://gpu-llm:8000/v1  # vLLM
  # base_url: http://localhost:11434/v1  # Ollama
  model: meta-llama/Llama-3.1-70B-Instruct
  timeout_ms: 30000

OpenClaw doesn't need your weights—just a clean interface to your existing API.

Step 4: Define Tools Your LLM Can Use

Following OWASP's AI Agent Security guidelines, apply least-privilege permissions:

tools:
  - name: list_files
    type: shell
    command: ls
    sandbox: true
    permissions:
      - read_only  # Least privilege

  - name: create_ticket
    type: http
    method: POST
    url: https://api.internal.com/tickets
    requires_approval: true  # Human-in-the-loop for sensitive actions

Your LLM can request tools. OpenClaw validates permissions, runs pre-execution checks, and decides if they're allowed.

Step 5: Let the LLM Call Tools (Safely)

With OpenAI-compatible tool calling, your LLM returns structured responses:

{
  "tool_calls": [{
    "function": {
      "name": "list_files",
      "arguments": "{\"path\": \"/home/user\"}"
    }
  }]
}

OpenClaw intercepts this, validates against your policy, executes in a sandboxed environment (using gVisor-style isolation), and returns the result. Your LLM never touches the system directly.

Step 6: Observe, Audit, and Iterate

Every action is logged: tool name, inputs/outputs, execution time, success/failure, and the user who triggered it. Essential for security reviews, compliance, and debugging production agents.

Common Deployment Patterns

What This Buys You

By separating reasoning from execution, you get:

• 🔐 Strong security boundaries (sandbox isolation, least privilege)
• 🧠 Model flexibility (swap Llama for Mistral without changing tools)
• 🛠 Tool reuse across agents
• 📊 Full audit trail for every action
• 🚫 No prompt-based "hope and pray" safety

This is the difference between chatbots and real agents.

Your model thinks. OpenClaw acts.

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Ready to Deploy?

Setting up OpenClaw yourself works—but production deployments need SSL, authentication, backups, and security hardening.

Clawctl handles all of this in one command:

clawctl deploy --plan starter

You get a managed OpenClaw instance connected to your GPU-hosted LLM, with enterprise security out of the box. Start your deployment →