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 walks through how to connect a GPU-hosted LLM (local or cloud) to OpenClaw.ai, so your model can execute actions, call tools, and stay under your control.

No magic. No SaaS lock-in. Just a clean control plane.

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Why Connect Your Own LLM to OpenClaw?

If you're already 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

That's exactly what OpenClaw is designed for.

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

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Architecture Overview

At a high level, 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
3. OpenClaw decides whether it's allowed
4. Tool executes in a sandbox
5. Result flows back to the LLM

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Step 1: Run Your LLM on a GPU

You can host your model however you want, as long as it exposes an HTTP API.

Common setups:

• vLLM (recommended for throughput)
• Ollama (simple + local)
• TGI / Text Generation Inference
• Custom FastAPI / Flask wrapper

Example (FastAPI):

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Prompt(BaseModel):
    input: str

@app.post("/generate")
def generate(prompt: Prompt):
    output = llm.generate(prompt.input)
    return {"text": output}

Your GPU does one job only: generate text.

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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
• A policy engine
• A tool registry
• Audit logs

Think of it as a control plane for AI actions.

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Step 3: Register Your LLM as a Reasoning Engine

Tell OpenClaw where your model lives:

llm:
  name: gpu-llm
  type: http
  endpoint: http://gpu-llm:8000/generate
  timeout_ms: 30000

OpenClaw does not need your weights. It just needs a clean interface.

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Step 4: Define Tools Your LLM Can Use

This is where OpenClaw shines.

Example: allow safe shell commands.

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

Or API calls:

tools:
  - name: create_ticket
    type: http
    method: POST
    url: https://api.internal.com/tickets

Your LLM can request tools. OpenClaw decides if they're allowed.

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Step 5: Let the LLM Call Tools (Safely)

Your prompt format might look like:

You are an agent.
If an action is required, respond in JSON:

{
  "tool": "list_files",
  "reason": "Check directory contents"
}

OpenClaw:

• Parses the intent
• Validates permissions
• Executes the tool
• Returns structured output

Your LLM never touches the system directly.

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Step 6: Observe, Audit, and Iterate

Every action is logged:

• Tool name
• Inputs / outputs
• Execution time
• Success / failure

This matters when:

• You deploy to teams
• You expose agents to users
• You care about security reviews

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Common Deployment Patterns

Local GPU + OpenClaw

• Ideal for research and experimentation

Cloud GPU + OpenClaw

• Production agents
• Team access
• Strong isolation

Multiple LLMs, One OpenClaw

• Fast model for routing
• Big model for reasoning
• Same execution layer

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What This Buys You

By separating reasoning from execution, you get:

• 🔐 Strong security boundaries
• 🧠 Model flexibility (swap anytime)
• 🛠 Tool reuse across agents
• 📊 Real observability
• 🚫 No prompt-based "hope and pray" safety

This is the difference between chatbots and real agents.

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Final Thoughts

If you're serious enough to host your own GPU, you're serious enough to control execution.

OpenClaw gives you:

• The freedom of self-hosted LLMs
• The discipline of production systems

Your model thinks. OpenClaw acts.

If you want, next we can:

• Add streaming
• Add multi-agent routing
• Lock this down for enterprise
• Or turn this into a "WP Engine for AI agents" style deployment

Just say the word.

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Deploy OpenClaw with Clawctl — control plane for your GPU-hosted LLM.