Run Local Models with OpenClaw: Hardware & Setup Guide

Running everything in the cloud is easy until you see the monthly invoice or start handling sensitive data you would rather keep on your own hardware. Moving to a local setup gives you back control, but it usually comes with a trade-off in performance or a lot of configuration pain.

Local is doable, but OpenClaw expects large context and strong defenses against prompt injection. Small cards truncate context and leak safety. Aim high: ≥2 maxed-out Mac Studios or equivalent GPU rig (~$30k+). A single 24 GB GPU works only for lighter prompts with higher latency. Use the largest / full-size model variant you can run; aggressively quantized or “small” checkpoints raise prompt-injection risk (see Security).

If you want the lowest-friction local setup, start with Ollama and openclaw onboard. This page is the opinionated guide for higher-end local stacks and custom OpenAI-compatible local servers.

Recommended: LM Studio + large local model (Responses API)

This is the best current local stack. Load a large model in LM Studio (for example, a full-size Qwen, DeepSeek, Llama, or other large builds), enable the local server (default http://127.0.0.1:1234), and use Responses API to keep reasoning separate from final text.

{
  agents: {
    defaults: {
      model: { primary: “lmstudio/my-local-model” },
      models: {
        “anthropic/claude-opus-4-6”: { alias: “Opus” },
        “lmstudio/my-local-model”: { alias: “Local” },
      },
    },
  },
  models: {
    mode: “merge”,
    providers: {
      lmstudio: {
        baseUrl: “http://127.0.0.1:1234/v1”,
        apiKey: “lmstudio”,
        api: “openai-responses”,
        models: [
          {
            id: “my-local-model”,
            name: “Local Model”,
            reasoning: false,
            input: [“text”],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 196608,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
}

Setup checklist

• Install LM Studio: https://lmstudio.ai
• In LM Studio, download the largest model build available (avoid “small”/heavily quantized variants), start the server, and confirm http://127.0.0.1:1234/v1/models lists it.
• Replace my-local-model with the actual model ID shown in LM Studio.
• Keep the model loaded; cold-load adds startup latency.
• Adjust contextWindow/maxTokens if your LM Studio build differs.
• For WhatsApp, stick to Responses API so only final text is sent.

Keep hosted models configured even when running local; use models.mode: "merge" so fallbacks stay available.

Hybrid config: hosted primary, local fallback

{
  agents: {
    defaults: {
      model: {
        primary: "anthropic/claude-sonnet-4-6",
        fallbacks: ["lmstudio/my-local-model", "anthropic/claude-opus-4-6"],
      },
      models: {
        "anthropic/claude-sonnet-4-6": { alias: "Sonnet" },
        "lmstudio/my-local-model": { alias: "Local" },
        "anthropic/claude-opus-4-6": { alias: "Opus" },
      },
    },
  },
  models: {
    mode: "merge",
    providers: {
      lmstudio: {
        baseUrl: "http://127.0.0.1:1234/v1",
        apiKey: "lmstudio",
        api: "openai-responses",
        models: [
          {
            id: "my-local-model",
            name: "Local Model",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 196608,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
}

Local-first with hosted safety net

Swap the primary and fallback order; keep the same providers block and models.mode: "merge" so you can fall back to Sonnet or Opus when the local box is down.

Regional hosting / data routing

• Hosted MiniMax, Kimi, GLM, or similar variants also exist on OpenRouter with region-pinned endpoints (e.g., US-hosted). Pick the regional variant there to keep traffic in your chosen jurisdiction while still using models.mode: "merge" for Anthropic/OpenAI fallbacks.
• Local-only remains the strongest privacy path; hosted regional routing is the middle ground when you need provider features but want control over data flow.

Other OpenAI-compatible local proxies

vLLM, LiteLLM, OAI-proxy, or custom gateways work if they expose an OpenAI-style /v1 endpoint. Replace the provider block above with your endpoint and model ID:

{
  models: {
    mode: "merge",
    providers: {
      local: {
        baseUrl: "http://127.0.0.1:8000/v1",
        apiKey: "sk-local",
        api: "openai-responses",
        models: [
          {
            id: "my-local-model",
            name: "Local Model",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 120000,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
}

Keep models.mode: "merge" so hosted models stay available as fallbacks.

Troubleshooting

• Gateway can reach the proxy? curl http://127.0.0.1:1234/v1/models.
• LM Studio model unloaded? Reload; cold start is a common “hanging” cause.
• Context errors? Lower contextWindow or raise your server limit.
• Safety: local models skip provider-side filters; keep agents narrow and compaction on to limit prompt injection blast radius.

Need more help with your configuration? Check out the AI Setup Assistant.

Next steps

• Security Guide
• Ollama Provider