Question 1

Can I run OpenClaw with Ollama completely for free?

Accepted Answer

Yes — for simple tasks. Ollama is free, no API keys required, and all its models work with OpenClaw. Your only costs are electricity and hardware you already own. The limitation is capability: local 7B–14B models handle classification, triage, and formatting well but struggle with complex multi-step reasoning. For a morning briefing or email categorization, free is genuinely free. For a full production agent doing code generation and complex analysis, you'll still want a cloud fallback for the hard tasks.

Question 2

What hardware do I need to run Ollama for OpenClaw?

Accepted Answer

1B–3B models run on 8GB RAM (even a Raspberry Pi 5). 7B–8B models need 16GB minimum. 14B models want 32GB RAM or 16–24GB VRAM. 27B+ models need 24GB+ VRAM or 32GB unified memory on Apple Silicon. Important: OpenClaw's 17K-token system prompt adds 4–8GB via KV cache on top of model weights, so plan for more headroom than vanilla Ollama benchmarks suggest. The practical test: if your model generates fewer than 10 tokens/second, it's too large for your hardware. Watch for GitHub issue #35436 — the agent bootstrap currently hardcodes 128K context, ignoring lower num_ctx settings.

Question 3

Is it worth renting a GPU VPS to run local models?

Accepted Answer

Almost never. A GPU VPS costs $100+/month, which negates the cost savings of running locally. At that price, you'd get better capability from cloud API credits with zero maintenance overhead. The only scenario where a GPU VPS makes sense is when you need both local-level privacy (data doesn't leave your infrastructure) and GPU capability beyond what your local hardware provides — and even then, the cost math rarely favors it over a hybrid local+cloud setup.

Question 4

Which model should I start with?

Accepted Answer

A 14B model. It's the sweet spot: fast enough on consumer hardware (40+ tokens/second on Apple Silicon), capable enough for classification and formatting tasks, and small enough that it doesn't monopolize your GPU. If you confirm it runs well and want to push capability higher, try GLM-5 (77.8% SWE-bench Verified) or Qwen3.5 27B (72.4% SWE-bench). Note: Qwen 3.5 27B currently has a tool-calling bug in Ollama, so if your workflows are tool-heavy, GLM-5 may be the safer choice right now. For high-throughput routing tasks, the Qwen3.5 35B-A3B MoE variant hits 112 tok/s on an RTX 3090. Start small, confirm speed, then scale up.

Question 5

How does hybrid routing work in practice?

Accepted Answer

You configure a fallback chain in agents.defaults.model.fallbacks. Your primary model handles every request; if it fails or rate-limits, the next model in the chain takes over automatically. OpenClaw includes a circuit breaker — when the primary fails N times within a window, all requests auto-route to the fallback with no per-request retry delay. Email categorization, data formatting, and triage go to Ollama (local, free). Complex reasoning, code generation, and long-context analysis go to OpenRouter (cloud, metered). Sensitive data stays local; complex tasks escalate to cloud models. The agent keeps running even if one provider has an outage.

Question 6

How much can hybrid routing actually save?

Accepted Answer

A well-optimized hybrid setup runs $80–$120/month total. An unoptimized cloud-only setup runs $300+/month. One user on r/AIToolsPerformance reported API costs dropping 6x after switching to hybrid routing with GLM-5 locally. If you invest in local hardware, the break-even point versus cloud APIs is roughly 8 months — dropping to 3–4 months with heavy usage (100M+ tokens/month). After that crossover, every local token is effectively free. Managed Care's quarterly optimization typically finds 20–40% additional savings by adjusting which tasks route to which models based on your actual usage data.

Question 7

Does Ollama work with all OpenClaw features?

Accepted Answer

Yes. All models from Ollama work with OpenClaw — it's an official provider as of March 2026. Tools, skills, system prompts, and agent configuration all function the same way regardless of whether the underlying model is local or cloud-hosted. One caveat: Qwen 3.5 27B currently has a tool-calling bug in Ollama (requests route through the wrong format pipeline), so test tool-heavy workflows carefully with that specific model. The broader variable is model capability: a 7B local model may not follow complex multi-step tool-calling instructions as reliably as a frontier cloud model. That's a model limitation, not an integration limitation.

Rakesh Patel

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