OpenClaw Local Models for 16 GB RAM

With 16 GB of shared system memory, stability matters more than loading the largest possible model. Leave enough headroom for the operating system, Ollama, the OpenClaw Gateway, context cache, and any tools the agent launches.

The official local-model runbook sets a much higher hardware expectation for a comfortable, full agent loop. Treat 16 GB as a constrained experimentation setup for compact models and narrow tool surfaces, not as a replacement for a strong hosted model on high-risk tasks.

Start conservatively

Begin with a compact, quantized instruct model that declares native tool support. Test one model at a time and close memory-heavy applications during the first measurement.

ollama list
openclaw models list --provider ollama

Model sizes, quantizations, and context requirements vary. Use the runtime’s actual memory reading rather than assuming that a parameter count guarantees a fit.

Limit the first context

Large context windows consume additional memory. Start with the shortest context that covers the intended workflow, then increase it only after the model completes a multi-step task without swapping or being killed.

Test under realistic load

Run a plain prompt, a file read, a structured tool call, and a short two-tool sequence. While it runs, watch memory pressure, latency, and whether the process swaps.

If the model barely fits at an empty prompt, choose a smaller quantization or model. Sustained swapping usually makes an interactive agent feel stalled and makes multi-step testing misleading.

Keep a hosted fallback

A local primary can be paired with a stronger hosted fallback for tasks that exceed the machine’s capability, provided that sending those prompts to a remote provider matches your privacy policy.

Expected result

The model loads with usable memory headroom, responds without sustained swapping, and passes the same native tool-call test several times in fresh sessions.