6 ways to reduce OpenClaw RAM usage
1 Unload models when not coding
Run 'ollama stop <model>' when you step away. Ollama keeps models loaded in memory by default, consuming the full VRAM allocation even when idle. On a 24 GB Mac Mini, this is the single biggest win.
2 Match model size to your Mac Mini
24 GB Mac Mini: stick to 9B models (Q4_K_M, ~5.5 GB). 36 GB: you can run 14B models comfortably. 48 GB Mac Mini Pro: 32B models fit with headroom for dev tools. 64 GB+: 70B models become viable.
3 Close Chrome before loading large models
Chrome easily consumes 8–15 GB. On a constrained Mac Mini, closing Chrome before loading a model can be the difference between smooth inference and swap thrashing.
4 Use Q4_K_M quantization
Q4_K_M offers the best quality-to-size ratio. Q8_0 sounds better but uses nearly double the RAM for marginal quality gains — not worth it on a Mac Mini.
5 Watch for swap, not just RAM
macOS will happily swap to SSD rather than killing processes. Your model will still 'run' but at 10x slower inference. DevPulse's swap tracking catches this before you notice the slowdown.
6 Consider the Mac Mini M4 Pro (48 GB)
If you're serious about local AI coding, the 48 GB Mac Mini Pro is the sweet spot. It runs Qwen 2.5 Coder 32B at Q4_K_M with 28 GB to spare for Chrome, Docker, and your IDE.
How DevPulse helps with OpenClaw
DevPulse groups OpenClaw's agent processes and the model backend (Ollama/LM Studio) into one unified view. It shows the combined memory cost, tracks model load/unload events, and warns you when remaining headroom is dangerously low — before macOS starts swapping and your inference speed collapses.
Instead of guessing how much RAM OpenClaw consumes or manually checking Activity Monitor, DevPulse gives you a clear, always-visible answer in your menu bar.