OpenClaw exposes local agent overhead on older Macs

This looks less like a “small model” problem and more like an “agent runtime overhead” problem: OpenClaw adds enough coordination, tooling, and memory pressure that borderline hardware can fall over even when the base model seems fine alone.

• –Running a 0.8B model in a terminal only tests inference speed, not the extra overhead of an always-on agent framework
• –OpenClaw is positioned as a personal AI agent layer, so responsiveness depends on orchestration, tool calls, and system resources, not just parameter count
• –CPU spikes and near-maxed RAM are strong signals that the machine is hitting system limits before model quality becomes the real bottleneck
• –For local-only setups on older Macs, heavily quantized small models or a lighter agent stack are more realistic than expecting full OpenClaw performance
• –This is useful signal for AI developers because local-agent UX still depends heavily on hardware headroom, not just whether a model can technically load