OpenClaw users debate local inference stack

This is a useful snapshot of where local agent infrastructure is headed: the bottleneck is no longer just model quality, but which serving layer can make a multi-model setup actually usable on finite hardware.

• –The proposed stack mixes orchestration, coding, research, and execution models, which makes backend compatibility more important than raw benchmark speed alone.
• –SGLang and vLLM usually matter when operators want high-throughput GPU serving, while llama.cpp stays relevant for simpler local setups, quantized models, and tighter memory budgets.
• –The post highlights a real trend in self-hosted AI: power users are moving from single-chatbot workflows toward agent systems that need production-like inference choices even on personal hardware.