Local LLMs spark big-vs-small debate

The real tradeoff is capability versus systems complexity, not just parameter count. A single large model is simpler to serve and share across users because you load it once and manage kv cache centrally. Smaller models can punch above their weight after fine-tuning, especially when the task is narrow and the eval target is clear. Better retrieval often closes more of the quality gap than adding more parameters, which is why RAG keeps coming up in the thread. A multi-model stack only works well if you also build routing, orchestration, and fallback logic; otherwise it mostly adds latency and fragility. For local deployments, hardware constraints like memory bandwidth, concurrency, and VRAM fit often matter as much as raw model size.