3B models top 7B for hallucination-free local LLM tasks

The "bigger is better" narrative is hitting a wall in local inference as 3B models prove that surgical training beats brute-force scaling.

• –Dataset curation (like the "Tiny" series or Phi-3) is proving more vital for faithfulness than model size
• –7B models often suffer from "intelligence degradation" during aggressive quantization, while 3B models remain stable at higher precision within the same VRAM footprint
• –KL Divergence (KLD) is emerging as a superior metric to Perplexity for measuring how much a model "lies" after compression
• –Smaller models are increasingly preferred for deterministic tasks like extraction and summarization where "creative" reasoning is a bug, not a feature
• –This trend accelerates the viability of high-performance LLMs on edge devices and mobile hardware