Why your local LLM feels dumber than it is
What happened
Local large language models often seem less intelligent than their cloud‑based counterparts because they run with lower‑precision quantization, smaller context windows, and limited prompt engineering (e.g., missing system messages). These technical constraints, not a fundamental lack of knowledge, make them appear slower, forgetful, or less coherent, especially when compared to the polished APIs of hosted services.
Why it matters
Understanding these bottlenecks shows that the perceived performance gap is largely engineering‑driven; as hardware gets cheaper and tooling improves, on‑device LLMs will become more competitive, boosting privacy‑focused and cost‑effective AI deployments.
Is this a big deal?
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