February 22, 2026
The most expensive mistake in local LLM adoption is running a 70B model for a task that a 3B model handles at 20x the speed for equivalent quality. The second most expensive mistake is running a 3B model on a task that requires 32B-level reasoning and getting garbage output.
Matching model size to task complexity is the core skill. This guide provides a framework grounded in empirical benchmarks, not marketing claims.
February 22, 2026
Prompting a 7B local model is not the same as prompting Claude or GPT-4. Cloud models are overtrained on instruction following, tolerate vague prompts, and self-correct. Small local models need more structure, more constraints, and more explicit formatting instructions. The prompts that work effortlessly on cloud models often produce garbage on local models.
This is not a weakness — it is a design consideration. Local models trade generality for speed and cost. Your prompts must compensate by being more specific.