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.
February 22, 2026
Small models (2-7B parameters) produce structured output that is 85-95% as accurate as cloud APIs for well-defined extraction and classification tasks. The key is constraining the output space so the model’s limited reasoning capacity is focused on filling fields rather than deciding what to generate.
This is where local models genuinely compete with — and sometimes match — models 30x their size.
Ollama’s JSON mode forces the model to produce valid JSON:
February 22, 2026
A 32B model with a 32K context window can process roughly 8-10 source files at once. A real codebase has hundreds. Concatenating everything into one prompt fails — the context overflows, quality degrades, and the model either truncates or hallucinates connections.
The two-pass pattern solves this by splitting analysis into two stages:
This effectively multiplies your context window by the compression ratio of summarization — typically 10-20x. A 32K context that handles 10 files directly can handle 100-200 files through summaries.