Performance
The current LordCoder docs are strongest when they stay practical about speed, memory, and model fit. This page turns that guidance into a clearer reliability story for developers choosing between fast iteration and heavier reasoning.
Performance fit
LordCoder’s performance guide is unusually practical: it describes the tradeoff between speed, memory, and coding power in a way developers can actually act on.
Balanced default
The docs recommend `qwen2.5-coder:14b` as the sweet spot for the documented i5-10400 and 24GB RAM setup. That keeps the product story honest: enough capability for real work, without pretending every local machine should run the largest option.
Operational advantage
You can move between 7B, 14B, and 32B based on task shape, available memory, and response-time expectations. That level of control is part of why local-first AI can be more dependable for privacy-conscious engineering teams.
Use 7B when you want quick debugging loops, lighter requests, or you are operating on a more constrained machine.
Use 14B for most day-to-day coding work and the best overall fit to the current project guidance.
Use 32B when the project and machine justify more expensive reasoning for complex codebase changes.