Testing Local LLMs in Practice: Code Generation, Quality vs. Speed
Over the past few months, the landscape of open-weight large language models has changed dramatically. New models are being released at a pace that makes systematic evaluation difficult, yet increasingly necessary.
Most LLM comparisons still rely on synthetic benchmarks. While useful, they often fail to answer a more practical question:
How do these models perform in a real, production-like task?
This article presents a structured evaluation of local LLMs based on a concrete, repeatable, and measurable workload: autonomous code generation. Specifically, we focus on how well an autonomous agent, backed by these models, generates production-ready log parsers, and how that performance scales in terms of quality vs. speed.