Testing Local LLMs in Practice: Code Generation, Quality vs. Speed
Use the published harness to benchmark local LLMs for autonomous Go code generation, measuring compilation success, schema validation, and throughput.
Run the harness on your local LLMs to evaluate Go code generation performance.
Summary
The author built an AI agent that autonomously writes Go code using local LLMs, targeting log parser generation for SIEM pipelines. To evaluate the agent, a harness was created that generates real Go parsers, compiles the code, validates extracted fields and types, measures parsing quality against expected schemas, and tracks throughput over long runs. The benchmark is publicly available and provides a methodology for objectively measuring autonomous coding tasks. The results show how different open‑weight models perform in terms of quality and speed. The harness can be used to compare models side‑by‑side on the same workload. The article invites feedback and asks which model should be tested next.
The benchmark demonstrates that local LLMs can produce functional Go code, but the quality and speed vary across models. The public release of the harness allows developers to replicate the tests and adapt them to their own pipelines.
Key changes
- Generates real Go parsers
- Compiles the code
- Validates extracted fields and types
- Measures parsing quality against expected schemas
- Tracks throughput/speed over long runs
- Public benchmark methodology available
- Results show quality vs. speed trade‑offs
- Invites community feedback