Case study · 2025–2026
Open-Weight LLM Benchmarking
A reproducible evaluation framework for choosing local language models based on capability and inference trade-offs.
- Discipline
- AI systems · Performance engineering
- Technology
- Python · Ollama · Gemma · DeepSeek · Qwen
Overview
I compared Gemma, GPT-OSS, DeepSeek, and Qwen across reasoning, code generation, and local inference latency to understand which models fit different offline workloads.
System map
How the pieces connect
The challenge
What made the problem worth solving
Model selection is rarely a simple leaderboard problem. A model that performs well on reasoning may be too slow for an interactive local application, while a fast model may fail the workload that matters.
The approach
Turning the problem into an engineering plan
- 01
Defined repeatable reasoning and code-generation prompts rather than relying on one-off impressions.
- 02
Measured inference latency in the same local runtime to keep comparisons meaningful.
- 03
Documented trade-offs as a selection framework that downstream projects could use instead of treating one model as universally best.
Result
The work turned model experimentation into an engineering decision process, connecting evaluation evidence to real deployment constraints.