Fine-tuning Nemotron to Replace GPT-class Models: 50% Cost Drop, 4% Accuracy Boost
baseten · x · 2026-08-12
CodeRabbit collaborated with NVIDIA and Baseten on a model fine-tuning experiment to optimize routing decisions for code reviews. They performed a two-stage post-training process (including SFT and RLVR) on the NVIDIA Nemotron 3.5 Lightning model using CodeRabbit's historical routing data.
Key Results:
- Fast Training: The entire fine-tuning process took under 3 hours.
- Low Cost: Total training cost was less than $100.
- Performance: Achieved about 4% higher accuracy than the previous GPT-class model.
- Inference Savings: Reduced estimated inference costs by roughly 50%.
This demonstrates that for specific high-volume tasks, fine-tuning a smaller specialized model with unique data can outperform larger models while significantly cutting costs.
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