Fine-Tune the biggest open-source models (even with a bad PC)
David Ondrej
Jul 7, 2026
David Andre demonstrates how to perform supervised fine-tuning on large-scale open-source models like Kim K2.7 using LoRA (Low-Rank Adaptation). By leveraging cloud-based GPU platforms and high-quality datasets, developers can build custom, specialized AI models without the prohibitive $100,000 cost of local hardware, significantly outperforming generic models in specific domains.
Key insight: Fine-tuning a trillion-parameter model using LoRA requires training only a small adapter layer rather than the entire base model, reducing the cost of high-compute GPU usage to less than $40 for a full training cycle.
