Surviving 12GB VRAM : Autonomous Memory Management for Local QLoRA Fine Tuning

Local LLM training has a dirty secret. Everyone talks about the magic of custom weights, but nobody talks about the grueling reality of babysitting PyTorch scripts. You set up your data, configure your parameters, hit run, and walk away. Twenty minutes later, you come back to the dreaded CUDA out of memory stack trace. The pipeline is broken, and your 12GB RTX 3060 is choking on memory fragmentation. The bottleneck is not your hardware. It is the lack of autonomous memory management. While building out workflows and analyzing business intelligence at Ensono, I realized that manual intervention at every Out Of Memory failure destroys scalability. We need systems that adapt to the VRAM ceiling on the fly. 🏗️ Enter VikaasLoop This exact pain point is why I built VikaasLoop . It is an autonomous 5-agent swarm designed to completely eliminate the manual bottleneck in the optimization lifecycle. While the DataGen Agent leverages Gemini 2.0 Flash for synthetic dataset generation and the Eval