QLoRA Consultation — Fine-Tune Large Models on Consumer Hardware

I will assess your hardware and model requirements and deliver a written QLoRA strategy consultation — covering whether QLoRA's 4-bit quantised fine-tuning can achieve your quality target on your available hardware, the quality tradeoff versus standard LoRA and full fine-tuning, optimal quantisation configuration, and a realistic training budget estimate. QLoRA's principal value is enabling fine-tuning of models too large for full-precision fine-tuning on available hardware — a 70B parameter model that requires 8x A100 GPUs for full fine-tuning can be fine-tuned with QLoRA on a single A100 by quantising base model weights to 4-bit; the consultation determines whether this hardware savings justifies any quality difference for your specific task.

The consultation covers your hardware assessment (GPU VRAM), the largest model QLoRA makes feasible on your hardware, quality tradeoff estimate versus standard LoRA, quantisation configuration recommendation, and training cost estimate.