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Interactive 3D/Fine-Tuning Methods Compared
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★ Pareto efficient
LoRA r=16 is the default choice for most tasks - near Full FT performance at 3.5x less memory. Use QLoRA to fit 70B+ on consumer hardware.

Fine-Tuning Methods Compared - Interactive Visualization

Full fine-tuning achieves best performance but requires updating all parameters and storing full gradients - infeasible for 70B+ models on consumer hardware. LoRA reduces trainable parameters by 100-10000x with minimal performance loss. QLoRA enables fine-tuning 70B models on a single 48GB GPU. This demo compares all methods on memory, speed, and performance across model sizes and tasks.

  • Full fine-tuning: all weights updated, full optimizer states stored, highest GPU memory cost
  • LoRA: low-rank adapters trained on frozen base model, 100-10000x fewer trainable parameters
  • QLoRA: 4-bit quantized base model with LoRA adapters, fits 70B on a single 48GB GPU
  • Memory and speed comparison: radar chart across VRAM, training time, and downstream accuracy

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.