Data & AI

LoRA Fine-Tuning Pipeline Explained

Data & AI Pipelines diagram · made in Skemio

Architecture diagram of LoRA fine-tuning showing input data passing through frozen base weights and parallel low-rank matrices A and B to an optimizer.
Architecture diagram of LoRA fine-tuning showing input data passing through frozen base weights and parallel low-rank matrices A and B to an optimizer.
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What it shows

  • Freezes base weights W0, adding low-rank matrices A and B (rank r << d).
  • Reduces trainable parameters by 99%+, shrinking GPU VRAM usage by up to 70%.
  • Merges adapters back into base weights at inference with zero added latency.

The takeaway

LoRA cuts training VRAM dramatically by updating low-rank adapter matrices instead of full model weights.

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