PEFT (Parameter-Efficient Fine-Tuning)
A family of methods, including LoRA and QLoRA, that adapt a large model by training a tiny fraction of its parameters, so tuning fits on a single GPU instead of a cluster.
What is PEFT (Parameter-Efficient Fine-Tuning)?
A family of methods, including LoRA and QLoRA, that adapt a large model by training a tiny fraction of its parameters, so tuning fits on a single GPU instead of a cluster.
PEFT (Parameter-Efficient Fine-Tuning) is a advanced concept that sits in the LLM and GenAI Ops area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space, not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition, with diagrams, code, and a quiz to lock it in, work through the "Parameter-Efficient Fine-Tuning" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
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Related glossary terms you might want to look up next.
LoRA (Low-Rank Adaptation)
A parameter-efficient fine-tuning method that freezes the base weights and trains a small pair of low-rank matrices instead, cutting memory and cost while keeping most of the quality.
Fine-Tuning
Continuing to train a pretrained model on your own data to change its behavior, style, or format. Best for teaching a skill, not for injecting facts, which is what retrieval is for.
Quantization
Storing model weights in fewer bits, such as FP8 or INT8, to shrink memory and speed up inference. Modern methods lose little quality on most tasks.
RAG (Retrieval-Augmented Generation)
A pattern that retrieves relevant text from an external index and puts it in the model's prompt so the answer is grounded in current, verifiable facts rather than the model's memory.
Embedding
A list of numbers that represents the meaning of a piece of text, so that similar meanings sit close together in vector space. The same model must embed both the corpus and the query.
ANN (Approximate Nearest Neighbor)
Search that finds vectors close to a query without scanning every item, trading a small chance of missing a true neighbor for a large gain in speed. HNSW and IVF are the common indexes.