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LLM & Generative AI Systems • Fine-tuning at Scale (LoRA, QLoRA, PEFT)Easy⏱️ ~3 min

What is Parameter Efficient Fine Tuning (PEFT)?

Definition
Parameter Efficient Fine Tuning (PEFT) adapts large foundation models to specific tasks by training only a tiny subset of new parameters (typically under 1% of model size) while keeping the base model frozen, dramatically reducing memory, compute, and storage costs.
The Core Problem: Modern Large Language Models (LLMs) are massive. A 7 billion parameter model in 16 bit precision requires about 14 GB just to store weights. A 65 billion parameter model needs roughly 130 GB. When you fully fine tune such models, you must store gradients, optimizer states like momentum and variance, and intermediate activations. This easily pushes memory requirements to 4 to 6 times the raw weight size, exceeding what even an 80 GB GPU can handle.

Full fine tuning also creates an operational nightmare at scale. Imagine you're building an LLM platform serving 100 different products: code generation, ad copywriting, customer support across verticals, internal knowledge assistants. If each specialization requires its own fully trained 70B model, you'd need to store and deploy 140 GB of weights per variant in 16 bit format. For 100 variants, that's 14 terabytes of model storage.

How PEFT Solves This: Instead of updating all billions of parameters, PEFT methods freeze the base model entirely and introduce a small set of trainable parameters per task. These additional parameters are typically well under 1% of the original model size. For instance, adapting a 3 billion parameter model might introduce only 13 million trainable parameters.

Memory Efficiency Gains
100%
FULL FINE TUNE
<1%
PEFT ADAPTER

Because you only update this tiny parameter set, optimizer states shrink from gigabytes to mere megabytes. Training time drops significantly. Storage per task becomes manageable: a single adapter might be 50 to 200 megabytes instead of 140 gigabytes. You can now maintain one shared base model and thousands of lightweight task specific adapters.

Real World Impact: This architectural shift enables multi tenancy at scale. A platform can load one 70B base model per GPU and dynamically swap in different adapters based on the incoming request's tenant or task identifier. This is how systems support user created custom GPTs or specialized skills without duplicating the entire foundation model.

💡 Key Takeaways

✓PEFT trains only a tiny fraction of parameters (under 1% typically) while freezing the base model, reducing memory by 100x or more compared to full fine tuning

✓A 70B parameter full model might need 400 to 600 GB for training (weights plus optimizer states), but PEFT adapters need only 50 to 200 MB per task

✓Enables multi tenancy: one shared base model serves thousands of specialized tasks by loading small adapters dynamically based on request context

✓Training becomes accessible: teams can adapt large models on single GPUs instead of requiring expensive multi GPU clusters for every specialization

📌 Interview Tips

1A 3B parameter base model with PEFT adapters introduces only 13M trainable parameters (0.43% of model size) when targeting attention layers with rank 8

2Serving 100 product variants: Full fine tuning needs 14 TB of storage (140 GB × 100), PEFT needs 140 GB base plus 10 GB adapters (100 × 100 MB)

3Production platforms like those at Meta or Google use PEFT to serve hundreds of internal teams from a single shared foundation model with per tenant adapters

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