What is Parameter-Efficient Fine-Tuning (PEFT) for Large Language Models?
The rapid growth of artificial intelligence has come with skyrocketing costs. Training large language models (LLMs) has become so expensive that only corporations with billion-dollar budgets can afford it. For example, according to research data, the development of ChatGPT-4 cost between $41 million and $78 million, while Google’s Gemini 1 reached nearly $200 million. And that doesn’t even include staff salaries, which can add up to 49% of the final cost.
For most businesses, such expenses are out of reach. Even if a company only needs to adapt an existing model for specific use cases—like handling customer queries, personalizing services, or analyzing large datasets—traditional fine-tuning quickly becomes too costly.
This is why Parameter-Efficient Fine-Tuning (PEFT) is attracting more and more attention. It enables companies to fine-tune models at a fraction of the cost and time, while still maintaining high performance. For business owners, PEFT represents a way to leverage AI as a competitive advantage without billion-dollar investments.
What is Parameter-Efficient Fine-Tuning (PEFT) in Simple Terms
Parameter-Efficient Fine-Tuning, or PEFT, is a modern machine learning approach to adapting large AI models without retraining them from scratch. Instead of updating all the billions of parameters inside a pre-trained model, PEFT focuses only on a small portion of them or introduces lightweight additional layers. As a result, fine-tuning a neural net will be cheaper, faster, and a much more practical solution for organizations.
Fine-Tuning and Parameter-Efficient Fine-Tuning
Classic fine-tuning involves retraining the entire large pre-trained model on new data. This gives good results, but requires enormous computing power, time, and budget.
PEFT, on the other hand, only “adjusts” individual parameters or uses special techniques such as adapters, prompt tuning, or LoRA. The result is almost the same quality, but at a much lower cost.
Why Parameter-Efficient Fine-Tuning is Important for Businesses
The value of PEFT for businesses is obvious. It allows them to reduce costs and avoid spending millions on model training. Setting up and implementing solutions takes much less time, so new products and features can be brought to market faster.
In addition, PEFT offers flexibility — the model can be adapted to a specific industry, language, or customer needs. Simply put, it is a way to reap all the benefits of cutting-edge artificial intelligence while using resources as efficiently as possible. And in a highly competitive environment, it is precisely this efficiency that often becomes the decisive factor for success.
PEFT Methods and How to Choose the Right One
Parameter-Efficient Fine-Tuning isn’t a single technique but rather a whole family of approaches. Each method has its own strengths: some are better suited for quick experiments, while others are designed for large-scale projects with massive datasets. To make it easier for businesses to navigate, let’s look at the three most popular options — Adapter, Prompt Tuning, and LoRA — and see in which cases each of them can be most useful.
Adapter
Adapters can be seen as an “add-on” placed on top of an existing model. They allow the model to quickly learn new skills without changing its entire structure. Several adapters can be quickly swapped live. For businesses, this means you can add the functionality you need to an already working system — almost like plugging in a new module to your CRM or online store. Fast and cost-effective.
Prompt tuning
Prompt tuning is even simpler. It’s like explaining to an employee how to answer emails properly instead of sending them back to university. The model adapts to your wording and business tasks with minimal resource use. This approach is ideal for chatbots or customer support systems.
LoRA
Today, LoRA is one of the most practical tools for working with large language models. Instead of “teaching” the entire system from scratch, this method allows you to add new knowledge or skills in a targeted manner. This technique also supports hot-swapping.
How to Know Which Method Fits Your Project
Choosing the right PEFT method is less about the technology itself and more about your business priorities. The “best” option will always depend on what you’re trying to achieve, how fast you need results, and what resources you can allocate.
If your goal is to quickly test a new idea or concept, then lightweight methods such as Adapters or Prompt Tuning are often the smartest choice. They don’t require huge investments and can show whether the approach is worth scaling further. For example, a retailer could use Prompt Tuning to rapidly adapt an AI chatbot for handling seasonal customer requests without re-training a full-scale model.
When you’re dealing with large-scale projects, complex datasets or you need a specific output format, LoRA becomes the more practical solution. It gives you the flexibility to fine-tune massive language models for highly specialized tasks — like processing financial reports or analyzing healthcare records — while keeping costs under control.
Model Using PEFT in Business
PEFT is valuable not only because it reduces costs, but also because it allows AI to be fine-tuned to the specific needs of a business. The model can adapt to industry terminology, customer communication styles, and domain-specific requirements. This means PEFT-based solutions integrate more smoothly into workflows and deliver results that are directly relevant to real business challenges.
The Role of Training Data
At the same time, data will still remain critically important. Even though the tuning process becomes simpler and more affordable, the quality of the outcome depends heavily on the examples used for training. The better the quality and cleanliness of the data, the better the model understands customer requests and provides relevant answers.
For companies, this means that it’s not just PEFT that they have to implement — they need to ensure their training data is well, what is the word “prepared” in a way that means not prepared.
A Short Example of Fine-Tuning in Practice
Imagine an e-commerce company that wants to deploy an AI assistant to handle customer orders and inquiries. With traditional fine-tuning, the entire model would need to be retrained, taking months and millions of dollars.
With PEFT, the process looks very different: Typically, configuring large models takes months and requires millions in investment. However, with PEFT, the process becomes a week-long project: a ready-made model is taken, data from your field is added, and the system adapts to your business tasks. As a result, the company receives a ready-made assistant that responds to customers naturally and without delay.
The result: within just a few weeks, the company gets a chatbot that understands customers and responds in their language — at a fraction of the cost of traditional fine-tuning.
For clarity, we’ve put the key benefits of PEFT into a simple table. It shows what concrete advantages businesses can gain by adopting this approach.
| Benefit | What it means for business |
| Cost and resource savings | No need for full model retraining — reduces expenses on infrastructure, specialists, and development time. |
| Faster adaptation | AI systems can be quickly adjusted to new markets, languages, and products without lengthy implementation cycles. |
| Scalable solutions | Easily expand the capabilities of existing models without full retraining, accelerating business growth. |
| Hot switching between models | Quickly switch between your several tuned models in an optimal way. |
Benefits of PEFT for Businesses
How SCAND Helps Businesses Implement PEFT
SCAND offers a full range of model fine-tuning services — from traditional fine-tuning to modern parameter-efficient fine-tuning (PEFT) methods. We help companies harness the power of AI without unnecessary expenses, making advanced technologies both accessible and practical.
Expertise in PEFT Methods
Our team has hands-on experience with various approaches, including LoRA, Adapter, Prompt Tuning, and more. We select the right method based on specific business goals — whether it’s a quick chatbot launch, adapting a model to a new language, or building large-scale solutions for Big Data.
SCAND Implements PEFT Solutions Across Industries
- Banking and Fintech — personalization of services and automated customer support.
- E-commerce — chatbots for order processing and intelligent recommendation systems.
- SaaS platforms — model adaptation for niche markets and specific user needs.
If you’re ready to implement parameter-efficient fine-tuning and unlock the full potential of AI, get in touch with SCAND — we’ll help turn advanced technology into your competitive advantage.
