Knowledge Distillation: Compressing Neural Networks Without Losing Quality

Knowledge distillation is a training method for neural networks where a large, complex model transfers part of its knowledge to a more compact model. The large neural network acts as a teacher, and the smaller one as a student. The goal is to create a model that requires less memory and computational power while maintaining a significant portion of the original system's quality.

This approach is especially important for modern neural networks with billions of parameters. While these can be run on powerful servers and specialized accelerators, deploying such models on smartphones, laptops, or other devices is much more challenging. Knowledge distillation enables transferring some of the large model's capabilities into a lighter architecture.

What Is Knowledge Distillation in Neural Networks?

In standard training, a neural network receives input data and correct answers. For example, the model is shown a picture of a cat and told that the correct class is "cat." The algorithm then adjusts the network's parameters to increase the probability of the correct answer.

With knowledge distillation, an additional source of information appears: a large, already-trained model. It analyzes the same data and shows the smaller model not just the final correct answer, but also its perspective on other possible options.

For instance, the large network might assess an image as 85% cat, 8% tiger, 5% dog, and 2% for other options. In a typical training dataset, the small model would only see the label "cat." With distillation, it gets much more insight into which objects the teacher model considers similar.

Why Standard Training Isn't Enough for Small Models

The size of a neural network limits the number of patterns it can store and use. If you take a small architecture and train it on the same data as a larger model, the result is usually worse.

The large model, through training, develops an internal understanding of relationships between objects, words, and features. Some of these patterns can be transferred to the smaller model through its outputs.

Thus, the purpose of distillation isn't simply to copy results. The student model tries to mimic the behavior of the more powerful system, gaining more information from the same training examples.

Teacher Model and Student Model

The classic scheme uses two neural networks: a teacher model and a student model.

• The teacher is usually much larger. It has already been trained and performs well on the target task. During distillation, its parameters typically remain unchanged; it serves solely as a source of knowledge.
• The student is more compact and has fewer parameters. During training, it compares its answers not only with the correct dataset labels, but also with the teacher's outputs.

This "teacher-student" setup allows the expensive model to be used only during preparation. After training, the student alone remains in the real system, operating faster and requiring fewer resources.

How Does Knowledge Distillation Work?

The main idea is for the student to imitate not just the right answers, but also the teacher's behavior. Both networks receive the same inputs, and the student's results are compared to the teacher's.

During training, the error is calculated from several sources: one measures how the student's answer differs from the correct dataset label, another checks how its probability distribution diverges from the teacher's. The student's parameters are gradually updated to reduce both errors.

How Does the Large Model Transfer Knowledge?

Consider image recognition. In a dataset, a photo of a car has only one correct label: "car." In standard training, the model just needs to maximize the probability of the right class.

The teacher model, however, can provide a much richer answer: perhaps 90% car, 6% truck, 3% bus, and less than 1% for the rest. This distribution is used in distillation. The student sees not just the correct answer, but also which alternatives the teacher considers similar, gaining insights not present in ordinary dataset labels.

The principle is similar for language models. Instead of classes, possible next tokens are considered. The large model can show the student which words or word parts it considers most suitable for a given context.

Why Probabilities Hold More Information Than Just the Final Answer

If a model reports only the final answer, much of the decision-making process is lost. Two examples might share the same label but be perceived quite differently by the network.

For example, for a domestic cat photo, the teacher might nearly rule out all other classes. For a large wild cat, the probability for "tiger" or "leopard" might be much higher. The dataset label remains "cat" in both cases.

These secondary probabilities-sometimes called soft targets-help the student understand not just the boundary between right and wrong, but also the relative similarity between options.

This is why training a small neural network with a large model's guidance is often more effective than simply training the compact architecture on the original data alone.

Temperature in Distillation

To convey more information to the student, the teacher's probability distribution is often softened using a temperature parameter.

Normally, one probability may dominate. For example, the teacher gives 98% to one class, with the rest receiving tiny fractions. In such a distribution, extra information is nearly invisible.

Raising the temperature makes the probabilities less sharp: the main class remains most likely, but the values for others increase. This helps the student see which alternatives the teacher considers more or less likely.

After training, the higher temperature isn't needed anymore-it's just a tool for transferring knowledge during training. The student network then works independently.

Why Distillation Helps Compress Neural Networks

The main practical aim of distillation is to compress neural networks without sacrificing all the quality of a larger model. Instead of running a complex system with billions of parameters for every request, developers can train a compact model to use where speed, computation cost, and limited memory matter.

Distillation is different from simply shrinking a model's architecture. If you just cut layers or parameters, quality can drop sharply. Through distillation, the small model is specifically trained to reproduce the teacher's behavior, so some capabilities are preserved.

Fewer Parameters and Lower Memory Requirements

The number of parameters directly affects the memory needed to store and run a model. The more compact the network, the easier it is to fit into a device's RAM or VRAM.

This is crucial for smartphones, laptops, embedded systems, and other devices where you can't allocate dozens of gigabytes for a single model. After distillation, the network may take up much less space and run on more affordable hardware.

Infrastructure requirements also decrease. A compact model typically needs less computational power, so one server can handle more requests without a proportional boost in accelerators.

Faster Inference

Inference is the stage where a trained neural network receives input and produces an answer. The larger the model, the more operations per request.

Reducing parameters and computations shortens response time. This matters for systems needing near-instant replies-voice assistants, translators, image recognition, chatbots, and local AI apps.

For large online services, the gain is not just speed. If each request needs less computation, server, energy, and accelerator costs drop. With millions of calls, even small savings per run add up.

What Happens to Model Quality?

Distillation can't literally fit all the capabilities of a large model into a much smaller architecture. The student has fewer parameters and less capacity for complex patterns.

The goal is to retain the most useful teacher behaviors and achieve an acceptable balance between quality and model size. The more the model is shrunk, the harder it is to maintain original accuracy.

The result also depends on the student's architecture, data quality, and knowledge transfer method. Well-tuned distillation can yield a compact model that outperforms an equally small model trained only on the original dataset.

Thus, compressing machine learning models via distillation is especially useful where top quality isn't the only priority. Sometimes a slightly less accurate model is more practical if it runs faster, cheaper, and on user devices.

Knowledge Distillation in Large Language Models

Distillation is especially useful for today's large language models (LLMs). A large model may have tens or hundreds of billions of parameters, requiring powerful GPUs, vast memory, and robust server infrastructure.

For many tasks, this computational power is unnecessary. If the application needs only to classify messages, extract data, answer typical questions, or run locally, a smaller model can be much more practical. That's why the large LLM is used as a teacher, and a smaller model is trained on its answers and probability distributions.

How Compact Language Models Are Created

When distilling a language model, the teacher receives a wide range of text prompts and generates responses. These outputs are then used in training the student alongside regular training examples.

It's not just finished texts that can be transferred. If developers have access to the model's internal results, the student can learn from token probabilities, intermediate representations, or other signals from the large network. The more information available from the teacher, the better the student can mimic its behavior.

As a result, the small model gradually learns to generate answers similar to the teacher's, despite its smaller size. Its architecture doesn't need to exactly match the large network.

Where Small Models Are Needed

Compact language models are especially valuable where limited power consumption and fast responses are key. They can run on laptops, smartphones, single-board computers, and other devices without constant cloud server access.

Local deployment also reduces dependence on internet connectivity and allows processing data directly on the device. For businesses, small models are attractive as they serve more requests with fewer infrastructure demands.

For a deeper dive into the differences between compact and large language models, see the article Small Language Models (SLM) vs LLMs: Why Businesses Choose Compact AI.

Why a Small Model Isn't an Exact Copy of the Large One

Even the best knowledge distillation doesn't turn a small model into a perfect copy of the teacher. The student has fewer parameters and computational resources, so it simply can't store all the patterns and capabilities of a much larger network.

Moreover, results depend on the data used during distillation. If the student only saw teacher answers for a limited set of tasks, it will reproduce teacher behavior best in those areas. For new or much more complex queries, differences may become more pronounced.

Therefore, distillation aims to preserve not every capability of the original LLM, but those skills most important for the specific scenario. The result is a smaller, specialized model that's much cheaper to run but still solves the target tasks well.

Distillation, Quantization, and Other Methods for Reducing Neural Networks

Knowledge distillation isn't the only way to make neural networks more compact and efficient. Optimization methods also include quantization, pruning, architecture reduction, and more. They solve similar problems but work differently.

The key difference is that distillation creates and trains a separate student model, which can have fewer layers, parameters, and computational blocks from the start. The large model is only used as a knowledge source during training.

Distillation vs Quantization

With quantization, the model's architecture usually stays the same, but the way its numbers are stored and processed changes. For example, instead of 16- or 32-bit calculations, 8-bit or smaller formats are used.

This reduces memory needs and can speed up inference on suitable hardware. However, the network's structure doesn't become much simpler.

Distillation, on the other hand, involves training a new small model to mimic the large one's behavior. As a result, both the numerical precision and the computational complexity of the model are reduced.

Simply put: quantization compresses an existing network, while distillation trains a new, smaller one on the knowledge of the large model.

The number of parameters plays a crucial role in a model's size and abilities. For more details, see the article Neural Network Parameters: What Billions Really Mean for AI.

Can the Methods Be Combined?

Distillation and quantization aren't mutually exclusive. In practice, you can first train a compact student with the teacher's help, then quantize the student model.

For example, the large model transfers knowledge to a student with fewer parameters. After training, the student's weights are converted to a more compact format, further reducing memory needs and speeding up calculations.

You can also combine distillation with pruning-removing insignificant connections or individual elements from the network. Such a multi-stage approach is especially useful for local AI and systems with tight resource constraints.

However, overly aggressive compression can noticeably impact quality, so developers usually seek a balance between model size, speed, power consumption, and answer accuracy.

Conclusion

Knowledge distillation enables transferring part of a large neural network's capabilities to a more compact model. Instead of training solely on raw data, the student receives extra information from the teacher: answer probabilities, relationships between options, and other signals generated by the large system.

This approach helps reduce model size, lower memory requirements, and speed up inference. Distillation is especially valuable for language models, which-after optimization-can run on less powerful servers, laptops, or even mobile devices.

However, the small model never becomes a full copy of the large one. The more the architecture is reduced, the harder it is to retain all teacher abilities. In practice, distillation helps balance quality, speed, and computational cost. If resources are still limited, you can combine distillation with quantization and other optimization methods.