What Is Model Distillation?

The generative AI boom has largely been governed by one simple rule: Bigger is better. More data, more computing power and massive models with billions (or even trillions) of parameters are what drive progress. But as these models grow, so do their costs, inefficiencies and energy consumption. Model distillation offers a way to overcome these challenges, compressing the knowledge from a large, pre-trained model into a smaller one without compromising on performance.

Also known as knowledge distillation, model distillation is essentially a form of supervised learning, where a smaller “student” model is trained to mimic the behavior of a larger “teacher” model. The goal is to not only match the teacher’s accuracy, but to emulate its actual thought processes, including its style, cognitive abilities and alignment with human values. The result is a model that has similar capabilities to the larger one, but that is faster and cheaper to run.

What Is Model Distillation?

Model distillation is the process of transferring the capabilities of a large, complex model into a smaller, more efficient one. By condensing — or “distilling” — all of these traits and behaviors, the smaller model can perform just as well as the larger one, but more quickly and with fewer compute resources.

The concept of model distillation was first introduced in a 2006 paper titled “Model Compression,” where researchers used a powerful classification model made up of an ensemble of hundreds of smaller models to label a large dataset. They then trained a single neural network on that labeled data through supervised learning, creating a new model that was “a thousand times smaller and faster,” than the original model “without significant loss in performance.” 

Several papers have been published in the years since, refining the technique. Today, model distillation has found its way into all sorts of artificial intelligence fields, including natural language processing (NLP), computer vision and speech recognition.

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Why Does Model Distillation Matter?

Model distillation has been around for decades, and is now emerging as an effective way to make language model development more accessible, transferring the skills of large, often proprietary models to small, open source ones. DeepSeek-R1, an open source language model made by Chinese AI startup DeepSeek, exemplifies this approach — distilling knowledge from Alibaba’s Qwen2.5 and Meta’s Llama 3.1 to match the reasoning capabilities of some of the world’s top foundation models, but with far less computational power and more affordable hardware. Meanwhile, researchers at UC Berkeley used distillation to replicate OpenAI’s o1 model for less than $450. Teams at Stanford and the University of Washington did the same for less than $50 (and in just 26 minutes).

Model distillation also raises some legal concerns though, particularly around intellectual property and data usage. OpenAI recently accused DeepSeek of “inappropriately” distilling information from its models to build a competitor, which violates its terms of service. Companies like Google and Anthropic have similar rules against using their services to build competing models. 

Nevertheless, as AI development costs continue to rise, model distillation is becoming an industry standard, poised to fundamentally reshape how AI gets built. Tech giants have spent billions building massive data centers and training big, powerful models, ultimately cornering the generative AI market. But if a small startup or team of university researchers can make a comparable model using a fraction of the resources, the entire industry could be upended.

Applications of Model Distillation

Model distillation can be applied to various machine learning and deep learning use cases. Below are some of the most common examples.

Natural Language Processing

Natural language processing enables AI systems to understand and generate language, bridging the gap between human communication and machine comprehension. Model distillation has become quite popular in NLP — especially as large language models grow more unwieldy and inefficient, demanding substantial resources to train and run. Transferring knowledge from these larger models into smaller ones enhances efficiency, reducing both time and computational strain in performing NLP tasks like:

• Text generation
• Machine translation
• Question answering with chatbots
• Conversational AI
• Document retrieval and summarization
• Sentiment analysis

Computer Vision

Computer vision is focused on processing and understanding visual data like images and videos, allowing AI systems to identify and analyze objects, actions and other relevant features in the world around them. Computer vision models are often based on deep neural networks that benefit from distillation, as they make various tasks more efficient and accessible — particularly in more resource-constrained settings. These tasks include:

• Image recognition and classification
• Object detection
• Facial recognition
• Text-to-image synthesis
• Video captioning
• Image captioning
• Lane and pedestrian detection in self-driving cars
• Medical imaging 

Speech Recognition

Speech recognition is what allows AI models to process spoken language, enabling machines both understand and respond to human speech. Distillation helps these systems process audio data faster and with more efficiency, which is especially useful for edge devices with limited computational power, such as smartphones and smart speakers. Model distillation can be used in various speech applications, including:

• Speech translation
• Voice assistants
• Audio classification
• Speech-to-text transcriptions
• Speaker recognition

How Does Model Distillation Work?

At a high level, model distillation works by transferring knowledge from a large model (the “teacher”) to a small model (the “student). Getting from a large, complex model to an optimized smaller model is a fairly complicated process, but it can be broken down into three main components: (1) the teacher model, (2) the student model and (3) knowledge transfer.

1. Teacher Model

The model distillation process begins with a big, pre-trained model to serve as a sort of expert system from which knowledge can be distilled. 

Training a teacher model involves extensive computation, massive amounts of data and sophisticated optimization techniques so that it can capture all of the complex patterns, relationships and contextual nuances within that data. It’s an iterative process, involving continuous adjustment of billions of parameters — the variable weights and biases that determine how input data is transformed as it moves through a neural network — in order to shape how different parts of the network influence one another to produce accurate outputs. Parameters tend to correspond to an AI model’s problem-solving skills, so models with more parameters generally perform better than those with fewer parameters.

Soft targets are also generated during the training of the teacher model. In general, AI models make predictions — a large language model predicts the next word or phrase, for example. Before arriving at these final predictions (called “hard targets”), models first make several preliminary predictions (called “soft targets”), which reflect their confidence in various possible outcomes. These soft targets capture the model’s behavior and decision-making patterns, creating a valuable dataset on which a smaller student model can be trained. And because soft targets provide such rich and structured information, the student model can effectively learn on fewer training examples and at a faster rate than the original teacher model. 

2. Student Model

At this stage, a smaller student model is initialized. Using a simpler architecture and lower computational requirements, the student is trained on its teacher’s soft targets to replicate the larger model’s output probabilities so it can produce accurate results efficiently.

3. Knowledge Transfer

After the student model has been initialized, it is trained even further in a process known as knowledge transfer. This is also known as the distillation phase, where the soft targets generated by the teacher model are combined with the original training dataset to train the student model, with the aim of matching the student model’s predictions to the teacher’s.

At this point, a distillation algorithm is typically applied to ensure the student model acquires the knowledge of the teacher model as efficiently as possible. Deciding which algorithm to use depends on the work at hand, the given model’s features and the data used. They include:

• Adversarial distillation: Uses a generative adversarial network (GAN)-like framework, where the student’s outputs are evaluated by a separate model (called a “discriminator”) to determine how closely its predictions match those of the teacher.
• Multi-teacher distillation: Combines the knowledge of multiple teacher models to enhance the student’s learning and generalization capabilities.
• Cross-modal distillation: Transfers knowledge between models that process different data types (text, images, video, etc.). This can be especially useful for multimodal tasks, such as visual question answering and image captioning.
• Graph-based distillation: Uses graph structures to capture the ways different data points relate to each other, where each vertex of the graph represents a self-supervised teacher that may be based on response-based or feature-based knowledge.

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Model Distillation Methods

There are three ways to approach model distillation, depending on whether or not the teacher model is being modified at the same time as the student model:

1. Offline Distillation

Offline distillation is the most common approach, where a teacher model is pre-trained and its weights are frozen to prevent any changes as it transfers its knowledge to the student model. In this case, the teacher is often a large, proprietary model that remains unmodified while the student undergoes training to replicate its outputs.

2. Online Distillation

In online distillation, the student and teacher models are modified at the same time in a single, end-to-end training process. As the teacher gets updated with new data, the student learns to reflect those changes in real-time. This is made possible through parallel processing, where multiple computations are run simultaneously across different processors, making it a highly efficient method.

3. Self-Distillation

While distillation typically entails the transfer of knowledge from one individual model to another, self-distillation involves using the same network for both the teacher and the student model. In other words: The model learns from itself, transferring knowledge from the network’s deeper layers to the same network’s shallow layers. This approach can help narrow the accuracy gap that usually forms between teacher and student models during offline and online distillation.

Types of Model Distillation

Another way to think about model distillation is through the different types of knowledge within a neural network, each of which is transferred from teacher to student in its own unique way.

Response-Based

Response-based model distillation is the most common type of knowledge transfer, where the student model learns directly from the teacher model’s soft targets in the final output layer. Instead of just training on labeled data, the student learns to mimic the teacher’s responses to certain inputs, capturing the subtle patterns and decision-making processes that occur. This is often accomplished through a distillation loss function, which measures the difference between the teacher and student’s outputs, gradually refining the student’s predictions so that they align with the teacher’s.

Feature-Based

Feature-based model distillation transfers knowledge by focusing on the intermediate “hidden” layers of the teacher’s neural network, rather than just its final output. The student model learns to extract the teacher’s internal features — distinct characteristics, patterns and relationships that get progressively richer as data is transmitted across the network. To ensure knowledge has been transferred effectively, the student’s feature maps are often aligned with those of the teacher using a loss function that incrementally minimizes the differences between them.

Relation-Based

Instead of focusing solely on the outputs of specific model layers, relation-based knowledge distillation looks at the underlying relationships between the inputs and outputs, capturing how different inputs relate to each other within the teacher model’s learned outputs. These relationships can be modeled in various ways, including correlations between feature maps, graphs, similarity matrices, feature embeddings or probabilistic distributions. In the end, the goal is for the student model to emulate the teacher model’s thought process when producing its own outputs.

Benefits of Model Distillation

Model distillation offers several significant advantages, including:

• Reduced size: In distillation, the resulting student model is much smaller than the teacher it learns from, making it easier to deploy in edge environments with limited power and storage.
• Lower costs: Because they require less processing power, distilled models are cheaper to train and run than large models, which often rely on specialized AI chips and cloud services that can cost millions of dollars. Plus, because they demand less energy, these smaller models are able to run on standard hardware, minimizing operational expenses even more.
• Faster performance: Distilled models are designed to process data more quickly, which helps them generate outputs faster than their larger counterparts. This speed can be critical in applications like chatbots and virtual assistants, where users expect fast replies, as well as autonomous vehicles, which rely on real-time decision-making to safely navigate roads and avoid obstacles.  
• Enhanced multilingual capabilities: Through efficient knowledge transfer, distilled models can be trained to perform well across multiple different languages, without the need for the vast, language-specific datasets that are typically required in bigger models.
• Improved generalization: Student models tend to generalize new information better than their teacher models. By learning the thought patterns of a larger, more complex model, the distilled model is able to focus on the most important features, enabling it to retain essential insights from the teacher model while avoiding the complexities that could lead to overfitting. 
• Greater customizability: Distillation allows users to select desirable traits from multiple teacher models and transfer them to student models, meaning distilled models can be trained for very specific applications.
• More explainability: Smaller distilled models have been shown to be fundamentally more explainable than larger models. In a system with hundreds of billions of parameters, it can be challenging to figure out exactly which parts of a neural network contributed to what output. But by transferring knowledge from these complex, black box models to simpler ones, users can get a better idea of what’s going on inside, which can be especially useful in high stakes fields like healthcare and finance.
• Increased accessibility: By bringing the advanced capabilities of larger AI models to smaller ones, model distillation makes it easier and more affordable for a wider range of organizations to build and deploy their own sophisticated artificial intelligence systems — even those with far fewer resources than the tech giants behind the larger models.

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Limitations of Model Distillation

Distilled models share many of the same limitations as larger models: They can generate false, misleading or illogical outputs, and perpetuate the biases in their training data. But they also face some more unique challenges, including:

• Potential performance gaps: While distilled models are designed to replicate the behavior and qualities of larger models, they may still experience a performance gap. The student model usually retains a lot of the essential capabilities, but its reduced size and complexity may affect its ability to achieve the same level of performance — particularly with more advanced tasks.
• Possible knowledge loss: In the distillation process, there’s a risk that the student model may not fully capture the information or nuanced behaviors of the teacher model, which could diminish the accuracy of the smaller model’s outputs.
• Limited by the teacher model: The student model’s capabilities are inherently limited by the teacher model’s strengths and weaknesses. If the larger model has flaws or biases, those will be transferred to the smaller model — and it can be hard to unlearn them. 
• Reliance on large models: Distilled models may not require a lot of power to run on their own, but they need sophisticated, computationally intensive teacher models to learn from initially. So, at the end of the day, the model distillation process still uses a considerable amount of energy and, thus, has a negative environmental impact.
• Technical complexity: While the distillation process is generally simpler than training a large model from scratch, it still demands quite a lot of work. The teacher model needs to be well-trained, and transferring knowledge that adequately captures the full capabilities of the teacher model is a delicate balancing act. Plus, fine-tuning the student model requires a careful selection of hyperparameters and optimization techniques. Any missteps can compromise the quality of the distilled model.

Frequently Asked Questions