Distilled models explained: smaller, faster, cheaper
The rapid development of large language models (LLMs) has led to systems with hundreds of billions of parameters. Although these massive models score excellently on a wide range of tasks, hosting them brings significant technical and financial challenges. They require multiple high-end graphics cards (GPUs), consume a lot of energy, and introduce delays when generating responses.
To address these challenges, developers and researchers increasingly rely on knowledge distillation . This technique makes it possible to transfer the knowledge, reasoning patterns, and skills of a large model to a significantly smaller model. The result is a network that responds faster, uses less memory, and is more cost-effective in everyday use, while retaining much of the performance of the original model.
What is knowledge distillation? The teacher-student mechanism
Knowledge distillation is a training method in which a compact model (the student or student) is trained under the guidance of a larger, already trained model (the teacher or teacher). Instead of training the small model from scratch solely on raw text files, the small model learns directly from the output and decision structures of the large model.
In traditional training, a model learns via so-called 'hard labels'. The model is given a text and simply has to predict the next word. With knowledge distillation, however, the student gets access to the full probability distribution over the teacher model's entire vocabulary. This detailed probability distribution is referred to as logits or soft labels.
Core concept: Soft labels contain 'dark knowledge'. When a model processes the text "The cat sits on the..." the teacher assigns not only a high probability to the word "mat" but also a small probability to "chair" or "couch", and a nearly negligible probability to "airplane". These relative proportions teach the student exactly how the teacher model nuances concepts and contextual relationships.
By letting the student see how the teacher thinks about alternative options, the student more quickly understands which language patterns are logical. As a result, the training process requires significantly less computing power and data than if a model of equal size were trained from scratch on raw internet text.
Why distillation exists: the practical benefits
Knowledge distillation solves concrete bottlenecks that arise when deploying large language models into production. Companies and developers who integrate LLMs into applications or business processes quickly run into limits around infrastructure and operating costs. Distillation offers a solution in four specific areas:
- Lower hardware requirements: A teacher model with, say, 70 billion parameters requires advanced enterprise GPUs with a large amount of graphics memory (VRAM). A distilled model with 8 billion or 3 billion parameters fits on standard hardware or a single consumer GPU.
- Faster response times: Because the smaller number of parameters requires fewer matrix multiplications per token, the distilled model can process and generate text faster. This reduces latency for the end user.
- Lower processing costs: Operational costs per thousand tokens processed decrease proportionally with the size of the model. This makes large-scale automation or processing of high data volumes financially feasible.
- Local applicability: Small distilled models can run directly on consumer hardware, laptops, or mobile devices. Anyone who wants to dive deeper into running these compact variants locally can read more about small models on a device deploying locally.
The difference between distillation and quantization
In practice, terms like distillation and quantization are sometimes used interchangeably, because both methods aim to shrink and speed up AI models. However, they are two fundamentally different techniques that operate at a different level of the model architecture.
Quantization changes the numerical precision of the weights within an already trained model. Instead of storing each weight as a 16-bit floating-point number (FP16), quantization scales these values down to, for example, 8-bit (INT8) or 4-bit (INT4). The model retains exactly the same number of parameters and layers, but each parameter takes up less memory space. Details about this technique and its effect on memory usage can be found in the article on quantization explained on the guides subdomain.
Distillation , on the other hand, trains an entirely new, smaller network with fewer layers and fewer parameters. The structure of the model changes drastically compared to the teacher.
The two methods are not mutually exclusive; in fact, they reinforce each other. In modern software pipelines, it's common to first distill a large teacher model into a smaller student model, and then quantize that distilled model. This shrinks the model structure while also optimizing the precision of memory usage.
How do you recognize distilled models in practice?
Model catalogs and developer platforms use specific naming conventions to indicate that a model is the result of a distillation process. Recognizing these designations helps in selecting the right components for a software architecture.
Naming conventions and designations
Many model families use additions in the model name. Common terms include:
-Mini,-Tinyor-Nano: Indicates a significantly scaled-down variant within a model family.-Instruct-Distilledor-Distilled: Explicitly indicates that the instruction-following qualities come from a larger teacher model.- Parameter size: Models with designations such as
1B,3Bor7Bwithin a series that originally starts at70Bor405B, are in almost all cases created with the support of knowledge distillation or synthetic data from the larger variants.
What the label does and does not tell you
The label 'distilled' indicates how the model was trained and that it offers a favorable ratio between size and performance. It says nothing, however, about absolute suitability for every conceivable task. A distilled model with 3 billion parameters may perform excellently at summarizing articles but fall short at solving multi-step logical puzzles or abstract mathematics.
| Property | Teacher model (e.g., 70B+) | Distilled Model (e.g., 8B) | Quantized Teacher Model (4-bit) |
|---|---|---|---|
| Number of parameters | Very high | Low to medium | Very high (unchanged) |
| Memory footprint (VRAM) | Large (>140 GB) | Limited (6-16 GB) | Medium (35-50 GB) |
| Inference speed | Lower | High | Medium |
| Training method | Full pre-training | Trained on teacher output | Post-training conversion |
| Suitability | Complex analysis & reasoning | High volumes & targeted tasks | Retains broad reasoning power |
Domain-specific distillation and the role of synthetic data
One of the most effective applications of knowledge distillation is building specialized, domain-specific models. Instead of trying to retain all of the teacher's general world knowledge, the training process focuses on a single specific field, such as legal data analysis, medical reporting, or generating source code.
In domain-specific distillation, a powerful teacher model generates a large number of training examples within that defined field. This is called synthetic data . The teacher is, for example, asked to analyze thousands of complex legal cases and provide step-by-step reasoning. The student is then trained on this high-quality collection of questions, answers, and reasoning paths (chain-of-thought).
The result is a compact model that delivers performance within its specific domain that comes close to the teacher model's level. Outside that field, the student's performance is significantly lower, but for specialized business applications this is often not a problem. The model doesn't waste capacity on general knowledge that isn't relevant to the intended business process automation.
Practical trade-offs: when is a distilled model sufficient?
Selecting the right AI model requires a continuous trade-off between quality, speed, and operational costs. Distilled models are not the best choice in every situation, but for a wide range of applications they are the most efficient option. For a structured overview of how to choose models based on specific requirements, you can consult the guide on choosing the right AI model for a specific task.
Ideal applications for distilled models
- Customer service and chatbots: Handling common questions, where fixed patterns and fast response times are essential.
- Information extraction and classification: Categorizing emails, extracting entities from documents, or converting unstructured text into JSON.
- Text summarization at scale: Processing large volumes of reports or news articles where the core message needs to be extracted quickly.
- Edge devices and mobile applications: Applications that need to work offline or run on devices with strict battery and memory constraints.
When the teacher model remains necessary
When a task requires deep logical reasoning, combining rare knowledge domains, or handling ambiguous instructions, the larger teacher model remains superior. Small distilled models tend to 'jump' more quickly to likely answers, making them less capable of tracing subtle nuances or complex edge cases.
An overview of the strategic trade-offs between quality, licenses, and cost structures can be found in the general selection guide for choosing an AI model.
Limitations, pitfalls, and quality assurance
Despite the clear benefits, using distilled models also carries specific risks. Developers and system architects should take the following limitations into account:
1. Transfer of errors and biases
A student model learns directly from the teacher's behavior patterns. If the teacher model makes certain systematic errors, hallucinates on specific topics, or exhibits subtle biases, these traits are passed on to the student. The student inherits the teacher's blind spots, often without any correction mechanisms in place.
2. Knowledge decay with aggressive downsizing
When the difference in size between teacher and student becomes too large — for example, distilling a model with hundreds of billions of parameters into a variant with fewer than 1 billion parameters — serious quality loss occurs. The smaller model simply lacks sufficient capacity to store all the logical relationships. This leads to a higher rate of hallucinations and more grammatical or content inconsistencies.
3. The need for empirical testing
A common misconception is that a distilled model is automatically 'good enough' because it comes from a renowned teacher model. Because quality loss is task-dependent, every distilled model must be thoroughly tested against a representative test set from your own organization.
Measuring response times, throughput (in tokens per second), and output accuracy is a required step before deployment. For setting up a proper measurement framework, read the guide on measuring speed and latency in language models.
Summary
Knowledge distillation is a proven technique for making the powerful capabilities of large language models accessible in compact, efficient formats. By training a smaller student model on the distributions and output of a teacher model, the result is a system with lower hardware requirements, faster responses, and significantly lower everyday costs.
Although distilled models lag behind their larger predecessors on complex reasoning tasks, they represent an excellent and realistic alternative for the majority of everyday AI applications. By combining distillation with techniques such as quantization and domain-specific training with synthetic data, organizations can roll out AI solutions precisely tailored to their specific performance and budget requirements.
