Model distillation is a way to train one AI model to imitate another. Ordinary AI use, by contrast, usually means sending a prompt to a model that has already been trained and receiving its answer. Distillation creates or adapts a student model; using that student to answer prompts is a separate inference step.
What model distillation means
In knowledge distillation, a teacher provides a learning signal and a student is trained to reproduce useful parts of the teacher’s behavior. The student may be smaller, faster, or less costly to serve, but those are aims rather than guaranteed results. The UK government’s AI Insights guidance describes distillation as a model-compression technique; the method is broader than any one product or training recipe.
The signal does not have to be just the teacher’s final answer. Depending on the method and access to the teacher, training can use output probabilities (often represented as logits), intermediate representations such as hidden activations, or responses generated for selected prompts.
How distillation differs from ordinary AI use
| Ordinary AI use (inference) | Model distillation |
|---|---|
| A trained model receives an input and returns an output. | A teacher’s behavior or responses provide a training signal for a student. |
| The user consumes the answer; the request does not by itself replace the model’s parameters with a newly trained student. | Training produces or updates a student model, which can then be used for inference. |
| Typically a per-request activity. | Includes a training stage, such as generating data and optimizing the student, plus evaluation and compute needs. |
An analogy: ordinary use is asking a knowledgeable system a question; distillation is using examples of its behavior to train another system for a defined job. The analogy is incomplete because some methods transfer probability distributions or internal representations, not merely visible answers.
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What a distillation workflow involves
- Choose the teacher and student. Decide which teacher can supply useful behavior and what student model or training setup is suitable for the intended task.
- Select relevant prompts or examples. The data should reflect the student’s intended work; a student trained on narrow examples may not transfer well to different inputs.
- Collect the teacher signal. Depending on access and method, this may be probabilities, intermediate features, or teacher-generated responses.
- Train the student. The student is optimized to match the chosen signal. Some approaches also use student-generated sequences and teacher feedback on them.
- Evaluate for the real task and deployment. Test on held-out, task-relevant data and conditions that resemble actual use. Do not infer equivalent quality from parameter count or a few sample answers.
Amazon Bedrock provides one managed example: its model-distillation documentation describes selecting teacher and student models, supplying prompts or invocation logs, generating teacher responses, and fine-tuning the student. A cloud service can implement distillation, but it is not required for the technique.
Different ways knowledge can be transferred
Response-based distillation
The student learns from the teacher’s output distributions or “soft” targets, rather than only from hard labels such as a single correct class. Those distributions can convey uncertainty and relationships among alternative outputs, as the UK government guidance explains.
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Feature-based distillation
The student is trained to match intermediate teacher representations or activations as well as, or instead of, the final output. This requires a training setup with access to those internal signals.
Generated-response training
A teacher can generate prompt-and-response examples that are then used to fine-tune a student. This is a practical and studied approach, but it is not identical to every probability-based distillation method. A 2024 study of Llama 3.1 models found that synthetic-data quality and task-specific evaluation matter; its results apply to the models, tasks, and datasets it tested, not to every teacher–student pairing (study).
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Self-distillation and on-policy approaches
Distillation does not always require a separately selected external teacher. In self-distillation, later checkpoints or deeper parts of a model can supervise earlier checkpoints or shallower parts. In on-policy distillation, the student’s own generated sequences are used during training and the teacher provides feedback. This addresses a potential mismatch between fixed training examples and the outputs the student generates after deployment. Google DeepMind’s ICLR 2024 work studies this approach for language models.
Why use distillation—and what it does not guarantee
The motivation is often to reduce serving cost, memory use, latency, or reliance on a large hosted model. A smaller student may also be easier to run on constrained hardware. Whether it achieves those advantages while preserving enough task performance depends on the training method, data, student capacity, hardware, and workload.
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The UK government’s AI Insights guidance, updated August 3, 2026, gives illustrative expectations: a student may retain 80% to 95% of a teacher’s task-specific quality, and distillation may use 80% to 95% fewer compute resources. These are figures reported by that guidance, not guaranteed outcomes for a particular project. It also illustrates an 8-billion-parameter student responding in under 100 milliseconds on a single accelerator, compared with a 70-billion-parameter teacher taking several seconds and potentially requiring multiple GPUs. That is an example in the guidance, not a universal latency benchmark.
Research findings reinforce the need to measure results rather than assume equivalence. Stanton and co-authors’ NeurIPS 2021 analysis reports that the distillation dataset and temperature scaling affect how closely student and teacher predictive distributions match, and that substantial discrepancies can remain even when the student has capacity to match the teacher (paper). A DistiLLM paper reported up to 4.3× speedup over recent knowledge-distillation methods in its evaluated setup; that is a result about the paper’s method and experiments, not a general speedup for distilled models (ICML 2024 paper).
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How to judge whether a distilled model is suitable
Compare candidates under the conditions in which the student will actually be used. Useful checks include:
- Task quality: Does it perform well on held-out examples that represent the intended job, including difficult cases?
- Input match: Does evaluation reflect the prompts and inputs the student will encounter, including its own generated outputs where relevant?
- Serving requirements: What memory, latency, hardware, and operating cost does the student require in the target deployment?
- Training trade-off: What teacher signals are available, and do data generation and training costs make sense for the expected serving savings?
- Failure behavior: Where does the student diverge from the teacher, and are those errors acceptable for the use case?
A student that imitates a teacher on a narrow dataset may be useful for a narrow task without matching the teacher’s general capabilities. Test the student itself; similarity to the teacher is not proof of suitability.
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