On a specific task, often yes. On everything, no. Model distillation trains a small student model to imitate a large teacher model, so the student inherits much of the teacher's ability at a fraction of the size, cost and response time. The classic example is DistilBERT, which its authors reported was 40 percent smaller and 60 percent faster than BERT while keeping 97 percent of its language-understanding performance. For a narrow business task such as classifying emails or extracting fields from invoices, a distilled model can match the large model it learned from. What it loses is breadth: general knowledge, long reasoning chains and the ability to handle requests nobody planned for.
How does model distillation work?
- Pick a teacher. A large, capable model that already does the task well.
- Generate training material. The teacher answers thousands of example inputs. Its outputs, and in some methods its full probability scores or its step-by-step reasoning, become the lesson.
- Train the student. A much smaller model is trained to reproduce the teacher's answers.
- Evaluate on real examples. The student is tested against real, human-checked data, not just the teacher's opinions.
The idea dates from a 2015 paper by Geoffrey Hinton and colleagues, which showed that a teacher's soft probability scores carry more information than plain right-or-wrong labels. With large language models, the common form today is simpler: the teacher writes high-quality answers and the student is fine-tuned on them. DeepSeek, for example, released distilled versions of its R1 reasoning model built on smaller Qwen and Llama models, from 1.5 billion to 70 billion parameters.
Distillation vs quantization vs fine-tuning: what is the difference?
| Technique | What changes | Result | Effort |
|---|---|---|---|
| Distillation | A new, smaller model is trained from a larger one | Fewer parameters; much faster and cheaper | High: data generation plus training |
| Quantization | The same model stored with fewer bits | Same parameters, about half to a quarter of the memory | Low: often a download |
| Fine-tuning | An existing model is adjusted on your examples | Same size, better at your task | Medium |
| Pruning | Unimportant weights are removed | Somewhat smaller model | Medium to high |
They combine. A typical path is to distil a large model into a small one, fine-tune it on your own examples, then quantize it for deployment. Distillation is also one way small models are made in the first place; see small vs large language models.
How close can the student get?
- Narrow tasks: very close. Classification, extraction, routing, tagging and fixed-format summaries distil well.
- Domain question answering: good, especially when the student answers from retrieved documents rather than from memory.
- Open-ended reasoning and coding: a visible gap remains. A small student can copy the style of reasoning without the depth.
- Unusual inputs: students fail less gracefully. They know what the teacher showed them and little else.
A student also inherits the teacher's mistakes and biases, and can't exceed the teacher on the things it was taught. Keep a human-checked test set that the teacher never produced.
Distillation doesn't shrink a model's intelligence evenly. It keeps the part you trained it on and discards the rest. That is a good trade when you know exactly what the job is.
When is distillation worth it for a business?
- High volume. Thousands of similar requests a day, where a large model's cost or speed is the problem.
- Edge or on-device use. The model must run on a laptop, a kiosk or a Jetson module inside a machine.
- Privacy. A small private model can replace calls to an outside service.
- Latency. The answer is needed in a fraction of a second.
It is not worth it when volume is low, when the task changes weekly, or when a ready-made small model already does the job. Try an off-the-shelf small model first.
Is it legal to distil from any model?
Check the licence and terms. Many commercial AI services prohibit using their outputs to develop competing models, and some restrict automated bulk extraction. Open-weight models have their own licences, which vary. The safest routes are to use an openly licensed teacher that you run yourself, or to obtain written confirmation from the provider. This is general information, not legal advice.
What hardware does distillation need?
| Stage | What it stresses | Typical hardware |
|---|---|---|
| Running the teacher to generate data | Memory for a large model, batch throughput | 128 GB GB10 desktop, 96 GB GPU or DGX Station |
| Training the student | GPU compute and VRAM | One workstation GPU for small students; more for larger ones |
| Deploying the student | Low cost and power | AI PC, mini PC, Jetson or a modest server GPU |
Teacher generation is a batch job, so it benefits from continuous batching. For the training step, see our tests of fine-tuning on GB10 and on DGX Station.
Buying in Singapore
To run a large teacher privately, the NVIDIA DGX Spark 4TB (S$9,450) or HP ZGX Nano G1n 4TB (S$11,499). To train students, the NVIDIA RTX PRO 5000 Blackwell (S$14,971.20) or RTX PRO 6000 Blackwell Workstation Edition (S$29,100). To deploy them at the edge, the Jetson Orin Nano Super 8GB Developer Kit (S$696) (listed 11 Nov 2026; prices change; ask for a current quotation). For larger teachers, DGX Station GB300 systems such as the MSI XpertStation WS300 are price on request. To start, use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.
Frequently asked questions
What is model distillation? Model distillation, or knowledge distillation, trains a small student model to imitate the outputs of a large teacher model, producing a smaller, faster model that keeps much of the teacher's ability on the trained tasks.
Can a distilled model match a large model? On narrow, well-defined tasks it often can. On open-ended reasoning, broad knowledge and unexpected inputs, a gap usually remains.
What is the difference between distillation and quantization? Distillation trains a new model with fewer parameters. Quantization keeps the same model and stores its numbers with fewer bits. The two are often used together.
Is distilling from a commercial AI model allowed? It depends on the provider's terms. Many prohibit using outputs to build competing models, so check the licence or use an openly licensed teacher you run yourself.
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