Introduction

Model distillation, also known as knowledge distillation, is a machine learning technique that transfers knowledge from a large, complex model (the "teacher") to a smaller, more efficient model (the "student"). It has become a cornerstone for optimizing AI models, particularly when computational resources, speed, or costs are limiting factors. Large models, such as large language models (LLMs) or Vision-Language Models (VLMs), excel at complex tasks but are often too slow or expensive for practical deployment. Distillation addresses this by creating compact models that retain much of the teacher’s performance, making them suitable for real-world applications like mobile apps, edge devices, or cost-sensitive environments.

This guide explores when model distillation is necessary, how to build an effective dataset for it, and real-world examples demonstrating its business impact.

When is Model Distillation Necessary?

Model distillation is particularly valuable in scenarios where large models are impractical due to resource constraints or performance requirements. Below are key situations where distillation is likely necessary, supported by insights from industry sources:

High Computational Costs

Large models (some frontier models are now in the trillions of parameters), require significant computational resources, leading to high operational costs, especially in cloud environments. For example, a model with 70 billion parameters may need 168GB of GPU memory, making it expensive to run at scale. Distillation creates smaller models that reduce these costs while maintaining high accuracy, making AI more affordable for businesses. Typically distilled models retain 95%+ of the accuracy of a large model, depending on the task!

Real-Time Applications

Applications requiring low latency, such as chatbots, recommendation systems, or realtime classifiers benefit from smaller models that process requests faster. If you are generating JSON from a model to then render the response to a user, it's likely that latency will become an issue, as we don't want the user to get frustrated before they receive a response. For instance, a large VLM like Claude Opus can take 25 seconds to classify an image and extract metadata, which is impractical for a consumer app. Distilled models improve response times, enhancing user experience while significantly reducing costs and enhancing reliability. Still, without training these super large models may be the only ones capable of solving complex tasks, and should be the starting point. You can collect valuable data for training while serving early users, with the plan to improve latency later.

Resource-Constrained Environments

Deploying AI on devices with limited computational power, such as smartphones or IoT devices, requires compact models. Distillation enables advanced capabilities on edge devices, where large models are infeasible due to memory or power constraints. The smaller the model the easier it is to run on lower-end hardware. If you can run something on a 4090 instead of an B200, you may be able to significantly improve your margins.

Complex Multimodal Tasks

Tasks requiring both visual and textual understanding, such as visual question answering, medical report generation, or visual storytelling, often rely on VLMs. These tasks are complex, and traditional classification models may fail due to their inability to process multimodal inputs effectively. Distillation allows businesses to deploy efficient VLMs for such tasks, balancing performance and practicality. When considering distillation, we want the task to be narrow enough that we don't need the full intelligence of the model to perform it. General purpose tasks requiring a breadth of knowledge may not be optimal for distillation, but a straightforward task like image/video captioning may be extremely easy to distill.

When Traditional Models Fail

For tasks where simpler models cannot achieve the required accuracy, but large models can, distillation bridges the gap. By training a smaller model to mimic the teacher’s behavior, businesses can achieve high performance without the overhead of the large model.

Building a Great Dataset for Model Distillation

Creating a high-quality dataset is critical for successful model distillation, especially for VLMs, which handle both visual and textual inputs. The dataset typically consists of inputs (e.g., images, text prompts) and corresponding outputs generated by the teacher model. Below are detailed steps to build an effective dataset, tailored for VLM tasks like visual question answering or medical report generation:

1. Define the Task Clearly With a Prompt

Specify the task the student model will perform with a sophisticated prompt. To help with your prompt, you may want to experiment with OpenAI's playground, which has recently become more sophisticated with lots of prompt improvement tools and full versioning. The best practice for complex tasks is to write a megaprompt, which is a large prompt which includes all necessary information and instructions to complete a task. These prompts sometimes grow to thousands or tens of thousands of tokens.

2. Collect Diverse Inputs

Gather a representative set of inputs that cover the range of scenarios the model will encounter.

A really good way to get relevant, diverse data is just to get data from users while operating a larger model, and then store these inputs and outputs with an LLM observability provider like Helicone. For multimodal tasks, gathering diverse outputs might just mean scraping images. For instance, if your task is plant classification, you may want to scrape 100k plant images from Pinterest.

3. Generate Teacher Model Outputs

Use the teacher model to produce outputs for the collected inputs. We'll then be able to train on input-output pairs.

For an X-ray image, the teacher VLM might generate a detailed report describing findings.

We want to make sure that the teacher model is able to solve the task effectively first. If it isn't, it might make sense to manually create a dataset, and fine-tuning a large teacher (like a 70B parameter model), before distilling that down into a smaller model.

4. Ensure Data Quality

Verify the accuracy of the teacher’s outputs, as errors will propagate to the student model. If possible, use expert annotations for a subset of the data to validate the teacher’s performance. For example, in medical applications, have radiologists review a sample of generated reports.

5. Balance and Augment the Dataset

Ensure the dataset is balanced across categories (e.g., different medical conditions) to avoid biases. Use data augmentation techniques to increase diversity:

• Image Augmentation: Apply transformations like cropping, rotation, or color adjustments.
• Text Augmentation: Vary prompt phrasing to cover different ways of asking the same question. This improves the student model’s robustness DataCamp.

Augmentation isn't always necessary for distillation, but it is something to consider.

6. Include Challenging Examples

Incorporate edge cases or difficult inputs to make the student model more resilient. For example, include low-quality X-ray images or ambiguous questions to test the model’s generalization. Challenging examples also make it easier to test models, as you don't need to test the teacher or student on a dataset of 10k examples--50 hard examples may be sufficient. It may make sense to write these examples by hand, as they can make or break your model.

7. Create a Validation Set

Set aside a portion of the dataset for validation to monitor the student model’s performance during training. This helps prevent overfitting and ensures the model generalizes well. Make sure your hardest examples are both in the training and validation set.

8. Dataset Size Considerations

While smaller models may require less data than large models, the dataset must be large enough to capture input variability. For VLMs, aim for thousands of input-output pairs. Depending on task complexity, this can rise to millions.

Final Thoughts

Model distillation is not a silver bullet, but rather a tool to improve latency and costs when large models can already do a task well, but do so slowly or only with long Chain-of-Thought chains. Training a model can be expensive, with training runs ranging from a few hundred to tens of thousands of dollars.

At Inference.net, we have an in-house team of model trainers, and years of expertise with MLOps. This allows us to not only quickly train SOTA small language models, but deploy them efficiently and robustly for enterprises.