Blog
The race to building competitive advantage just took an interesting turn. Here is what you need to know.
What is model distillation?
In chemistry, distillation is a purification process where a liquid mixture is heated until components with different boiling points separate. The result is often a more concentrated, refined substance. This elegant concentration technique has been used for centuries to create everything from perfumes to whiskey, preserving essential qualities while removing unnecessary bulk.
Model distillation in AI follows a remarkably similar principle; knowledge is extracted from a large and complex AI model (the “teacher”) and transferred to a smaller, more efficient model (the “student”). At its core, model distillation is about teaching the student to act like the teacher. Instead of training a small model with traditionally labeled data, response-based knowledge distillation allows the student model to mimic the intelligence of the teacher (i.e., output token probability distributions, for the data scientists among us).
The result is a refined, lightweight model that captures a concentrated essence of its larger counterpart's capabilities within its domain of expertise.
Why model distillation is everywhere
While DeepSeek did not invent model distillation, their breakthrough in early 2025, which we discussed before, catapulted cost efficiency for GenAI models into the frontstage, proving that smaller teams with limited resources could compete at the cutting edge of AI development.
The latest generation Large Language Models (LLMs) like GPT-4.5, DeepSeek V3, and Claude 3.7 are incredibly powerful—but also incredibly expensive to run. Model distillation offers the process of training a smaller model to mimic a larger one, delivering near state-of-the-art performance in a narrow domain at a fraction of the cost.
DeepSeek was a game changer in the pricing of large scale models. Model distillation extends beyond that: it enables AI capabilities where even the cheapest full-scale LLMs are too expensive or impractical to run.
For example, researchers from the University of Washington created their own reasoning model in merely 26 minutes for less than $50. InHand Networks successfully distilled models on their edge AI computers to provide low-power real-time inference capabilities. This revolution has forced even industry giants like OpenAI to reconsider their closed-source approach.
Why it is a game-changer for businesses
The value of model distillation extends far beyond reducing expenses—it unlocks new possibilities for AI implementation that were not feasible previously. Here's how it's changing the game:
✅Better unit economics. Running small, distilled models requires significantly less computational resources, translating directly to lower operational costs for high-volume AI applications.
✅ Enhanced inference speed. Smaller models process data more quickly, leading to faster response times and improved user experience in time-sensitive applications.
✅ Near state-of-the-art performance. For well-defined use cases, a distilled model can perform remarkably close to full-size LLMs while being more efficient and cost-effective.
✅ Smaller bottleneck on data. The requirement on your data shifts from curating and labeling large datasets, to a smaller curated set of examples with teacher-generated answers, significantly reducing annotation costs and time.
Not a silver bullet: the downsides you should consider
Model distillation is not without hurdles. Here’s what you need to plan for:
❌ Upfront development cost. Building and training your own distilled model takes time and effort from experts in your team.
❌ Teacher performance limitation. The small model can only be as good as the large model it learns from. Furthermore, some large proprietary model providers like OpenAI and Anthropic put restrictions on the output of their models, hindering their use for model distillation.
❌ No automatic updates. When improved foundation models are released, you need to repeat the process of distillation to get access to these improvements in distilled format.
Model distillation in action: where it works best
Distillation works great when you need cost-effective, scalable AI in a controlled environment. Good use cases include:
🔹 Edge devices & on-device solutions. Frontier models do not fit on consumer hardware or smaller edge devices. (Edge devices—e.g., phones, sensors, or cameras—process data locally and on the spot instead of sending it to a distant data center.) If low-latency or portability is a requirement, you may want to embed the required intelligence in a smaller package.
🔹 Personalized content generation at scale. Think online learning platforms that generate exercises tailored to their students’ needs, or marketing tools that generate personalized e-mail content.
🔹 Domain-specific chatbots. Customer support bots trained for highly specific industries without relying on expensive full-scale models for all interactions.
🔹Specialized parts of agentic workflows. Agents use notoriously many tokens. Hence, splitting your solution up in specialized modules with distilled models can reduce costs and latency significantly, especially at scale.
However, don’t use model distillation for deep research or complex, open-ended reasoning tasks. If your AI needs to push the boundaries of intelligence or perform highly creative problem-solving, a distilled model won’t cut it; inherently, it will remain inferior to its teacher.
The bottom line: does distillation fit your business needs?
Model distillation is transforming from an academic idea into a competitive advantage for businesses. It democratizes advanced AI capabilities while dramatically reducing costs. For businesses facing the dual pressures of innovation and efficiency, distillation offers a middle ground: near state-of-the-art performance without the steep price tag.
Just be mindful that it’s not a silver bullet. Many use cases will still require full-scale LLMs, which offer the benefit of broader and more general knowledge and capabilities.
If your AI roadmap has well-defined use cases in a specific domain that can afford a short-term investment for long-term cost benefits, it may well be worth it to explore this approach.
This article was written by Jacco Broere, Data & AI Engineer at Rewire and Simon Koolstra, Principal at Rewire.
