DeepSeek released six dense distilled models, ranging from 1.5 billion to 70 billion parameters, fine-tuned on reasoning traces generated by its full-size DeepSeek-R1 model, and the 32-billion-parameter version reportedly beat OpenAI’s o1-mini on several benchmarks in DeepSeek’s own evaluations (Kingy.ai). Model distillation is the practice of training a smaller model to imitate a larger frontier model’s outputs, so an enterprise ends up with a compact model that captures much of the frontier model’s capability at a fraction of the size and cost to run.
How Distillation Works
A frontier “teacher” model generates outputs, including its reasoning traces, across a large set of prompts. A smaller “student” model is then trained on those outputs rather than on raw internet text, which transfers learned reasoning patterns rather than just surface-level facts. DeepSeek’s approach fine-tuned Qwen2.5 and Llama base models this way, and the result carried R1-level reasoning down into models as small as 1.5 billion parameters.
Nvidia Is Doing the Same Thing for Its Nemotron Line
Nvidia’s Llama Nemotron reasoning models are built on Llama, post-trained on Nvidia-curated synthetic data generated from DeepSeek-R1 outputs, combining knowledge distillation with neural architecture search (Nvidia Developer Blog). Nemotron 3 Super, a 120 billion parameter mixture-of-experts model with only 12 billion active parameters, runs on a single H100 GPU, a direct illustration of the self-hosting economics distillation makes possible.
Why Enterprises Distill Instead of Just Calling the Frontier API
Self-hosting keeps sensitive data off external APIs entirely, which matters a great deal to teams handling regulated or proprietary business data. Distilled models are also cheap enough to run continuously rather than metering every call against a frontier API bill, which changes the economics of high-volume, always-on workloads. Licensing plays a role here too: DeepSeek’s MIT license explicitly allows commercial use, modification, and further distillation, which is part of why a distillation ecosystem grew up around it so quickly.
Commercient’s agent platform is built for exactly this kind of choice. Its Model Flexibility capability lets agents point at a self-hosted model, including a distilled one, instead of a commercial API, with the same governance, permissions, and knowledge base carrying over regardless of which model sits underneath. A distilled model handling routine data tasks between an ERP and CRM doesn’t require rebuilding anything when the underlying model changes, and sensitive business data stays governed under the same Trust & Control framework either way.
See how Commercient lets your agents run on a self-hosted or distilled model without rebuilding your setup. Book a free demo.
Frequently Asked Questions
What is model distillation in AI? Model distillation is a training technique where a smaller “student” model learns from a larger “teacher” model’s outputs, including its reasoning traces, rather than being trained from scratch on raw data, transferring much of the teacher’s capability into a far smaller model.
Can a distilled model really match a frontier model’s performance? Not fully, but it can get surprisingly close on specific tasks. DeepSeek’s own evaluations reported that its 32-billion-parameter distilled model beat OpenAI’s o1-mini on several benchmarks, though distilled models generally still trail their full-size teacher model on broad, general capability.
Why would a company self-host a distilled model instead of using a frontier API? Self-hosting keeps sensitive data inside the company’s own infrastructure rather than sending it to an external API, and a distilled model small enough to run on modest hardware, like Nemotron 3 Super on a single H100 GPU, is cheap to run continuously rather than metered per call.
What models are commonly used to create distilled versions? DeepSeek distilled its R1 reasoning model into six dense models ranging from 1.5 billion to 70 billion parameters, fine-tuned from Qwen2.5 and Llama bases, and Nvidia’s Llama Nemotron reasoning models were built on Llama using synthetic data generated from DeepSeek-R1 outputs.
How does Commercient support self-hosted or distilled models? Commercient’s Model Flexibility capability lets AI agents point at a self-hosted model, including a distilled one, instead of a commercial API, and preserves the same agent roles, permissions, and knowledge base when the underlying model changes.
