What is Distillation? Definition & Guide

What is Distillation?

odel distillation is the technique of training a smaller student model to mimic the behaviour of a larger teacher model. The result is a compact model that runs faster and cheaper while retaining most of the teacher's capabilities. Distillation is critical for enterprise AI deployment because it enables running powerful AI capabilities on constrained hardware, including on-premise environments with limited GPU capacity.

The distillation process works by using the teacher model's output distributions — not just the final predicted labels — as training targets for the student model. This 'soft label' training gives the student much richer signal about the teacher's reasoning than hard labels alone. For example, when classifying a piece of text, the teacher's probability distribution across all possible classes reveals which categories it found plausible even when it didn't choose them — information that helps the student learn more nuanced distinctions. The student model ends up approximating not just the teacher's answers, but its reasoning patterns.

For enterprise on-premise deployment, distillation solves a real infrastructure problem. A frontier-scale model (70B+ parameters) requires 8+ A100 GPUs to run efficiently. A well-distilled 7B or 13B model derived from that frontier model can run on 1-2 GPUs while retaining 85-95% of the parent model's capability on the specific tasks it was distilled for. This makes the difference between a deployment that requires a $500K GPU cluster and one that runs on standard enterprise servers that many regulated organisations already own.

Also known as: Model Distillation, Knowledge Distillation

Key Points

Core idea
Distillation trains on the teacher model's output probability distributions, not just its final answers — giving the student model richer information about the teacher's reasoning.
Why it matters
A well-distilled model retains 85-95% of the teacher's capability on targeted tasks at 10-30% of the parameter count, running on a fraction of the GPU hardware.
Enterprise use
Distilling for specific enterprise tasks (claims classification, entity extraction, intent detection) produces better task performance than general-purpose distillation, even with smaller student models.

How It Works

How Distillation works

Define the purpose, inputs, and success criteria that Distillation must support.
Apply Distillation in the relevant workflow while recording its inputs, configuration, and outputs.
Evaluate the result against representative data, operational constraints, and human review before expanding production use.

How Fluid AI Uses This

Distilled models for on-premise deployment.

Fluid AI uses model distillation to deliver production-grade agentic AI performance on customer-owned infrastructure without requiring cloud-scale compute.

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Topics Covered

model distillation enterprise AI
knowledge distillation on-premise
small language model distillation
LLM distillation edge deployment
distilled AI model enterprise
model compression AI
distillation vs fine-tuning
on-premise AI model size

Distillation