Definition

Model compression is the process of shrinking large AI models to make them more efficient. This can involve pruning, quantization, or distillation—techniques that reduce size, speed up inference, and lower compute costs while preserving most of the model’s accuracy.

Example

“Instead of using a full-size GPT model, you can run a compressed version like TinyLLM for faster, cheaper results.”

How It’s Used in AI

Compressed models are ideal for mobile apps, edge devices, and cost-sensitive deployments. They're used in voice assistants, real-time translation, and any setting where response time and efficiency matter.

Brief History

As models like GPT-3 grew to billions of parameters, researchers developed compression methods to make them usable on regular hardware. Techniques like knowledge distillation and quantization became standard in the deployment pipeline.

Key Tools or Models

DistilBERT, TinyLlama, and MobileBERT

Compression tools like ONNX, DeepSpeed, and TensorRT

Quantization libraries in Hugging Face Transformers

Pro Tip

Don’t overcompress. Go too far, and your model loses performance. Always test outputs to make sure the results still match your needs.