Quantization
A technique that reduces AI model size and speeds up inference by representing model weights with fewer bits (e.g., 4-bit instead of 32-bit). Enables running large models on smaller devices with minimal quality loss.
Why it matters
Quantization is why you can now run capable AI models on a laptop or even a phone instead of a data center. By storing each weight with fewer bits, it shrinks a model's memory footprint and speeds up responses, usually with only a small quality trade-off. This unlocks private, offline AI and dramatically lowers costs, which matters for anyone wary of sending data to the cloud.
In practice
A developer wants to run a large open-weight chatbot on a gaming PC with a single graphics card. The full model won't fit, so they download a 4-bit quantized version that does, and it answers nearly as well while using a fraction of the memory. A tip: if a quantized model feels noticeably less accurate, try a higher bit level like 6-bit or 8-bit as a compromise.
Related terms
AI Dubbing
Automated translation and re-voicing of audio/video content into other languages while preserving the original speaker's voice characteristics, timing, and emotional delivery.
AI Orchestration
Coordinating multiple AI models, tools, and data sources in a unified pipeline. An orchestration layer manages prompt routing, context passing, error handling, and output aggregation across different AI services.
API (Application Programming Interface)
A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own applications programmatically.
Attention Mechanism
A technique that allows AI models to focus on the most relevant parts of input data when generating output. In language models, attention determines which words in a sentence are most important for understanding each other word.
Diffusion Model
A type of generative AI that creates images by gradually removing noise from a random starting point. Powers tools like Stable Diffusion, DALL-E, and Midjourney. Works by learning the reverse of a noise-adding process.
Distillation
A technique where a smaller 'student' model learns to replicate the behavior of a larger 'teacher' model. Produces compact models that retain most of the teacher's capability while being faster, cheaper, and deployable on smaller devices.