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.
A concrete example
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.
How to use it
It is what lets a large model run on hardware that could not otherwise hold it, at some cost in precision. If you are running models locally, this is the main lever between "fits and is fast" and "more accurate": try the smaller precision first and only move up if quality on your actual task is visibly worse. The loss is rarely uniform, so test on the work you care about rather than on a benchmark.
The common mistake
Assuming the quality cost is negligible because a benchmark score barely moved. Aggregate scores hide degradation on specific capabilities, and reasoning and long-form coherence tend to suffer before simple recall does.
Related terms
Inference
The process of running data through a trained AI model to get predictions or outputs. When you send a prompt to ChatGPT or Vincony, the model performs inference to generate a response.
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.
Inference Cost
The computational expense of running a trained AI model to generate outputs. Measured in cost per token or per request. Varies dramatically between models — GPT-4 class models cost 10-50x more per token than smaller models.
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.