Key 7 - AI Model Size Explained: Parameters, Capabilities & Edge AI

Bigger isn’t always better but it often is. Understand how model parameters (connections between neural network layers) determine capabilities, why GPT-4 vastly outperforms GPT-2, and the emerging trend of smaller, smarter models through knowledge distillation, pruning, and quantization. Discover how AI is moving from massive data centers to smartphones and edge devices.

Key concepts covered:

What parameters mean: synapses connecting neural network layers
Evolution from GPT-2 (1.5B parameters) to GPT-4 (1T+ parameters)
How more parameters = more pattern recognition capability
Techniques to compress models: distillation, pruning, quantization
Small models (Llama 3.2, Gemini Nano) running locally on phones
Choosing model size based on your computational resources and needs

Other videos in this series:
Building on privacy considerations from Key 6 , next explore Key 8 about how reasoning models “think” before responding.

Who this is for: Developers, AI engineers, and tech decision-makers choosing between model options, or anyone curious about how model architecture affects performance and deployment.

#ModelParameters #NeuralNetworks #EdgeAI #ModelCompression #AIArchitecture