Efficiency & Compression

The most capable AI models are enormous - hundreds of billions of parameters, requiring specialised hardware clusters just to run. This creates a fundamental tension: the best models are also the most expensive to operate, the slowest to respond and the hardest to deploy. Efficiency and compression techniques address this by making models smaller, faster and cheaper without sacrificing too much quality. Quantisation reduces the numerical precision of model weights. Pruning removes unnecessary connections. Knowledge distillation transfers a large model's knowledge into a smaller one. These aren't just academic exercises - they're what makes it possible to run AI on your phone, keep API costs manageable and serve millions of users simultaneously. The efficiency gains can be dramatic: a well-quantised model might use a quarter of the memory while retaining 95% of the quality. For businesses, these techniques determine the practical economics of AI deployment: the gap between "technically possible" and "commercially viable" often comes down to how efficiently the model can run, and understanding these trade-offs helps you make better decisions about which model to use, where to deploy it and what performance-cost balance makes sense for your specific application.