Efficient by Design: Small Models, Big Impact

Bigger is not always better. Efficient by Design is a deep learning book devoted to building smaller, faster models that deliver real impact without excessive cost or complexity.
The writing challenges the assumption that scale alone drives performance. Readers explore techniques that improve efficiency—model compression, distillation, pruning, quantization, and architectural simplicity—showing how thoughtful design can rival brute force.
Rather than treating efficiency as optimization after the fact, the book frames it as a design philosophy. Choices made early determine latency, energy use, deployability, and accessibility. Each chapter connects efficiency decisions to real constraints in edge devices, production systems, and global deployment.
The tone is practical and disciplined, aimed at engineers and leaders balancing performance with sustainability. Language remains actionable and grounded, translating theory into deployable strategy.
Efficient by Design moves through compact architectures, training-efficient methods, inference optimization, and cost-aware evaluation—demonstrating how impact scales when intelligence is lean.
Key themes explored include:
• Model efficiency and compression
• Performance under constraints
• Edge and real-time deployment
• Sustainable AI systems
• Design-driven optimization
Efficient by Design is for teams who ship—offering a blueprint to build models that matter without excess.