What: Energy-efficient AI means designing and deploying models and runtimes that use less compute, power, and memory per inference while preserving useful accuracy. It covers techniques such as model pruning, quantization, knowledge distillation, efficient architectures, and optimized runtimes for edge or cloud hardware.

Why: Efficiency delivers concrete operational wins—lower cloud bills, faster responses, longer battery life on devices, and a smaller carbon footprint. It also enables on-device personalization, broader edge deployments, and clearer paths to meet regulatory or sustainability requirements.

How: Apply focused, measurable techniques and validate them on real hardware:

• Prune: Remove unnecessary weights to shrink model size and memory; structured pruning can improve runtime speed on accelerators.
• Quantize: Lower numeric precision (FP16/INT8) to cut inference time and energy with minor accuracy cost when calibrated.
• Distill: Train compact student models to mimic larger teachers, retaining much capability at far lower compute.
• Use efficient architectures & dynamic inference: Choose MobileNet/EfficientNet designs and techniques like early exits to spend less work on easy inputs.
• Measure and iterate: Profile with PyTorch Profiler, TensorBoard, and platform traces; estimate emissions with CodeCarbon; convert to portable formats (ONNX, TFLite) and test on target devices.

Practical steps: 1) Benchmark baseline (latency, tail latency, energy per inference, FLOPs, parameters). 2) Pick one modest goal (e.g., 20% latency or 15% energy reduction). 3) Run a single targeted experiment (INT8 quantization or light pruning). 4) Re-measure, run A/B tests, and monitor in production.

What if you don’t (or want to go further): Ignoring efficiency increases costs, limits edge reach, and can compound small accuracy or fairness regressions across many users. To go further, publish reproducible ablation studies, track hidden training energy, cross-check claims with MLPerf and vendor reports, and adopt open, portable runtimes to avoid vendor lock-in.

Bottom line: Small, targeted efficiency choices—measured on real hardware and reported transparently—translate into faster experiences, lower costs, and measurable environmental benefits that users and stakeholders notice.