7.2 Resource-Efficient Training

As we’ve seen, today’s largest language models achieve astonishing results—but at enormous cost. Training a state-of-the-art LLM can involve thousands of GPUs running for weeks, consuming massive energy and driving up environmental impact. The solution? Resource-efficient training: methods that retain model accuracy while dramatically reducing compute needs.

In Chapter 7.2 of the book, we explore the leading approaches that make LLM development faster, cheaper, and more sustainable.

What You’ll Discover

1. Model Distillation

Train a smaller student model to mimic a larger teacher. Distillation speeds up inference, reduces costs, and enables deployment on edge devices or mobile hardware.

2. Quantization

Lower model weight precision (e.g., FP32 → FP16 or INT8). This cuts memory use and boosts inference speed, with FP16 balancing accuracy and efficiency, and INT8 enabling ultra-low-power deployment.

3. Distributed Training

Break up massive training workloads across many GPUs or TPUs. Data parallelism and model parallelism make it possible to train models that no single device could handle.

4. Improving Data Efficiency

Smarter data use saves compute: transfer learning reduces the need for full retraining, augmentation expands datasets synthetically, and cleaning removes low-quality inputs.

5. LLM-Driven Optimization

Emerging techniques let LLMs optimize their own training—through self-refinement and automated curriculum generation— reducing human effort while boosting efficiency.

7.2 covers:

• Distillation and Quantization: Smaller, faster, and lighter models with minimal accuracy trade-offs.
• Distributed Training: Makes it possible to train at massive scale.
• Data Efficiency: Transfer learning, augmentation, and cleaning reduce wasteful computation.
• LLM-Driven Optimization: Promises automated efficiency improvements for the future.