Part 8/9:
Training LLaMA 3.3 involved substantial GPU resources, accumulating 39.3 million GPU hours and generating approximately 11,390 tons of CO2 emissions. However, Meta counters this environmental impact by utilizing renewable energy resources to achieve net-zero emissions during the training phase. Transparency about energy consumption highlights Meta's commitment to addressing the ecological footprint of AI development.
LLaMA's Competitive Edge
In performance evaluations, LLaMA 3.3 frequently outshines models of similar sizes, achieving an 86% accuracy on math benchmarks and 77% on coding tasks, among others. While it may not always surpass the largest models in every task, it remains competitive and shows promise in multilingual reasoning.