Right-size the model to the task : A task-specific model uses 20 to 30 times less energy than a general-purpose one. Distillation is key here; a full model could initially be trained from scratch and then refined for a specific task. DeepSeek R1, for instance, is “so huge that most organizations can’t afford to use it” because you need at least 8 GPUs. By contrast, distilled versions can be 10, 20 or even 30X smaller and run on a single GPU. This is the next frontier of added value. Make efficiency the default: Adopt “nudge theory” in system design, set conservative reasoning budgets, limit always-on generative features and require opt-in for high-cost compute modes. In cognitive science, “nudge theory” is a behavioral change management approach designed to influence human behavior subtly. Optimize hardware utilization: Use batching; adjust precision and fine-tune batch sizes for specific hardware generation to minimize wasted memory and power draw. Going from one batch size to plus-one can increase energy use because models need more memory bars. Incentivize energy transparency: Hugging Face earlier this year launched AI Energy Score. It’s a novel way to promote more energy efficiency, utilizing a 1- to 5-star rating system, with the most efficient models earning a “five-star” status. It could be considered the “Energy Star for AI,” and was inspired by the potentially-soon-to-be-defunct federal program, which set energy efficiency specifications and branded qualifying appliances with an Energy Star logo. Hugging Face has a leaderboard up now, which it plans to update with new models (DeepSeek, GPT-oss) and continually do so every 6 months or sooner as new models become available. The goal is that model builders will consider the rating as a “badge of honor.” Rethink the “more compute is better” mindset: Instead of chasing the largest GPU clusters, begin with the question: “What is the smartest way to achieve the result?” For many workloads, smarter architectures and better-curated data outperform brute-force scaling. Instead of simply going for the biggest clusters, enterprises have to rethink the tasks GPUs will be completing and why they need them, how they performed those types of tasks before, and what adding extra GPUs will ultimately get them.