arXiv:2607.04553v1 Announce Type: cross Abstract: We present a bidirectional framework for estimating the energy consumption of text-to-video (T2V) and text-to-video-audio (T2VA) models from architectural first principles and observable generation parameters such as resolution and duration, requiring no access to weights, model size, or implementation details. Forward, it predicts energy from generation parameters and architectural principles; backward, it recovers architectural scaling behavior from observed inference times, with accuracy serving as a criterion for architectural validity. Bui
As AI models, particularly for video generation, scale in complexity and capability, understanding and managing their energy consumption becomes a critical but previously opaque factor.
This framework allows for the quantification of AI energy consumption without access to proprietary model details, enabling better planning for infrastructure, sustainability, and resource allocation in the rapidly expanding AI sector.
The ability to estimate energy costs from observable parameters changes how developers and policymakers can assess the environmental and economic footprint of large-scale AI deployments.
- · Energy utilities
- · Data center operators
- · Sustainability tech developers
- · Policymakers
- · AI developers ignoring energy efficiency
- · Regions with limited energy infrastructure
The framework provides a standardized method for estimating energy consumption of large AI models, particularly for video generation.
Increased awareness and quantification of AI energy demands will drive innovation in more energy-efficient AI architectures and potentially influence AI development toward smaller, more optimized models.
Energy consumption could become a primary constraint or competitive advantage for AI model deployment, leading to new geopolitical considerations for compute availability and sustainable AI development.
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Read at arXiv cs.AI