The increasing computational demands of state-ofthe-art machine learning models have posed significant challenges for real-time applications, particularly in environments where hardware resources are limited. A novel adaptive inference-time compute framework is introduced, offering an efficient solution that dynamically adjusts computational resources based on the complexity of the input data. Through the use of dynamic scaling of model layers, attention heads, and precision levels, this approach optimizes computational efficiency without significantly compromising accuracy. Extensive experiments demonstrate the potential for substantial reductions in FLOPs, latency, and energy consumption, while maintaining high performance across various tasks. The results highlight the framework's ability to intelligently manage computational resources, ensuring scalability and practicality, especially in resource-constrained settings. This adaptive methodology represents a significant advancement in improving the balance between computational efficiency and model performance.