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The shortage of high-end processing chips is leading to difficulty (for researchers, small companies and edge computing systems) in accessing powerful AI hardware. This study attempts to make AI work efficiently on everyday graphics cards (Particularly for NVIDIA RTX 4060). We propose a system that makes AI models smaller and faster using less memory and power. Our experimentation is specially designed for regular consumer graphics cards rather than expensive server equipment. Testing this method on 10,000 images results in 45% less power and runs 20% faster than standard methods, with accuracy dropping by less than 2.5%. This improvement allows secure, fast AI applications to run locally on devices. The proposed application domain may be real-time image recognition in smart home systems (Without needing to send data to remote servers). This reduces security risks and makes AI more accessible to everyone by working on widely available hardware. This paper shows ways to make AI more sustainable, secure and affordable for everyday use.

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Secure and Energy Efficient Inference Optimisation for Consumer GPUs in Edge Computing Environments

  • 会议论文

  • 发表日期：

    2026年

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  • Shaligram Prajapat,
  • Abhiraj Chouhan,
  • Nitin Nagar,
  • Prakshep Goswami,
  • Priyanshi Soni,
  • Soham Kothari

摘要

The shortage of high-end processing chips is leading to difficulty (for researchers, small companies and edge computing systems) in accessing powerful AI hardware. This study attempts to make AI work efficiently on everyday graphics cards (Particularly for NVIDIA RTX 4060). We propose a system that makes AI models smaller and faster using less memory and power. Our experimentation is specially designed for regular consumer graphics cards rather than expensive server equipment. Testing this method on 10,000 images results in 45% less power and runs 20% faster than standard methods, with accuracy dropping by less than 2.5%. This improvement allows secure, fast AI applications to run locally on devices. The proposed application domain may be real-time image recognition in smart home systems (Without needing to send data to remote servers). This reduces security risks and makes AI more accessible to everyone by working on widely available hardware. This paper shows ways to make AI more sustainable, secure and affordable for everyday use.