This research explores comprehensive approaches to deploying Generative AI models directly on edge devices rather than relying on cloud infrastructure.

Key findings:

• Edge-based GenAI deployment reduces latency and enhances security by keeping sensitive data local
• Implementation requires specific software optimizations, hardware adaptations, and specialized frameworks
• Effective deployment strategies balance computational constraints with model performance
• Future edge GenAI will enable new applications in resource-constrained environments

For engineering teams, this represents a significant shift in AI deployment architecture, enabling more responsive, private, and efficient GenAI applications in scenarios where cloud connectivity is limited or undesirable.

GenAI at the Edge: Comprehensive Survey on Empowering Edge Devices