Bringing AI inference closer to the data source offers significant advantages in cost, privacy and performance. Recent advancements in lightweight GenAI models (i.e., 1-8B parameters) provide a disruptive opportunity to shift GenAI deployment from the cloud to the edge, but alternatives to cloud-based GenAI need to be practical and efficient. This white paper outlines a strategic approach to shift GenAI deployments from cloud-native (i.e., GPU based) solutions to edge (i.e., hardware based) solutions using the built-in compute acceleration of CPUGPU-NPU (e.g., Intel® Core™ Ultra processors, Intel® Arc™ GPUs) and open source GenAI models. On-device deployment offers low total cost of ownership (TCO), offline capabilities, data sovereignty and reduced latency, making powerful GenAI models accessible across regions and sectors that may previously have faced barriers to deployment.

Large, sophisticated GenAI models have immense computational needs, and as a result, cloud-based GenAI solutions have dominated. However, these models require substantial infrastructure investment or recurring end-user API costs, which limits access to their advanced capabilities. Recent advances in GenAI open ecosystem development are changing the landscape; models are optimized to be smaller, faster and less power hungry, enabling edge-based, decentralized deployments with the ability to run GenAI models locally. This shift from cloud-based AI towards offline, edge-based AI presents a compelling deployment alternative — one that democratizes GenAI access away from centralized infrastructure and offers benefits like reduced costs and increased data privacy by leveraging hardware (CPU-GPU-NPU).