Hybrid AI: The Future Path to Cost-efficient, Powerful AI Processing
In a recent whitepaper titled “The Future of AI is Hybrid,” Qualcomm postulates that the future of artificial intelligence will rest upon a hybrid architecture, namely a symbiosis of cloud and endpoint device operations. This fusion, they argue, will augment overall operational efficiency while simultaneously reducing usage costs.
In this document, Qualcomm elucidates that the most effective AI operational model integrates both cloud and endpoint device computation. This allows AI to distribute workloads as per actual requirements to appropriate locations, such as accelerating operations on endpoint devices and improving AI precision through cloud computing.
Moreover, Qualcomm anticipates that dispersing AI across various locations will contribute to reducing overall operational costs and accelerating implementation efficiency. Consequently, they predict that the blend of endpoint and cloud AI operation models will inevitably become a future trend.
Furthermore, Qualcomm has proposed AI stack combinations to provide OEM manufacturers and developers with comprehensive AI solutions. These can be applied in mobile devices, automobiles, mixed reality devices, edge computing, the Internet of Things, and cloud platforms. They also emphasize that the AI computational performance of the Snapdragon 8 Gen 2 processor has shown a significant improvement. When combined with cloud AI computational resources, it will drive even greater AI computational performance.
This approach, in fact, is shared by public cloud providers such as Google, Microsoft, and AWS. In their current plans for cloud AI applications, they too propose edge computing application solutions. They deploy small AI computational models on endpoint devices to execute faster response AI processing tasks that complete preliminary calculations. When larger computational tasks are required, they can be handled with more complex and precise computations through cloud data centers.
