Generative AI is, without question, the darling of the IT industry. Its ability to answer complex questions, create new output and handle tasks quickly and accurately opens up a new world of applications. The possibilities seem endless.
Analysts are bullish on its economic potential. Bloomberg expects the generative AI market to exceed $1.3 trillion by 2032, representing 12 percent of total technology spending. McKinsey has estimated that generative AI will be worth $4.4 trillion in a decade. That’s 4 percent of world GDP.
Most organizations are only beginning to experiment with AI. Those that have implemented it primarily use productivity tools such as Microsoft 365 Copilot via the cloud. The development of more customized AI applications will require significant changes to the IT environment.
That’s why the hybrid IT model is ideal for AI. Hybrid IT allows organizations to distribute AI workloads among cloud platforms and edge devices, reducing costs, improving performance and increasing security.
The deep learning models used for generative AI mimic the activity of the human brain by predicting patterns in data. The model, comprising layers of complex algorithms, is trained by analyzing massive datasets. The result of the training is AI inference, which executes the model.
AI remained in the realm of science fiction until computer hardware became powerful enough to handle the computations involved. Graphics processing units (GPUs) are ideal for AI because they have thousands of cores that can handle thousands of threads simultaneously. However, GPUs require up to 15 times as much electricity as traditional CPUs.
Until recently, both AI training and inference have largely been constrained to the cloud. However, as AI moves into mainstream adoption, inferencing scales faster than training. The cost of running in the cloud can quickly become unsustainable.
A hybrid IT environment helps reduce these costs. AI inferencing has already been moving to smartphones, laptops and other edge devices. By taking advantage of these devices, organizations can scale their AI implementations while reducing the strain on cloud environments and accompanying costs. Edge devices are also more energy efficient, enabling organizations to meet their sustainability goals.
Moving some AI processing to edge devices can also improve performance and reduce latency. In addition, local processing enables AI to operate even if a device lacks connectivity. Because data isn’t transferred to and from the cloud, security and privacy are improved. AI apps can also be personalized according to each user’s unique needs and characteristics.
AI workloads can be distributed across a hybrid IT environment in several ways. Processing can occur primarily on the edge device, with more compute-intensive tasks offloaded to the cloud. This handoff is seamless to the user.
In other models, the cloud and local device share the AI workload. Edge devices can also serve as the “eyes and ears” of the AI application, collecting data and sending it to the cloud for processing.
Which model you choose depends on the applications. Digital assistants, generative AI search and productivity tools are best suited for a device-centric model. Augmented reality/virtual reality applications work best in the shared-workload environment, with inference occurring in the cloud and the XR headset rendering the 3-D images. Many IoT applications use the device-sensing model.
A hybrid IT environment is essential to the deployment and use of AI. Whether organizations use SaaS solutions or train an existing AI model using their data, hybrid IT can reduce cost and risk while providing a better user experience. Hybrid IT can also be used for custom AI development — once the model is developed and trained in the cloud, inference occurs on edge devices.
Technologent has a practice dedicated to the architecture, design and implementation of hybrid IT environments. Let us help you capitalize on the value of hybrid AI.