Red Hat and Intel Introduce easy AI solution for private 5G networks
If you are looking for a way to deploy and scale AI applications on private 5G networks, you might be interested in the new solution from Red Hat and Intel. The two companies have teamed up to create a reference solution that combines Red Hat’s OpenShift platform and Intel’s FlexRAN software to run edge/cloud computing workloads on private 5G networks.
The solution is designed to be developer-friendly, flexible, and high-performance. It can also support third-party apps and provide enhanced security for industrial use cases. One of the customers using the solution is Minsait, a Spanish industrial tech consultancy that is applying AI to various scenarios, such as wildlife and fire detection on wind farms, drone-based asset management in warehouses, and response management to oil spills.
The solution leverages the benefits of private 5G networks, such as low latency, high bandwidth, and reliable connectivity, to enable AI on the factory floor and beyond. Red Hat and Intel claim that their solution can help enterprises accelerate their digital transformation and Industry 4.0 initiatives with AI. Red Hat is also collaborating with NTT, NVIDIA and Fujitsu on an edge-cloud AI proof of concept in Japan, using an all-photonics network and pipeline acceleration technologies. The proof of concept shows how OpenShift can enable low-latency and low-power real-time AI analysis at the edge.
NTT and Red Hat have successfully tested a real-time AI analysis platform that reduces latency and power consumption for edge computing. The platform uses Red Hat OpenShift to scale up the GPU without the CPU becoming a bottleneck. The platform also achieved a 60 percent reduction in latency and a 40 percent reduction in power consumption per camera at the edge compared to conventional AI inference workloads. The platform has been recognized by the IOWN Global Forum, which aims to create a sustainable future with net zero emissions using IOWN technologies. The platform is an example of how IOWN can enable collective intelligence of AI and green computing.