Sponsored Post Any organization that has access to AI models hosted in the cloud knows how challenging it can be to ensure that the large volumes of data needed to build and train those types of workloads can be accessed and ingested quickly to avoid potential performance delays.

Chatbots and virtual assistants, map generation, AI tools for software engineers, analytics, defect detection, and generative AI applications—these are just some of the use cases that can benefit from real-time performance that can help eliminate these delays. And the Gcore Inference at the Edge service is designed to provide businesses in various industries, including IT, retail, gaming and manufacturing, with just that.

Latency is an issue that tends to be exacerbated when the collection and processing of datasets distributed across multiple geographic sources via the network is involved. It can be particularly problematic when deploying and scaling real-time AI applications in smart cities, TV translation and autonomous vehicles. Taking these workloads out of a centralized data center and hosting them at the network edge, closer to where the data actually resides, is a way around the problem.

That's what the Gcore Inference at the Edge solution is specifically designed to do. It deploys customers' pre-trained or custom machine learning models (including open-source Mistral 7B, Stable-Diffusion XL and LLaMA Pro 8B models for example) to "edge inference nodes" located at over 180 locations in the company's content delivery network (CDN).