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Gcore AI Infrastructure improves deployment time with Cloud IPU Virtual vPOD integration

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Gcore a European provider of high-performance, low-latency, international cloud, and edge solutions — launches Cloud IPU Virtual vPODs, that enhance the functionality of Gcore’s AI infrastructure. This novel solution grants users direct access to BOW host machines and significantly reduces deployment time. As a result, Gcore offers users a cutting-edge and potent means of accessing their AI infrastructure.

Gcore’s IPU-based AI Cloud is designed to help businesses across various fields, including finance, healthcare, manufacturing, and scientific research. It is built to support every stage of their AI adoption journey, from building proof of concepts, to training and deployment. Gcore has joined forces with Graphcore, implementing their state-of-the-art IPU to meet the rapidly growing demand for powerful, efficient, secure artificial intelligence computing in the cloud.

Gcore is now expanding the capabilities of its AI Cloud. Cloud IPU Virtual vPOD is a flavor of an AI cluster in which a server is deployed on a virtual machine instead of a dedicated vPOD, which deploys a server on a dedicated bare metal server. Cloud IPU Virtual vPODs make it easy for users to access IPU hardware. Each IPU virtual instance gives users full access, allowing them to install and run any code they wish. This provides an ultrafast connection with IPU accelerators, ephemeral storage, custom code execution in input pipelines, and better integration of Cloud IPUs into research and production workflows.

Seva Vayner, Director of Edge Cloud stream at Gcore, said: “Cloud IPU Virtual vPODs are an excellent way for businesses to improve their machine-learning performance. This technology allows for the rapid building of reliable infrastructures that can be reallocated quickly. It helps to reduce excessive provisioning and energy and maintenance costs, making it an incredibly cost-effective solution. This way, Cloud IPU Virtual vPODs offer businesses increased performance and scalability while saving them money.”

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