IONOS Cloud GPU Servers Integrations

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NVIDIA's virtual GPU (vGPU) software delivers high-performance GPU capabilities essential for various tasks, including graphics-intensive virtual workstations and advanced data science applications, allowing IT teams to harness the advantages of virtualization alongside the robust performance provided by NVIDIA GPUs for contemporary workloads. This software is installed on a physical GPU within a cloud or enterprise data center server, effectively creating virtual GPUs that can be distributed across numerous virtual machines, permitting access from any device at any location. The performance achieved is remarkably similar to that of a bare metal setup, ensuring a seamless user experience. Additionally, it utilizes standard data center management tools, facilitating processes like live migration, and enables the provisioning of GPU resources through fractional or multi-GPU virtual machine instances. This flexibility is particularly beneficial for adapting to evolving business needs and supporting remote teams, thus enhancing overall productivity and operational efficiency.

Intel’s Gaudi software provides developers with an extensive array of tools, libraries, containers, model references, and documentation designed to facilitate the creation, migration, optimization, and deployment of AI models on Intel® Gaudi® accelerators. This platform streamlines each phase of AI development, encompassing training, fine-tuning, debugging, profiling, and enhancing performance for generative AI (GenAI) and large language models (LLMs) on Gaudi hardware, applicable in both data center and cloud settings. The software features current documentation that includes code samples, best practices, API references, and guides aimed at maximizing the efficiency of Gaudi solutions such as Gaudi 2 and Gaudi 3, while also ensuring compatibility with widely-used frameworks and tools for model portability and scalability. Users have access to performance metrics to evaluate training and inference benchmarks, can leverage community and support resources, and benefit from specialized containers and libraries designed for high-performance AI workloads. Furthermore, Intel's commitment to ongoing updates ensures that developers remain equipped with the latest advancements and optimizations for their AI projects.