Unlocking AI innovation: GPU-as-a-Service with Red Hat

Unlocking AI innovation: GPU-as-a-Service with Red Hat The GPU challenge: A multifaceted problem for ITOps Red Hat's solution: Solving the GPU puzzle with GPU-as-a-Service Key components for delivering GPU-as-a-Service Red Hat: Your partner in AI innovation Get started with AI Inference About the author Martin Isaksson More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Graphics processing units (GPUs) are key to both generative and predictive AI. Data scientists, machine learning engineers, and AI engineers rely on GPUs to experiment with AI models, and to train, tune, and deploy them. Managing these essential resources can be complex, however, often stalling development and innovation. Infrastructure limitations shouldn't hold your organization back. Your team needs to focus on building, refining, and using AI models, not managing complex GPU infrastructure. This is why information technology operations (ITOps) plays a crucial role in enabling rapid AI development and inference by providing on-demand GPU access, also known as GPU-as-a-Service. Setting up an efficient GPU infrastructure for AI workloads is not trivial, and ITOps teams face several significant challenges: GPU scarcity and cost constraints : GPUs can be challenging to access due to limited supply, cloud constraints, and internal competition. This can be further compounded with a lack of customer choice and control over the underlying accelerator architecture, not to mention that. GPUs already come with high costs, including acquisition and operational expenses, and are often underused. Lack of GPU access drives shadow IT : If data scientists, ML engineers, and AI engineers cannot readily access GPUs when they need them, they may turn to "shadow IT. " This can mean using third-party services, potentially exposing sensitive company data, or independently procuring GPU resources from various cloud providers, leading to increased costs and security risks. This results in a loss of control over resource usage, data security, and compliance.