Mirantis has recently announced an important development that aims to simplify the deployment of artificial intelligence (AI) workloads through a new reference architecture. This innovative architecture, known as the Mirantis AI Factory Reference Architecture, focuses on leveraging a control plane specifically designed for Kubernetes clusters, making it easier for IT teams to build multi-tenant environments capable of running diverse AI applications.
Mirantis’ CTO, Shaun O’Meara, emphasizes that this reference architecture will streamline the process of assembling infrastructure using reusable templates. These templates encompass various essentials such as compute, storage, networking, and graphical processing units (GPUs) from leading manufacturers like NVIDIA, AMD, and Intel. By making the assembly of these components more efficient, Mirantis aims to help organizations effectively harness AI capabilities across their operations.
The foundation of this reference architecture is the Mirantis k0rdent AI, an extension of an open-source control plane that the company introduced earlier this year. One of the key advantages of this architecture is its ability to significantly reduce the time required for prototyping, iterating, and deploying AI models. Access to curated integrations for application-building tools and continuous integration/continuous delivery (CI/CD) platforms is also provided, alongside frameworks such as Gcore Everywhere Inference and NVIDIA AI Enterprise.
In addition to simplifying deployment, the Mirantis k0rdent AI aims to address several complex issues that organizations facing AI workloads often encounter. These include providing solutions for remote direct memory access (RDMA) networking, GPU allocation and slicing, scheduling requirements, performance tuning, and Kubernetes scaling.
The architecture is versatile, supporting various setups such as dedicated and shared servers, as well as public and hybrid/multi-cloud environments. This flexibility allows organizations to centralize the provisioning, configuration, and maintenance of their AI infrastructure, encompassing essential elements like storage and networking services.
However, there remains a challenge for IT teams: many are still underestimating the need to isolate AI workloads that run on shared infrastructure. With the increasing deployment of AI applications in production environments, the necessity for isolation is becoming more critical. This demand arises because few organizations can afford to dedicate complete infrastructure solely to a single application.
Moreover, companies are now grappling with digital sovereignty requirements, which dictate that certain workloads must run within designated geographic boundaries. O’Meara points out that many organizations lack the necessary expertise in IT infrastructure management required for successful AI workload deployment, a gap that the Mirantis k0rdent AI control plane seeks to bridge.
Organizations frequently overlook the total cost of AI by neglecting the IT infrastructure demands of running these workloads at scale. While the early days of AI deployment have shown promise, the challenge remains: AI applications are costly to develop and implement. With the increasing pressure to operationalize AI, IT teams will face an expanding array of infrastructure challenges, particularly as management of this infrastructure transitions from data science teams to IT operations.
In conclusion, Mirantis’ launch of the AI Factory Reference Architecture and the Mirantis k0rdent AI represents a significant step towards making AI deployment more approachable for organizations. As AI workloads continue to grow, both the complexities of managing the necessary infrastructure and the investment required to ensure success will remain at the forefront of discussions among IT leaders.
The evolution of AI technologies brings with it a host of new opportunities and challenges. The Mirantis k0rdent AI control plane aims to equip organizations with the necessary tools to navigate these dynamics effectively, ensuring that the operationalization of AI does not become a roadblock but rather a launchpad for innovation.
With these advancements, the journey towards seamless AI integration into everyday business functions becomes more achievable, opening doors for organizations to leverage AI to its fullest potential.
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