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Is Your AI Infrastructure a Bottleneck, Not a Launchpad?
Mirantis unveils the industry's first AI Factory Reference Architecture, addressing scalable, secure, and sovereign AI/ML infrastructure, rapid deployment, and optimized AI development lifecycles.
Key Highlights
- Mirantis launched the AI Factory Reference Architecture, aiming to streamline AI/ML infrastructure deployment and management.
- The architecture aims to accelerate AI workload deployment and shorten development lifecycles.
- It is designed to address complex high-performance computing challenges like RDMA networking and GPU allocation.
- The framework supports various AI workload types across diverse environments including edge and multi-cloud.
- The architecture aims to simplify AI infrastructure for data scientists and developers, addressing skill gaps.
The News
Mirantis, a company specializing in Kubernetes-native AI infrastructure, recently announced the Mirantis AI Factory Reference Architecture. This new offering is designed to provide a comprehensive guideline for building, operating, and optimizing AI and ML infrastructure at scale. The company states it is the industry’s first such reference architecture, aiming to support the complex requirements of modern AI workloads. Find out more by clicking here to read the press release.
Analyst Take
The evolving landscape of AI infrastructure, and the announcement from Mirantis regarding their AI Factory Reference Architecture is noteworthy. This move by Mirantis aims to address a growing pain point for enterprises: the complexity of deploying and managing AI and machine learning workloads at scale. As AI adoption accelerates, the underlying infrastructure often becomes a significant bottleneck, particularly for organizations lacking deep expertise in high-performance computing.
The core premise of the Mirantis AI Factory Reference Architecture, built on Mirantis k0rdent AI, revolves around providing a secure, composable, scalable, and sovereign platform. This is an ambitious goal, but one that directly aligns with the challenges I am seeing in the market. Many organizations are grappling with how to effectively provision specialized resources like GPUs and CPUs for AI models while simultaneously ensuring a smooth experience for data scientists and developers who are not infrastructure specialists.
One of the key trends I am tracking is the increasing demand for "AI-ready" infrastructure that can be deployed rapidly. Mirantis aims to tackle this by claiming that AI workloads can be deployed within days of hardware installation through k0rdent AI’s templated, declarative model. This emphasis on rapid provisioning and a shorter AI development lifecycle is critical. Faster prototyping, iteration, and deployment of models and services could genuinely accelerate innovation within enterprises. The inclusion of curated integrations via the k0rdent Catalog for essential AI/ML tools, observability, CI/CD, and security, leveraging open standards, is also a sensible approach. Interoperability and ease of integration are paramount in today's heterogeneous IT environments.
My observations suggest that the distinction between traditional cloud-native workloads and AI workloads is becoming increasingly stark. While cloud-native applications often thrive on scale-out, multi-core operations, AI workloads can demand the aggregation of numerous GPU-based servers into what essentially becomes a single supercomputer. This requires specialized networking, such as RDMA, and ultra-high-performance capabilities. The Mirantis reference architecture’s stated focus on these issues, including GPU allocation and slicing, sophisticated scheduling requirements, and performance tuning, indicates an understanding of these unique demands.
Furthermore, the architecture’s support for various AI workload types—training, fine-tuning, and inference—across diverse environments like dedicated or shared servers, virtualized environments (KubeVirt/OpenStack), public cloud, hybrid/multi-cloud, and edge locations, is a comprehensive approach. This flexibility is crucial given the varied deployment strategies enterprises are exploring for AI.
From my perspective, the challenges Mirantis highlights, such as the fine-tuning and configuration complexity of AI systems, the need for hard multi-tenancy for data security and isolation, and the critical importance of data sovereignty, are indeed prevalent. Data sovereignty, in particular, is a growing concern, as AI and ML workloads often contain sensitive intellectual property and proprietary data, making controlled usage and compliance with regional regulations non-negotiable. The ability to manage scale and sprawl, especially with highly distributed edge workloads, and the imperative to effectively share scarce and expensive GPU resources, are also points of significant friction for many organizations. Finally, the perennial challenge of skills availability, where data scientists and developers lack deep IT infrastructure expertise, is a recurring theme in my conversations with clients.
The composable nature of the Mirantis AI Factory Reference Architecture, allowing users to assemble infrastructure from reusable templates across compute, storage, GPU, and networking layers, tailored to specific AI workload needs, could provide much-needed agility. The support for accelerators from NVIDIA, AMD, and Intel is also a pragmatic step, acknowledging the multi-vendor reality of the hardware ecosystem.
What was Announced
The Mirantis AI Factory Reference Architecture is presented as a comprehensive guideline for enterprises to build, operate, and optimize AI and ML infrastructure. The architecture is built upon Mirantis k0rdent AI, designed to provide a secure, composable, scalable, and sovereign platform.
Key functional claims include:
- Rapid Deployment: The architecture is designed to enable the deployment of AI workloads within days of hardware installation, leveraging k0rdent AI's templated, declarative model for provisioning. This aims to dramatically shorten the AI development lifecycle.
- Accelerated Development: It is architected to facilitate faster prototyping, iteration, and deployment of models and services.
- Curated Integrations: The k0rdent Catalog aims to provide curated integrations for AI/ML tools, observability, CI/CD, and security, utilizing open standards.
- High-Performance Computing (HPC) Optimization: The architecture is designed to address complex HPC issues. This includes explicit support for Remote Direct Memory Access (RDMA) networking, fine-grained GPU allocation and slicing, and sophisticated scheduling requirements. It also aims to deliver performance tuning capabilities and enhanced Kubernetes scaling for AI workloads.
- Integration with AI Platform Services: The architecture is designed to integrate with various AI Platform Services, citing examples like Gcore Everywhere Inference and the NVIDIA AI Enterprise software ecosystem.
- Workload Versatility: It leverages Kubernetes and is architected to support multiple AI workload types, including training, fine-tuning, and inference. This support extends across various deployment environments: dedicated or shared servers, virtualized environments (KubeVirt/OpenStack), public cloud, hybrid/multi-cloud, and edge locations.
- Addressing Unique AI Infrastructure Needs: The reference architecture aims to resolve novel challenges related to provisioning, configuration, and maintenance of AI infrastructure. Specific areas of focus include high-performance storage and ultra-high-speed networking (Ethernet, Infiniband, NVLink, NVSwitch, CXL) to manage substantial AI data movement.
- Operational Challenges Addressed: The architecture aims to mitigate challenges such as the time-consuming nature of fine-tuning and configuration for AI systems, the requirement for hard multi-tenancy for data security and isolation, efficient resource allocation and contention management, and ensuring data sovereignty and compliance with regional regulatory requirements for AI/ML workloads containing sensitive intellectual property. It also addresses managing the scale and sprawl of distributed AI infrastructure and enabling efficient resource sharing of scarce and expensive GPU resources.
- Composability: The architecture is designed to be composable, allowing users to assemble infrastructure components from reusable templates across compute, storage, GPU, and networking layers, tailored to specific AI workload needs.
- Multi-Vendor Accelerator Support: It aims to include support for AI accelerators from NVIDIA, AMD, and Intel.
Looking Ahead
The introduction of a comprehensive reference architecture like the one from Mirantis signals a maturing phase in enterprise AI adoption. While many organizations have experimented with AI, scaling these initiatives beyond pilot projects remains a significant hurdle. The key trend that I am going to be looking out for is how effectively such reference architectures translate into tangible reductions in deployment time and operational complexity for enterprises. The emphasis on rapid provisioning and a shorter development lifecycle addresses a critical need, as speed to insight and model deployment directly impacts business value.
My perspective is that solutions simplifying the underlying infrastructure will gain significant traction. The focus on abstracting away the intricacies of high-performance computing, such as RDMA networking and GPU management, is particularly astute. This directly addresses the "skills availability" gap, where data scientists and developers often lack the deep IT infrastructure expertise required to manage these complex environments. When you look at the market as a whole, this announcement today is a step towards democratizing AI infrastructure, making it more accessible to a broader range of enterprises beyond those with large, specialized IT teams.
Going forward, how Mirantis performs on its claims of enabling true multi-tenancy and robust data sovereignty will be key. These are not merely technical features but critical enablers for enterprises operating under stringent regulatory frameworks or handling highly sensitive data. The ability to integrate seamlessly with various AI platform services, including established ecosystems like NVIDIA AI Enterprise, will also be a key differentiator. HyperFRAME will be tracking how the company does in future quarters, specifically observing adoption rates and documented case studies that validate the promised improvements in deployment speed and operational efficiency. The success of this reference architecture will hinge on its ability to deliver on its promise of making scalable, secure, and sovereign AI infrastructure an achievable reality for the mainstream enterprise, not just the early adopters.
Steven Dickens | CEO HyperFRAME Research
Regarded as a luminary at the intersection of technology and business transformation, Steven Dickens is the CEO and Principal Analyst at HyperFRAME Research.
Ranked consistently among the Top 10 Analysts by AR Insights and a contributor to Forbes, Steven's expert perspectives are sought after by tier one media outlets such as The Wall Street Journal and CNBC, and he is a regular on TV networks including the Schwab Network and Bloomberg.