ZEDEDA Automated Orchestration for the Distributed Edge

Event:

Appearance: ZEDEDA Edge Field Day Showcase

Company: ZEDEDA

Video Links:

Personnel: Padraig Stapleton

In this Edge Field Day showcase, ZEDEDA’s Padraig Stapleton, SVP and Chief Product Officer, provides a comprehensive overview of ZEDEDA, its origins, and its vision for bringing the cloud experience to the unique and often hostile environment of the edge. The video highlights how ZEDEDA’s platform enables businesses to securely and scalably run their applications at the edge. The discussion covers how the platform addresses the complexities of diverse hardware, environments, and security challenges, allowing customers to focus on their core business applications.
This presentation also introduces the ZEDEDA edge computing platform for visibility, security and control of edge hardware and applications. The presentation details a unique partnership with OnLogic to provide zero-touch provisioning and discusses various real-world use cases, including container shipping, global automotive manufacturing, and oil and gas.


Unlock AI Cloud Potential with the Rafay Platform

Event: AI Infrastructure Field Day 3

Appearance: Rafay presents at AI Infrastructure Field Day 3

Company: Rafay

Video Links:

Personnel: Haseeb Budhani

Haseeb Budhani, CEO of Rafay Systems, discusses how the Rafay platform can be used to address AI use cases. The platform provides a white-label ready portal that allows end users to self-service provision various compute resources and AI/ML platform services. This enables cloud providers and enterprises to offer services like Kubernetes, bare metal, GPU as a service, and NVIDIA NIM with a simple and standardized experience.

The Rafay platform leverages standardization, infrastructure-as-code (IaC) concepts, and GitOps pipelines to drive consumption for a large number of enterprises. Built on a Git engine for configuration management and capable of handling complex multi-tenancy requirements with integration to various identity providers, the platform allows customers to offer different services, compute functions, and form factors to their end customers through configurable, white-labeled catalogs. Additionally, the platform features a serverless layer for deploying custom code on Kubernetes or VM environments, enabling partners and customers to deliver a wide range of applications and services, from DataRobot to Jupyter notebooks, as part of their offerings.

Rafay addresses security concerns through SOC 2 Type 2 compliance for its SaaS product, providing pentest reports and agent reports for customer assurance. For larger customers, particularly cloud providers, an air-gapped product is offered, allowing them to deploy and manage the Rafay controller within their own secure environments. Furthermore, the platform’s unique Software Defined Perimeter (SDP) architecture enables it to manage Kubernetes clusters remotely, even on edge devices with limited connectivity, by establishing an inside-out connection and a proxy service for secure communication.


From Infrastructure Chaos to Cloud-Like Control with Rafay

Event: AI Infrastructure Field Day 3

Appearance: Rafay presents at AI Infrastructure Field Day 3

Company: Rafay

Video Links:

Personnel: Haseeb Budhani

Rafay, founded seven years ago, initially focused on Kubernetes but has evolved to address the broader challenge of simplifying compute consumption across various environments. Their solution aims to provide self-service compute to companies across verticals.

Rafay typically engages with companies that already have existing infrastructure, automation, and deployments. The core problem they solve is standardization across diverse environments and users. They help companies build a platform engineering function that enables efficient management of environments, upgrades, and policies. The Rafay platform abstracts the underlying infrastructure, providing an interface for users to request and consume compute resources without needing to understand the complexities of the underlying systems.

Rafay’s platform allows organizations to deliver self-service compute across diverse environments and teams, managing identity, policies, and automation. The goal is to reduce the time developers waste on infrastructure tasks, which, according to Rafay, can be as high as 20% in large enterprises. They offer a comprehensive solution that encompasses inventory management, governance, and control, all while generating the underlying infrastructure as code for versioning and auditability. In summary, Rafay enables companies to move away from custom, in-house solutions to a standardized, automated, and cloud-like compute consumption model.


Bridging the gap from GPU-as-a-Service to AI Cloud with Rafay

Event: AI Infrastructure Field Day 3

Appearance: Rafay presents at AI Infrastructure Field Day 3

Company: Rafay

Video Links:

Personnel: Haseeb Budhani

Rafay CEO Haseeb Budhani argues that to truly be considered a cloud provider, organizations must offer self-service consumption, applications (or tools), and multi-tenancy. He contends that many GPU clouds currently rely on manual processes like spreadsheets and bare metal servers, which don’t qualify as true cloud solutions. Budhani emphasizes that users should be able to access a portal, create an account, and consume services on demand, without requiring backend intervention for tasks like VLAN setup or IP address management.

Budhani elaborates on his definition of multi-tenancy, outlining the technical requirements for supporting diverse customer needs. This includes secure VMs, operating system images with pre-installed tools, public IP addresses, firewall rules, and VPCs. He highlights the difference between customers needing a single GPU versus those requiring 64 GPUs and emphasizes that all necessary networking and security configurations must be automated to provide a true self-service experience.

Ultimately, Budhani argues that the goal is self-service consumption of applications or tools, not just GPUs. He believes the industry is moving beyond the “GPU as a service” concept, with users now focused on consuming models and endpoints rather than managing the underlying GPU infrastructure. He suggests that his company, Rafay, addresses many of the complexities in this space, offering solutions that enable the delivery of applications and tools in a self-service, multi-tenant environment.


Accelerating AI Infrastructure Adoption for GPU Providers and Enterprises with Rafay

Event: AI Infrastructure Field Day 3

Appearance: Rafay presents at AI Infrastructure Field Day 3

Company: Rafay

Video Links:

Personnel: Haseeb Budhani

Haseeb Budhani, CEO of Rafay Systems, begins by highlighting the confusion surrounding Rafay’s classification, noting that people variously describe it as a platform as a service (PaaS), orchestration, or middleware, and he welcomes feedback on which term best fits. He then pivots to discussing the current market dynamics in AI infrastructure, particularly the discrepancy between the cost of renting GPUs from providers like Amazon versus acquiring them independently. He illustrates this with an example of using DeepSeek R1, highlighting that while Amazon charges significantly more for consuming the model via Bedrock, renting the underlying H100 GPU directly is much cheaper.

Budhani argues that many companies renting out GPUs are not true “clouds” and may struggle in the long term because they are not selling services on top of the GPUs. He references an Accenture report suggesting that GPU as a Service (GPaaS) will diminish as the market matures, with more value being derived from services. He emphasizes that hyperscalers like Amazon have understood this for a long time, generating most of their revenue from services rather than infrastructure as a service (IaaS). This presents an opportunity for Rafay to help GPU providers and enterprises deliver these higher-level services, enabling them to compete more effectively with hyperscalers and unlock significant cost savings, citing an example of a telco in Thailand that could save millions by deploying its own AI infrastructure with Rafay’s software.

The speaker concludes by emphasizing the increasing importance of sovereign clouds, especially in regions like Europe and the Middle East. Telcos, which previously lost business to public clouds, now have a renewed opportunity to provide AI infrastructure locally due to sovereignty requirements. He states that Rafay aims to provide these telcos and other regional providers with the necessary software stack to deliver these services, thereby addressing a common problem across various geographic locations. He highlights a telco in Indonesia, Indosat, as an early example of a customer using Rafay to deliver a sovereign AI cloud, underscoring the growing demand for such solutions globally.


The Open Flash Platform Initiative with Hammerspace

Event: AI Infrastructure Field Day 3

Appearance: Hammerspace presents at AI Infrastructure Field Day 3

Company: Hammerspace

Video Links:

Personnel: Kurt Kuckein

The Open Flash Platform (OFP) Initiative is a multi-member industry collaboration founded in July 2025. The initiative’s goal is to redefine flash storage architecture, particularly for high-performance AI and data-centric workloads, by replacing traditional storage servers with an open approach that yields a more efficient and modular, standards-based, and disaggregated model.

The presentation highlights the growing challenges of data storage, power consumption, and cooling in modern data centers, especially with the increasing volume of data generated at the edge. The core idea behind the OFP initiative is to leverage recent advancements in large-capacity flash (QLC), powerful DPUs (Data Processing Units), and Linux kernel enhancements to create a highly dense, low-power storage platform. This platform aims to replace traditional CPU-based storage servers with a modular design, ultimately allowing for exabyte-scale deployments within a single rack.

The proposed architecture consists of sleds containing DPUs, networking, and NVMe storage, fitting into trays that can be modularly deployed. This approach offers significant improvements in density and power efficiency compared to existing solutions. While the initial concept uses U.2 drives, the long-term goal is to leverage an extended E.2 standard for even greater capacity. Hammerspace is leading the initiative, fostering collaboration among industry players, including DPU and SSD partners, and exploring adoption by organizations like the Open Compute Project (OCP).

Hammerspace envisions a future where AI infrastructure relies on open standards and efficient hardware. The OFP initiative aligns with this vision by providing a non-proprietary, high-capacity storage platform optimized for AI workloads. The goal is to allow for modernizing storage systems without having to buy additional storage systems, utilizing the flash that’s already available. This would offer a modern AI environment.


Activating Tier 0 Storage Within GPU and CPU-based Compute Cluster with Hammerspace

Event: AI Infrastructure Field Day 3

Appearance: Hammerspace presents at AI Infrastructure Field Day 3

Company: Hammerspace

Video Links:

Personnel: Floyd Christofferson

The highest performing storage available today is an untapped resource within your server clusters that can be activated by Hammerspace to accelerate AI workloads and increase GPU utilization. This session covers how Hammerspace unifies local NVMe across server clusters as a protected, ultra-fast tier that is part of a unified global namespace. This underutilized capacity can now accelerate AI workloads as shared storage, with data automatically orchestrated by Hammerspace across other tiers and cloud storage to increase time to token while also reducing infrastructure costs.

Floyd Christopherson from Hammerspace introduces Tier 0, focusing on how it accelerates AI workflows in GPU and CPU-based clusters. The core problem addressed is the stranded capacity of local NVMe storage within servers, which, despite its speed, is often underutilized. Accessing data over the network to external storage becomes a bottleneck, especially in AI workflows with growing context lengths and fast token access requirements. While increasing network capacity is an option, it’s expensive and still limited. Tier 0 aggregates this local capacity into a single storage tier, making it the primary storage for workflows and enabling programmatic data orchestration, effectively unlocking petabytes of previously unused storage and eliminating the need to buy additional expensive Tier 1 storage.

Hammerspace’s Tier 0 leverages standards-based environments, with the client-side using standard NFS, SMB, and S3 protocols, eliminating the need for client-side software installations. The technology utilizes parallel NFS v4.2 with flex files, contributed to the Linux kernel, to enhance performance and efficiency. This approach avoids proprietary clients and special server deployments, allowing the system to work with existing infrastructure. The orchestration and unification of capacity across servers are key to the solution, turning compute nodes into storage servers without creating isolated islands, thereby reducing bottlenecks and improving data access speeds.

The presentation highlights the performance benefits of Tier 0, showcasing theoretical results and MLPerf benchmarks that demonstrate superior performance per rack unit. By utilizing local NVMe storage, Hammerspace reduces the reliance on expensive and slower cloud storage networks, leading to greater GPU utilization. Furthermore, Hammerspace contributes enhancements to the Linux kernel, such as local IO, to reduce CPU utilization and accelerate write performance, solidifying its commitment to standard-based solutions and continuous improvement in data accessibility. The architecture is designed to be non-disruptive, allowing for live data mobility behind the scenes, ensuring seamless user experience.


What is AI Ready Storage, with Hammerspace

Event: AI Infrastructure Field Day 3

Appearance: Hammerspace presents at AI Infrastructure Field Day 3

Company: Hammerspace

Video Links:

Personnel: Molly Presley

AI Ready Storage is data infrastructure designed to break down silos and give enterprises seamless, high-performance access to their data wherever it lives. With 73% of enterprise data trapped in silos and 87% of AI projects failing to reach production, the bottleneck isn’t GPUs—it’s data. Traditional environments suffer from visualization challenges, high costs, and data gravity that limits AI flexibility. Hammerspace simplifies the enterprise data estate by unifying silos into a single global namespace and providing instant access to data—without forklift upgrades—so organizations can accelerate AI success.

The presentation focused on leveraging existing infrastructure and data to make it AI-ready, emphasizing simplicity for AI researchers under pressure to deliver high-quality results quickly. Hammerspace simplifies the data readiness process, enabling easy access and utilization of data within infrastructure projects. While the presentation covers technical aspects, the emphasis remains on ease of deployment, workload management, and rapid time to results, aligning with customer priorities. Hammerspace provides a virtual data layer across existing infrastructure, creating a unified data namespace enabling access and mobilization of data across different storage systems, enriching metadata for AI workloads, and facilitating data sharing in collaborative environments.

Hammerspace addresses key AI use cases such as global collaboration, model training, and inferencing, particularly focusing on enterprise customers with existing data infrastructure they wish to leverage. The platform’s ability to assimilate metadata from diverse storage systems into a unified control plane allows for a single interface to data, managed through Hammerspace for I/O control and quality of service. By overcoming data gravity through intelligent data movement and leveraging Linux advancements, Hammerspace enables data access regardless of location, maximizing GPU utilization and reducing costs. This is achieved by focusing on data access, compliance, and governance, ensuring that AI projects align with business objectives and minimizing risks associated with data movement.

Hammerspace aims to unify diverse data sources, from edge data to existing storage systems, enabling seamless access for AI factories and competitive advantages through faster data insights. With enriched metadata and automated workflows, HammerSpace accelerates time to insight and removes manual processes. HammerSpace is available as installable software or as a hardware appliance, and supports various deployment models, offering linear scalability and distributed access to data. A “Tier 0” capability was also discussed, which leverages existing underutilized NVMe storage within GPU nodes to create a fast, low-latency storage pool, showcasing the platform’s flexibility and resourcefulness.


The AI Factory in Action: Basketball play classification with Hewlett Packard Enterprise

Event: AI Infrastructure Field Day 3

Appearance: HPE presents at AI Infrastructure Field Day 3

Company: HPE

Video Links:

Personnel: Mark Seither

This session provides a live demonstration of a practical AI application built on top of HPE Private Cloud AI (PCAI). The speaker, Mark Seither, showcases a basketball play classification application that leverages a machine learning model trained on PCAI. This model accurately recognizes and categorizes various basketball plays, such as pick and roll, isolation, and fast break. The demo highlights how the powerful and predictable infrastructure of PCAI enables the development and deployment of complex, real-world AI solutions. This example illustrates the full lifecycle of an AI project—from training to deployment—on a private cloud platform.

The presentation details the development of an AI application for an NBA team that focuses on video analysis, starting with the specific use case of identifying player fatigue. The initial approach involved using an open-source video classification model called Slow Fast, which was trained to recognize basketball plays such as pick and rolls, and isolations. To create a labeled dataset for training, the presenter manually extracted and labeled video clips from YouTube using tools like QuickTime and Label Studio. The model, trained on a small dataset of labeled plays, demonstrated promising accuracy in identifying these plays, and although it had limitations, the presentation illustrates a basic but functional model.

The speaker then discusses the next steps involving HPE’s Machine Learning Inferencing Service (MLIS) to deploy the model as an endpoint. This would allow the team to upload and classify video clips more easily. Furthermore, he plans to integrate the play classification with a video language model (VLM) enabling the team to query their video assets using natural language, such as “Show me every instance of Steph Curry running a pick and roll in the fourth quarter of a game in 2017.” He also showcased the RAG capabilities of the platform using the NBA collective bargaining agreement to answer specific questions, highlighting the platform’s potential to provide quick, valuable insights to customers.


The AI Factory: A strategic overview with Hewlett Packard Enterprise

Event: AI Infrastructure Field Day 3

Appearance: HPE presents at AI Infrastructure Field Day 3

Company: HPE

Video Links:

Personnel: Mark Seither

Many organizations find that their AI initiatives, despite early promise, fail to deliver a positive ROI. This can be traced to “token economics”—the complex and often unpredictable costs associated with consuming AI models, particularly in the public cloud. This session will dissect these hidden costs and the architectural bottlenecks that lead to runaway spending and stalled projects. We’ll then present a comprehensive overview of HPE Private Cloud AI, a full-stack, turnkey solution designed to provide predictable costs, superior performance, and total control. We will explore how its integrated hardware and software—from NVIDIA GPUs and HPE servers to a unified management console—enable a powerful and predictable path to production, turning AI from a financial gamble into a strategic business asset.

The presentation highlights the often-overlooked costs associated with AI initiatives in the public cloud, citing examples like over-provisioning, lack of checkpointing, and inefficient data usage. The speaker emphasizes that many companies experience significantly higher operational costs than initially anticipated, with one example of an oil and gas company spending ten times more than projected. While some companies may not be overly concerned with these cost overruns if the AI models deliver results, HPE contends that this isn’t sustainable for most organizations and that there are cost savings to be found.

HPE’s solution, Private Cloud AI, offers a predictable cost model and significant savings compared to cloud-based alternatives. These cost savings, averaging around 45%, are most pronounced with larger systems managed within the customer’s own data center, though co-location options are also available with slightly higher overhead. Furthermore, HPE’s solution addresses the hidden costs associated with building and managing an AI infrastructure from scratch, including the need for specialized teams and resources for each layer of the technology stack.

Beyond cost considerations, HPE’s Private Cloud AI provides greater control over data, mitigating concerns about data privacy and usage in downstream training cycles, which is important considering inquiries into the training data used for some AI models. The solution offers flexible purchasing options, including both CapEx and OpEx models, with HPE GreenLake enabling reserved capacity and on-demand access to additional resources without upfront costs. This combination of cost-effectiveness, control, and flexibility positions HPE Private Cloud AI as a compelling alternative to the public cloud for AI deployments.


The AI Chasm: Bridging the gap from pilot to production with Hewlett Packard Enterprise

Event: AI Infrastructure Field Day 3

Appearance: HPE presents at AI Infrastructure Field Day 3

Company: HPE

Video Links:

Personnel: Mark Seither

The AI market is booming with innovation, yet a significant and costly gap exists between the proof-of-concept phase and successful production deployment. A staggering number of AI projects fail to deliver on their promise, often stalling in “pilot purgatory” due to fragmented tools, unpredictable costs, and a lack of scalable infrastructure. In this session, we’ll examine why so many promising AI initiatives fall short and detail the key friction points—from data pipeline complexity and integration issues to governance and security concerns—that prevent organizations from translating AI ambition into measurable business value.

Mark Seither from HPE discusses the challenges organizations face in moving AI projects from pilot to production. He highlights the rapid pace of innovation in foundation models and AI services, making it difficult for companies to keep up and choose the right tools. A major concern is data security, with companies fearing data exposure when using AI models. The time and effort required to coordinate different teams and make decisions on building AI solutions also contributes to the delays.

Seither emphasizes that hardware alone is insufficient for successful AI implementation, and the conversation must center on business objectives. HPE offers a composable and extensible platform with a pre-validated stack of tools for data connectivity, analytics, workflow automation, and data science. Customers can also integrate their own preferred tools via Helm charts, though they are responsible for the lifecycle of those tools. The HPE platform is a co-engineered system with NVIDIA, meaning hardware choices are optimized for cost and performance and that the platform isn’t a reference architecture.

The HPE Data Lakehouse Gateway provides a single namespace for accessing and managing data assets, regardless of their location. HPE also has an Unleash AI program with validated ISV partners and supports NVIDIA Blueprints for end-to-end customizable reference architectures. Furthermore, HPE offers a private cloud solution with cost savings compared to public cloud alternatives, emphasizing faster time to value, complete control over security and data sovereignty, and predictable costs through both CapEx and OpEx models, including flexible capacity with GreenLake.


Your turnkey AI Factory for Rapid Development with Hewlett Packard Enterprise

Event: AI Infrastructure Field Day 3

Appearance: HPE presents at AI Infrastructure Field Day 3

Company: HPE

Video Links:

Personnel: Mark Seither

The vast majority of enterprise AI initiatives fail to deliver ROI, not because of a lack of innovation, but due to a significant gap between development and production. This session will explore the “token economics” behind these failures and introduce HPE Private Cloud AI, a turnkey AI factory designed to bridge this gap. We’ll show how this solution simplifies the journey from concept to full-scale deployment and demonstrate its power with a real-world use case: a powerful LLM built for the NBA, empowering you to drive measurable business value from your AI investments.

Mark Seither, Solutions Architect at HPE, introduced Private Cloud AI (PCAI), a turnkey AI factory designed to bridge the gap between AI development and production. PCAI is a fully integrated appliance comprised of HPE hardware, NVIDIA GPUs and switches, and HPE’s AI Essentials software, along with NVIDIA’s NVAI Enterprise (NVAIE). Seither emphasized that this is not a hastily assembled product but the result of long-term development, internal innovation, and strategic acquisitions, positioning PCAI as a unique and compelling solution in the AI market. He highlights the evolution of AI, noting that the current outcomes are so advanced that they are practically indistinguishable from what was once considered far-off science fiction, making it crucial for businesses to embrace and understand its potential.

The speaker also touched on the practical applications of AI, ranging from personalized product recommendations in retail to computer vision for threat detection and anomaly identification. He underscored a key trend he’s observing with his customers: the primary focus is not on replacing employees with AI but on enhancing their capabilities and improving customer experiences. Seither highlighted the challenges companies face in implementing AI, including a lack of enterprise AI strategies and difficulties in scaling AI projects from pilot to production. Data privacy, control, accessibility, and cost-effective deployment methodologies are also significant hurdles.

HPE’s PCAI aims to address these challenges by providing a ready-to-use solution that eliminates the need for companies to grapple with hardware selection, software integration, and driver compatibility. Offered in different “t-shirt” sizes, including a developer system, PCAI is designed to cater to various needs, from inferencing to fine-tuning. The goal is to empower data scientists to start working on AI projects from day one, focusing on differentiated work that directly impacts the business rather than on the complexities of setting up the AI infrastructure.


From Storage to Enterprise Intelligence, Unlock AI Value from Private Unstructured Data with CTERA

Event: AI Infrastructure Field Day 3

Appearance: CTERA presents at AI Infrastructure Field Day 3

Company: CTERA

Video Links:

Personnel: Aron Brand

Discover the obstacles that hinder AI adoption. What matters most? Data quality or quantity? Understand the strategy CTERA uses for curating data to create trustworthy, AI-ready datasets that overcome silos and security challenges, translating raw data into meaningful and actionable insights.

CTERA’s presentation at AI Infrastructure Field Day 3 focused on the transition from traditional storage solutions to “enterprise intelligence,” highlighting the potential of AI to unlock value from unstructured data. While enterprise GenAI represents a massive market opportunity, with projections reaching $401 billion annually by 2028, the speaker, Aron Brand, emphasized that current adoption is hindered by the poor quality of data being fed into AI models. Brand argued that simply pointing AI tools at existing data leads to “convincing nonsense,” as organizations often lack understanding of their own data, resulting in inaccurate and potentially harmful outputs. He identified three main “quality killers”: messy data, data silos, and compliance/security concerns.

To overcome these obstacles, CTERA proposes a strategy centered on data curation, involving several key steps. These include collecting data from various storage silos, unifying data formats, enriching metadata, filtering data based on rules and policies, and finally, vectorizing and indexing the data. CTERA aims to provide a platform that enables users to create high-quality datasets, enforce permissions and guardrails, and deliver precise context to AI tools. The platform is powered by an MCP server for orchestration and an MCP client for invoking external tools, facilitating an open and extensible system.

CTERA’s vision extends to “virtual employees” or subject matter experts created by users to automate tasks and improve efficiency. The system respects existing access controls and provides verifiable answers grounded in source data. The presented examples demonstrated the potential of the platform in various use cases, including legal research, news analysis, and medical diagnostics. The presentation emphasized that the goal is not to replace human workers but to augment their capabilities with AI-powered assistants that can access and analyze sensitive data in a secure and compliant manner.


Unlocking Enterprise Intelligence with CTERA MCP

Event: AI Infrastructure Field Day 3

Appearance: CTERA presents at AI Infrastructure Field Day 3

Company: CTERA

Video Links:

Personnel: Aron Brand

In this session, learn how the Model Context Protocol (MCP) tackles the challenges of utilizing unstructured data by providing seamless, permission-aware integration for AI models and data sources, eliminating the need for intricate custom connectors. Discover how this ‘USB for AI’ enables enterprise-wide data interaction and management, offering a reliable and future-proof architecture.

CTERA addresses the problem of connecting enterprise data sources to Generative AI models, which traditionally required custom connectors for each application, resulting in exponential complexity and fragility. The MCP protocol offers a solution by providing a seamless, guaranteed integration between any Gen AI model and tool that supports MCP, while also being permission and identity aware. It ensures contextual information about the user, their permissions, and authentication is readily available. CTERA has embraced MCP as a core part of its strategy, implementing it across its products.

CTERA’s implementation of MCP is structured in two main layers. The first layer resides within the global file system product, where files are stored, enabling Gen AI agents to access and utilize data while respecting user permissions. The second layer focuses on data intelligence, providing a semantic layer over the data that exposes textual information and metadata through MCP. The MCP server is implemented within the enterprise application, while the MCP client is the AI tool. This architecture is not specific to any LLM and supports OAuth2 authentication, allowing for secure and permissioned access to data.

A demonstration highlighted how CTERA’s MCP server could be easily enabled via the user interface, showcasing its integration with Claude. The demonstration showed how a user could instruct Claude to interact with the global file system, list files, read them, summarize them, and write the summary back, all without writing any code. This example illustrated how MCP enables end-to-end applications that democratize access to data and allow users to simplify repetitive tasks, thereby increasing efficiency and job satisfaction.


CTERA Enterprise Intelligence: Unify & Activate Your Private Data for Faster & Smarter Outcomes

Event: AI Infrastructure Field Day 3

Appearance: CTERA presents at AI Infrastructure Field Day 3

Company: CTERA

Video Links:

Personnel: Saimon Michelson

This session offers insight into the seamless integration of AI within the CTERA Intelligent Data Platform. Embedded AI and analytic Enterprise Data Services are explored, along with the underlying data fabric that facilitates secure, global data connectivity and ensures high-performance access. Additionally, the session demonstrates how agentic AI advances productivity, enabling virtual employees to interact efficiently with private data resources while maintaining robust security and operational effectiveness.

CTERA’s approach to enterprise intelligence is structured around three key pillars. The first pillar focuses on embedding AI and analytics within data management for enhanced security and data quality. This includes real-time inspection of I/O for anomaly detection and integration with security solutions like Purview. The second pillar involves providing a data foundation and fabric for AI training and inferencing, offering a global namespace for data aggregation and access, along with real-time metadata notifications and direct object access via a direct protocol that bypasses the CTERA portal.

The third pillar introduces agentic AI, enabling virtual employees to interact with private data resources efficiently. This pillar provides a semantic layer, allowing connections to various data sources (including non-CTERA systems), enrichment of metadata, and normalization of permissions across different data sources. This facilitates semantic searches and retrieval of relevant documents, empowering users with productivity-enhancing tools within a secure environment and answering questions with a chat-based interface.


CTERA Intelligent Data Management from Edge to Core

Event: AI Infrastructure Field Day 3

Appearance: CTERA presents at AI Infrastructure Field Day 3

Company: CTERA

Video Links:

Personnel: Saimon Michelson

Discover how CTERA addresses the complexities of hybrid cloud storage by enhancing operational efficiency and security, advocating a unified platform that extends from edge to cloud to manage increasing data demands. Practical use cases across various industries demonstrate how CTERA leverages AI-powered tools, automated workflows, and an intelligent data platform to improve data insights, security, and governance on a wide scale.

CTERA positions itself as a leader in distributed data management, providing a file system fabric that connects edge locations, core data centers, and cloud environments. Their platform allows for secure data access, protection, and sharing, forming the foundation for future AI applications. CTERA’s DNA comes from the security space and works with highly regulated industries such as healthcare, financial services, and government agencies. These organizations share common challenges of data cybersecurity consciousness, highly distributed data, and the need for information management.

CTERA addresses the increasing complexity of hybrid cloud storage by offering a platform that connects data centers, cloud providers, and edge offices. The company uses object storage as its backbone, providing reliability, durability, capacity, and cost-effectiveness. To overcome the limitations of centralized object storage, CTERA adds a complementary layer that caches data, provides performance, and enables multi-protocol access across various locations. The company modernizes unstructured data across operational efficiency, cyber storage and proactive data protection, productivity through multiprotocol access, and generative AI to improve productivity and security.

CTERA’s architecture consists of edge appliances that present file shares, optimize performance through caching, and provide real-time data protection to a centralized object store. This model enables near zero-minute disaster recovery and facilitates data sharing across geographically dispersed locations. CTERA simplifies data migration from traditional NAS platforms using built-in tools, allowing customers to move large datasets to CTERA-managed object storage, which can reside on-premise or in the cloud. The company excels in industries such as healthcare, public sector, retail, and manufacturing, where security, data distribution, and the need for modernization are critical.


Mirantis PaaS Technology Stack with Shaun O’Meara

Event: AI Infrastructure Field Day 3

Appearance: Mirantis presents at AI Infrastructure Field Day 3

Company: Mirantis

Video Links:

Personnel: Anjelica Ambrosio, Shaun O’Meara

Shaun O’Meara, CTO at Mirantis, described the platform services layer that sits above the GPU infrastructure and is delivered through Mirantis k0rdent AI. The PaaS stack is organized around composable service templates that let operators expose training, inference, and data services to tenants. Services can be chained, extended, and validated without requiring custom integration work for every new workload.

A central example in this segment was the use of NVIDIA’s Run.ai as the delivery platform for inference workloads. Anjelica Ambrosio demonstrated the workflow. She deployed an inference cluster template, selected GPU node profiles, and then added Run.ai services as part of the cluster composition. From the Mirantis k0rdent AI portal, she navigated into the Run.ai console to show inference jobs running against the GPU pool. The demonstration highlighted how Mirantis integrates Run.ai into its templated deployment model so that all dependencies, such as cert-manager, GPU operators, and Argo, are automatically provisioned. What would normally require a complex chain of manual installations was shown as a single cluster deployment taking about fifteen minutes on AWS, most of which was machine startup time.

O’Meara explained that the catalog approach lets operators bring in Run.ai alongside other frameworks like Kubeflow or MLflow depending on customer preference. The system labels GPU nodes during cluster creation, and Run.ai validates those labels to ensure that only GPU-backed nodes run GPU workloads while other tasks are placed on CPU nodes. This improves cost efficiency and prevents GPU starvation.

The PaaS stack makes GPU infrastructure usable in business terms. Enterprises can use the catalog internally to accelerate development or publish services externally for customers. Sovereign operators can keep the Run.ai-based services on local GPU hardware in air-gapped form, while hybrid operators can extend them across public and private GPU footprints. By integrating NVIDIA Run.ai directly into Mirantis k0rdent AI, the platform demonstrates how complex AI services can be delivered quickly, with governance and observability intact, and without the fragile manual integration that normally burdens GPU PaaS environments.


Mirantis IaaS Technology Stack with Shaun O’Meara

Event: AI Infrastructure Field Day 3

Appearance: Mirantis presents at AI Infrastructure Field Day 3

Company: Mirantis

Video Links:

Personnel: Anjelica Ambrosio, Shaun O’Meara

Shaun O’Meara, CTO at Mirantis, described the infrastructure layer that underpins Mirantis k0rdent AI. The IaaS stack is designed to manage bare metal, networking, and storage resources in a way that removes friction from GPU operations. It provides operators with a tested foundation where GPU servers can be rapidly added, tracked, and made available for higher level orchestration.

O’Meara emphasized that Mirantis has long experience operating infrastructure at scale. This history informed a design that automates many of the tasks that traditionally consume engineering time. The stack handles bare metal provisioning, integrates with heterogeneous server and network vendors, and applies governance for tenancy and workload isolation. It includes validated drivers for GPU hardware, which reduces the risk of incompatibility and lowers the time to get workloads running.

Anjelica Ambrosio demonstrated how the stack works in practice. She created a new GPU cluster through the Mirantis k0rdent AI interface, with the system automatically discovering hardware, configuring network overlays, and assigning compute resources. The demo illustrated how administrators can track GPU usage down to the device level, observing both allocation and health data in real time. What would normally involve manual integration of provisioning tools, firmware updates, and network templates was shown as a guided workflow completed in minutes.

O’Meara pointed out that the IaaS stack is not intended as a general-purpose cloud platform. It is narrowly focused on preparing infrastructure for GPU workloads and passing those resources upward into the PaaS layer. This focus reduces complexity but also introduces tradeoffs. Operators who need extensive support for legacy virtualization may need to run separate systems in parallel. However, for organizations intent on scaling AI, the IaaS layer provides a clear and efficient baseline.

By combining automation with vendor neutrality, the Mirantis approach reduces the number of unique integration points that operators must maintain. This lets smaller teams manage environments that previously demanded much larger staff. O’Meara concluded that the IaaS layer is what makes the higher levels of Mirantis k0rdent AI possible, giving enterprises a repeatable way to build secure, observable, and tenant-aware GPU foundations.


Mirantis Solution Approach: GPU Cloud in a Box with Shaun O’Meara

Event: AI Infrastructure Field Day 3

Appearance: Mirantis presents at AI Infrastructure Field Day 3

Company: Mirantis

Video Links:

Personnel: Anjelica Ambrosio, Shaun O’Meara

Shaun O’Meara, CTO at Mirantis, presented the company’s approach to simplifying GPU infrastructure with what he described as a “GPU Cloud in a Box.” The concept addresses operational bottlenecks that enterprises and service providers face when deploying GPU environments: fragmented technology stacks, resource scheduling difficulties, and lack of integrated observability. Rather than forcing customers to assemble and maintain a full hyperscaler-style AI platform, Mirantis packages a complete, production-ready system that can be deployed as a single solution and then scaled or customized as requirements evolve.

The design is centered on Mirantis k0rdent AI, a composable platform that converts racks of GPU servers into consumable services. Operators can partition GPU resources into tenant-aware allocations, apply policy-based access, and expose these resources through service catalogs aligned with existing cloud consumption models. Lifecycle automation for Kubernetes clusters, GPU-aware scheduling, and tenant isolation are embedded into the system, reducing the engineering burden that is typically required to make such environments reliable.

A live demonstration was presented by Anjelica Ambrosio, AI Developer Advocate. For the first demo, she reviewed the customers’ experience using the Product Builder. She showed how a user can log into the Mirantis k0rdent AI self-service portal and provision products with the Product Builder within minutes, selecting from preconfigured service templates. The demo included creating a new cluster product, setting parameters, and deploying the product to the marketplace. Real-time observability dashboards displayed GPU utilization, job performance, and service health. The demonstration highlighted how the platform turns what was once a multi-week manual integration process into a repeatable and governed workflow. The next demo Anjelica presented was the Product Builder from the Operator’s experience, showing how products can be created using nodes and configuring dependencies with Graph View.

O’Meara explained that the “Cloud in a Box” model is not a closed appliance but a composable building block. It can be deployed in a data center, at an edge location, or within a hybrid model where a public cloud-hosted control plane manages distributed GPU nodes. Customers can adopt the system incrementally, beginning with internal workloads and later extending services to external markets or partners. This flexibility is particularly important for organizations pursuing sovereign cloud strategies, where speed of deployment, transparent governance, and monetization are essential.

The value is both technical and commercial. Technically, operators gain a validated baseline architecture that reduces common failure modes and accelerates time-to-service. Commercially, they can monetize GPU investments by offering consumption-based services that resemble hyperscaler offerings without requiring the same level of capital investment or staffing. O’Meara positioned the solution as a direct response to the core challenge confronting enterprises and service providers: transforming expensive GPU hardware into sustainable and revenue-generating AI infrastructure.


Mirantis Company Overview

Event: AI Infrastructure Field Day 3

Appearance: Mirantis presents at AI Infrastructure Field Day 3

Company: Mirantis

Video Links:

Personnel: Kevin Kamel, Shaun O’Meara

Kevin Kamel, VP of Product Management at Mirantis, opened with a wide-ranging overview of the company’s heritage, its evolution, and its current mission to redefine enterprise AI infrastructure. Mirantis began as a private cloud pioneer, gained deep expertise operating some of the world’s largest clouds, and later played a formative role in advancing cloud-native technologies, including early stewardship of Kubernetes and acquisitions such as Docker Enterprise and Lens. Today, Mirantis leverages this pedigree to address the pressing complexity of building and operating GPU-accelerated AI infrastructure at scale.
Kamel highlighted three key challenges driving market demand: the difficulty of transforming single-tenant GPU hardware into multi-tenant services; the talent drain that leaves enterprises and cloud providers without the expertise to operationalize these environments; and the rising expectation among customers for hyperscaler-style experiences, including self-service portals, integrated observability, and efficient resource monetization. Against this backdrop, Mirantis positions its Mirantis k0rdent AI platform as a turnkey solution that enables public clouds, private clouds, and sovereign “NeoClouds” to operationalize and monetize GPU resources quickly.

What sets Mirantis apart, Kamel emphasized, is its composable architecture. Rather than locking customers into vertically integrated stacks, Mirantis k0rdent AI provides configurable building blocks and a service catalog that allows operators to design bespoke offerings—such as proprietary training or inference services—while maintaining efficiency through features like configuration reconciliation and validated GPU support. Customers can launch services internally, expose them to external markets, or blend both models using hybrid deployment approaches that include a unique public-cloud-hosted control plane.

The section also introduced Nebul, a sovereign AI cloud in the Netherlands, as a case study. Nebul initially struggled with the technical sprawl of standing up GPU services—managing thousands of Kubernetes clusters, enforcing strict multi-tenancy, and avoiding stranded GPU resources. By adopting Mirantis k0rdent AI, Nebul streamlined cluster lifecycle management, enforced tenant isolation, and gained automation capabilities that allowed its small technical team to focus on business growth rather than infrastructure firefighting.

Finally, Kamel discussed flexible pricing models (OPEX consumption-based and CAPEX-aligned licensing), Mirantis’ ability to support highly regulated environments with FedRAMP and air-gapped deployments, and its in-house professional services team that can deliver managed services or bridge skills gaps. He drew parallels to the early OpenStack era, where enterprises faced similar knowledge gaps and relied on Mirantis to deliver production-grade private clouds. That same depth of expertise, combined with long-standing open source and ecosystem relationships, underpins Mirantis’ differentiation in today’s AI infrastructure market.


« Previous Page
Next Page »