AI

August 11, 2025 by George Crump

In his recent blog, “Edge AI and IoT: AI’s Hidden Infrastructure Problem”, Keith Townsend (@CTOAdvisor) explains why edge AI fails. The low success rate is not due to the models themselves. It is due to the fragmented layers of firmware, drivers, and operating environments that underlie them. As Townsend points out, this infrastructure stack creates complexity, spread across diverse hardware at multiple sites. It leads to an operational environment where drift is inevitable, upgrades are inconsistent, and performance is unpredictable. Without a disciplined approach to managing these stacks, edge AI pilot projects rarely transition into stable, scalable production deployments.

VergeIQ, Private AI for Core Data Centers and the Edge

That’s the exact problem VergeIQ is built to solve. VergeIQ is a private, enterprise-class service that provides a complete AI pipeline. It delivers everything from data ingestion and preparation to model training, inference, and lifecycle management.

VergeIQ is embedded directly into VergeOS. It benefits from an infrastructure platform that integrates virtualization, storage, networking, and now AI into a single, cohesive operating environment. It eliminates the multiple, disconnected layers that create drift and operational friction. This unified design allows AI workloads to operate in the same secure, version-controlled environment as other critical enterprise applications.

Edge AI Requires Centralized, Integrated Infrastructure

In VergeOS, AI is a service, like file services: you simply turn it on. There’s no need to provision VMs, deploy containers, or manage separate orchestration layers.

Because AI capabilities are native to VergeOS, IT can provision Virtual Data Centers (VDCs) as siloed AI environments. Each VDC operates with its own isolated compute, storage, and networking resources. The AI “service” can be assigned to whichever VDCs need it. VDCs enable predictable AI performance and security without interfering with other workloads.

Lack of single point of management is one reason why edge AI fails

At the recent Future of Memory and Storage (formerly Flash Memory Summit) event, the VergeOS architecture enabled us to set up three separate AI environments in under an hour. This install time included physical setup, power-on, and connectivity verification. These environments ran as private, self-contained edge AI deployments, without relying on the show’s network. The result is an operational model where AI deployments are as fast to launch as they are secure and repeatable.

Real-Time Inventory and Observability

One of the reasons edge AI fails is that IT struggles to maintain accurate visibility into what is running where. Unknown infrastructure stacks are unmanageable, and without complete telemetry, infrastructure teams are blind to drift until it causes failures. VergeOS addresses this problem with ioMetrics. It captures real-time data about hardware configurations, firmware and driver versions, and operating system builds. This comprehensive view enables the immediate detection of deviations, ensuring that every edge environment remains in a known, validated state.

For organizations managing dozens—or even hundreds—of remote AI deployments, VergeOS’s Sites Dashboard extends that visibility into operational control. Sites Dashboard provides a single, centralized interface for monitoring and managing all VergeOS-powered edge locations in real-time. Platform teams can apply updates, adjust configurations, enforce security policies, and spin up or tear down environments across the entire deployment footprint without needing to send personnel on-site.

Edge AI Requires Secure, Unified Deployment

Security is another reason why edge AI fails. At the edge, physical access to devices and diverse deployment locations create an expanded attack surface. VergeIQ enforces secure boot processes, validates firmware integrity, and uses signed binaries for all components in the infrastructure stack. Each VDC is treated as an immutable artifact that has been validated in staging before being rolled out to production. If an update introduces instability, built-in rollback capabilities allow teams to revert to a known good state with minimal disruption. Because VergeOS integrates AI, it eliminates the need for separate orchestration layers or container clusters. The result is faster time-to-value and a reduced operational burden for platform teams.

Edge AI Requires Vendor-Neutral Acceleration

A common trade-off in edge AI is the choice between predictability and portability. Vendor-integrated stacks, such as those tied to a specific GPU vendor, can simplify lifecycle management. However, they introduce long-term lock-in, creating another reason why edge AI fails. VergeIQ supports heterogeneous accelerators, including NVIDIA, AMD, and other specialized processors—without compromising the ability to manage them consistently. Resource orchestration, clustering, and pooling are handled by VergeOS, allowing AI workloads to run optimally across almost any hardware mix. VergeOS flexibility enables organizations to design hardware strategies that align with business needs, rather than adhering to the vendor’s roadmap.

Abstraction Without Losing Control

Hardware standardization is not always possible in edge environments. The edge must adapt to local constraints, legacy equipment, or specific workload requirements. VergeIQ provides a uniform abstraction layer over diverse hardware, ensuring that AI behaves predictably regardless of the underlying platform. This is not an abstraction for its own sake. It is grounded in a lifecycle-managed infrastructure stack that is versioned, tested, and enforced across the entire deployment footprint. By controlling the infrastructure stack while abstracting its differences, VergeIQ enables both operational consistency and hardware flexibility. StorageSwiss explores the value of this kind of integrated approach to infrastructure in its article, “Why Hyperconverged Infrastructure Needs More Than Just Compute and Storage.”

Why VergeIQ Delivers Where Others Struggle

VergeIQ embodies the principle that infrastructure discipline must come before orchestration. By unifying AI workloads with the same Infrastructure Platform that runs enterprise applications, IT:

Standardizes and collapses the infrastructure stack
Maintains real-time observability
Secures the entire lifecycle
Enables portable acceleration strategies

These outcomes transform edge AI from a fragile, site-by-site experiment into a predictable, centrally managed platform that can scale without operational chaos.

See VergeIQ in action.
Join our webinar, “Introducing VergeIQ – Enterprise AI Infrastructure”, to learn how you can simplify, secure, and scale your AI deployments from edge to core.
Register here.

Filed Under: AI Tagged With: AI, Artificial Intelligence, Edge

June 27, 2025 by George Crump

VMware’s pricing changes, cloud cost overruns, or the AI skills shortage are symptoms of the infrastructure problem: legacy environments that demand new layers of complexity with every new initiative. Each shift in strategy—whether it’s migrating to the cloud, deploying AI workloads, or navigating vendor transitions—exposes just how fragile and fragmented traditional architectures have become. IT teams are forced to bolt on new platforms, hire niche expertise, or overprovision resources just to keep up. The result isn’t innovation—it’s operational drag. It’s time to rethink infrastructure from the ground up.

Watch our on-demand industry briefing with ESG to learn the impact of these challenges and how to solve them

According to recent (May 2025) ESG research titled “Private AI, Virtualization, and Cloud: Transforming the Future of Infrastructure Modernization,” a survey of 380 mid-sized to large data centers, organizations everywhere are scrambling to address these top challenges:

VMware’s acquisition by Broadcom has created uncertainty for the 80% of enterprises relying on their virtualization platform.
Simultaneously, 75% of organizations are rethinking their cloud strategies as costs spiral beyond projections.
And 53% of organizations plan to deploy private, on-premises AI infrastructure within the next two years, but 70% struggle to find qualified staff to manage increasingly complex infrastructure environments.

IT leaders are approaching these as separate crises requiring individual solutions. But what if there’s a single root cause driving all these problems?

The Symptoms of the Infrastructure Problem

The evidence of the infrastructure problem is everywhere:

VMware’s disruption has left organizations hunting for hypervisor alternatives, only to discover that most options require new skill sets and architectural approaches.
Cloud repatriation is accelerating as the economics of long-term workloads in public cloud environments prove unsustainable—what promised operational flexibility has become a financial burden.
AI adoption is stalling because implementing on-premises AI is a must-have to mine proprietary data, but vendors are suggesting it requires building separate infrastructure stacks with specialized hardware, networking, and storage.
Talent acquisition has become nearly impossible as the complexity of managing modern infrastructure outpaces the available skill pool.

These issues dominate IT planning discussions, budget meetings, and strategic reviews. However, focusing on symptoms instead of causes leads to fragmented solutions that exacerbate the underlying problem.

Software is the Source of the Infrastructure Problem

The source of the infrastructure problem isn’t any single vendor, technology, or market force. It’s the acceptance of fundamentally flawed infrastructure software that forces fragmentation by design.

For over two decades, the industry has normalized building data centers by assembling disconnected components—hypervisors that require separate storage systems, networking hardware that needs additional security appliances, backup solutions that demand their own management consoles, and now AI platforms that require new and again isolated stacks.

This fragmented approach creates four compounding problems:

Hardware Vendor Lock-In: Traditional infrastructure software ties organizations to proprietary hardware ecosystems. Storage controllers costing 10X what they should, certified network switches, rigid hardware compatibility lists—all designed to extract maximum revenue rather than deliver maximum value.

Operational Silos: Every new initiative spawns its own infrastructure requirements. Virtualization teams, storage specialists, network engineers, backup administrators, and now AI infrastructure experts—each managing separate tools, consoles, and technologies that barely communicate with each other.

The Add-On Trap: Poor infrastructure software creates gaps that must be filled with additional vendor solutions. What starts as “adding backup capabilities” becomes an ecosystem of interconnected products, each requiring its own licensing, hardware, support contracts, and specialized expertise.

Complexity Explosion: The staffing crisis isn’t just about finding qualified people—it’s about the exponential complexity created when organizations need specialists for every infrastructure domain, plus the integration expertise to make them work together.

How to Solve the Infrastructure Problem

Solving the infrastructure problem becomes possible when infrastructure software is designed correctly from the ground up. VergeOS demonstrates this approach by integrating virtualization, storage, networking, and AI capabilities into a single codebase, creating a unified platform.

Instead of assembling separate components, organizations get unified functionality that eliminates vendor lock-in, operational silos, add-on complexity, and excessive staffing requirements while leveraging existing hardware. A single platform addresses what organizations currently treat as separate problems: VMware alternatives, cloud cost optimization, AI infrastructure deployment, and skills shortage mitigation.

This isn’t theoretical—it’s happening today. Read our case studies to learn how organizations using VergeOS report reducing infrastructure costs, in some cases, by over 90%, eliminating multiple vendor relationships, and enabling single administrators to manage entire infrastructure stacks that previously required specialized teams.

The Path Forward

The infrastructure challenges consuming your planning cycles aren’t inevitable. They’re the predictable result of accepting software that forces fragmentation rather than enabling consolidation.

VMware disruption, cloud cost overruns, AI deployment complexity, and skills shortages are symptoms of a deeper architectural problem. Addressing symptoms individually—such as finding new hypervisors, optimizing cloud spend, building AI infrastructure, and hiring more specialists—treats the effects while leaving the cause untouched.

The solution requires recognizing that modern infrastructure demands modern architecture. Software that natively integrates all infrastructure functions. Platforms that work with commodity hardware rather than forcing proprietary purchases. Systems that simplify rather than complicate operations.

Organizations that recognize this shift now will gain significant advantages over those that focus on treating symptoms instead of addressing the underlying problem.

To learn more, download our white paper, “Four Forces Accelerating Infrastructure Modernization.”

Filed Under: Virtualization Tagged With: AI, Alternative, Cloud, IT infrastructure, VMware

June 11, 2025 by George Crump

Eliminating enterprise AI deployment barriers has become critical, as 70% of enterprise AI projects fail due to infrastructure complexity. However, organizations cannot afford to delay private AI adoption in today’s competitive landscape. As we detailed in our recent Blocks and Files analysis, traditional enterprise AI solutions create significant barriers that prevent broader adoption. These roadblocks—from infrastructure complexity to hardware lock-in—have limited private AI deployment to the largest corporations, which have the resources and expertise to overcome them.

VergeOS, with integrated VergeIQ, directly addresses every identified barrier through a fundamentally different approach: treating AI as a native infrastructure resource rather than a separate technology stack. By integrating generative AI capabilities directly into the unified data center operating system, VergeOS successfully eliminates the enterprise AI deployment barriers that have historically prevented mid-sized enterprises from deploying private AI.

Eliminate Enterprise AI Complexity Through True Integration

Traditional AI deployments require managing multiple software layers—such as containers, Kubernetes, orchestration platforms, and specialized AI frameworks—each adding complexity and requiring dedicated expertise. Unlike bolt-on AI solutions that create additional management overhead, VergeIQ operates as a native VergeOS service, eliminating the operational complexity of managing separate AI infrastructure.

The result is dramatic simplification: instead of requiring specialized AI infrastructure expertise, organizations can deploy and manage enterprise AI using existing IT skills and established operational procedures. This approach represents a fundamental advancement in eliminating enterprise AI deployment barriers through architectural convergence.

Built-In Capabilities Replace Separate Installations

Once VergeOS is installed, VergeIQ is immediately available as a native resource alongside virtualization (VergeHV), storage (VergeFS), and networking (VergeFabric). Organizations can deploy and utilize popular large language models like LLaMa, Qwen, Phi, and Gemma within minutes, without requiring additional software installations or complex configurations. This integration means IT teams manage AI workloads using the same unified interface they use for all infrastructure functions.

Eliminating Enterprise AI Deployment Barriers — Install from a Curated List of Models

VergeIQ includes comprehensive generative AI capabilities as part of the base VergeOS installation. Organizations don’t need to purchase, install, or integrate separate AI platforms, eliminating both the software licensing costs and integration complexity that plague traditional approaches.

Immediate and Practical Enterprise AI Value

Day one capabilities include document analysis of PDFs, spreadsheets, and text files; secure auditing and optimization of proprietary source code; automated infrastructure script generation; tailored enterprise content creation; and comprehensive infrastructure intelligence. All capabilities are available immediately upon deployment of VergeOS.

Additionally, VergeIQ enables experimentation without token-based pricing penalties. Organizations can set up secure, isolated virtual labs for testing and validation without requiring dedicated GPU resources, accelerating innovation while reducing operational risk.

This built-in approach ensures seamless compatibility and performance optimization, as VergeIQ is explicitly designed for the VergeOS environment rather than being bolted onto existing infrastructure.

Single Storage System Handles All Enterprise AI and Business Workloads

Traditional AI deployments create storage complexity by requiring separate, specialized storage systems for different workload types—high-performance storage for training, medium-performance for inference, and archival storage for long-term data retention. This specialization creates significant infrastructure duplication and operational overhead.

VergeFS, VergeOS’s integrated software-defined storage, provides unified storage that handles all AI workload requirements within a single system. The intelligent tiering capabilities automatically optimize data placement based on access patterns and performance requirements, eliminating the need for separate storage infrastructures.

Organizations can leverage their existing storage investments while accommodating AI requirements, dramatically reducing both capital expenses and operational complexity. VergeFS scales seamlessly from initial AI pilots to full production deployments without requiring architectural changes or additional storage systems.

Vendor-Neutral Hardware Approach Prevents Lock-In

VergeIQ provides complete vendor neutrality for compute hardware, supporting GPUs from multiple vendors or functioning on CPU-based systems. This flexibility ensures organizations aren’t locked into specific hardware vendors or dependent on GPU availability for AI functionality.

The platform features intelligent GPU orchestration that maximizes hardware efficiency across all vendors, while CPU-based AI capabilities ensure continued operation even when GPU resources are unavailable. Organizations can start with existing hardware and add GPU acceleration as needed, or change GPU vendors without architectural disruption.

This approach protects organizations against the rapid changes in AI hardware markets, allowing them to adopt new technologies as they emerge without being constrained by their initial infrastructure choices. Vendor neutrality is essential for eliminating enterprise AI deployment barriers that create long-term technological dependencies.

Enterprise AI Security Without Complexity

VergeOS includes comprehensive security features as part of its firmware-style operating environment. These built-in capabilities include network segmentation through VergeFabric, end-to-end data encryption, secure authentication systems, comprehensive audit logging, and role-based access controls.

For AI workloads, this means sensitive enterprise data remains secure within organizational boundaries without requiring additional security appliances or complex configurations. The integrated security model ensures that AI deployments meet regulatory compliance requirements while maintaining the operational simplicity that makes private AI practical for organizations of all sizes.

Unlike traditional approaches that require layering security solutions on top of AI platforms, VergeOS provides enterprise-grade security as a fundamental platform characteristic.

Broader IT Problem Resolution

VergeOS, combined with VergeIQ, addresses multiple critical IT challenges simultaneously. For organizations evaluating VMware alternatives due to Broadcom’s pricing and licensing changes, VergeOS offers more than an alternative; it modernizes the entire infrastructure without requiring hardware replacement.

The same installation that replaces VMware infrastructure provides comprehensive generative AI capabilities, storage modernization through VergeFS, and advanced networking through VergeFabric. This unified approach maximizes organizational value while minimizing the complexity of managing multiple infrastructure solutions. Log in to our case studies library to learn how the transition has gone for our customers.

Alternatively, organizations can deploy VergeOS alongside existing VMware infrastructure to immediately gain AI capabilities, then transition away from VMware when timing aligns with their broader infrastructure strategy. This unified approach is central to eliminating enterprise AI deployment barriers while addressing broader requirements for infrastructure modernization.

Implementation Path for Enterprise AI Success

VergeOS with VergeIQ removes the traditional barriers that have prevented broader enterprise AI adoption. By treating AI as a native infrastructure resource, organizations gain immediate access to powerful generative AI capabilities without the complexity, cost, and operational overhead of traditional approaches.

The platform’s vendor-neutral approach, integrated security, unified storage, and immediate value delivery create a practical path for organizations to deploy private AI that meets enterprise requirements while remaining operationally manageable.

Organizations can start with pilot deployments using existing hardware, validate business value, and then scale confidently in their architectural choices, eliminating the typical enterprise AI risk of significant upfront investments with uncertain outcomes.

For organizations seeking to leverage the transformational potential of private AI without the traditional deployment barriers, VergeOS with VergeIQ provides a comprehensive solution that makes enterprise AI practical, secure, and immediately valuable.

See VergeIQ in Action: Live Demonstration Tomorrow

Ready to see how VergeOS with VergeIQ succeeds in eliminating enterprise AI deployment barriers in real-time? Join us tomorrow, Thursday, June 12th at 1:00 PM ET, for our world-premier VergeIQ webinar and demonstration.

Watch our product experts showcase how VergeIQ delivers enterprise-ready AI deployments in minutes, not months. You’ll see live demonstrations of curated Large Language Models, such as LLaMa, Falcon, and DeepSeek, running with near-bare-metal performance, GPU pooling and clustering capabilities, and disconnected, sovereign AI solutions.

This comprehensive demonstration will show how VergeIQ transforms private AI from a complex, resource-intensive challenge into a simple, immediate infrastructure capability.

Register now for tomorrow’s live demonstration and discover how VergeOS with VergeIQ can deliver immediate AI value within your existing infrastructure strategy.

Filed Under: AI Tagged With: AI, Enterprise AI

June 5, 2025 by George Crump

Using existing frameworks that enable the creation and use of AI agents—that’s the core benefit of VergeIQ’s OpenAI-compatible service. With this capability, VergeIQ, makes enterprise-grade generative AI secure, manageable, and easily accessible—not just for developers, but for anyone who uses familiar, off-the-shelf AI software tools such as VS Code, Continue, and Anything LLM.

Fully integrated within VergeOS—the comprehensive data center operating system and leading VMware alternative—VergeIQ delivers immediate, seamless access to powerful generative AI capabilities without additional setup. Once VergeOS is installed, VergeIQ is ready to go. Enterprises can quickly deploy secure, locally hosted large language models (LLMs), allocate GPU resources dynamically, and interact with sensitive internal data entirely within their own data centers. With VergeOS, AI is on-premises, private, and secure.

VergeIQ’s OpenAI-compatible service Enables Familiar Tools

By providing an OpenAI-compatible router or service, VergeIQ removes the typical hurdles to private AI adoption. Users can seamlessly migrate their workflows to VergeIQ. No new tools, retraining, or significant code changes are needed—simply point your existing applications to VergeIQ’s internal API endpoint and begin working.

For business analysts, content creators, support specialists, and IT teams, this means quickly integrating powerful generative AI into everyday workflows without any steep learning curves.

Developers already familiar with OpenAI’s libraries and documentation can start building applications on VergeIQ without having to learn a new interface or rewrite their code. The only change required is pointing applications to a new, internal API endpoint.

If you don’t use any of these tools, then don’t worry, VergeOS with VergeIQ includes everything you need to leverage AI to understand your data and create new content.

Complete On-Premises Security and Control

VergeIQ’s OpenAI-compatible service ensures your sensitive data never leaves your environment. Unlike cloud-based AI services that transmit data externally, VergeIQ operates entirely within your premises. This complete on-premises deployment capability allows enterprises to run fully disconnected—no internet or cloud connectivity required—ensuring absolute control, full regulatory compliance, and secure management of confidential data.

On-premises operations not only enhance privacy but also dramatically reduce latency, providing real-time responses and faster insights.

Behind-the-Scenes Intelligence, Effortless Use

Underneath the familiar OpenAI-compatible interface, VergeOS intelligently manages all AI operations, including GPU orchestration, automatic model loading, resource allocation, and infrastructure optimization. Administrators can rely on VergeOS to dynamically scale resources, minimize manual intervention, and maximize performance without the complexities typically associated with on-premises AI deployments.

The result is enterprise-grade AI that’s easy to manage for IT teams, providing cloud-like simplicity with the full security and control of local infrastructure.

VergeIQ’s OpenAI-Compatible Service Enables Predictable Costs and Unlimited Usage

Unlike public cloud AI models that impose ongoing per-token or subscription fees, VergeIQ delivers predictable, flat-rate costs as part of VergeOS. Organizations aren’t penalized as their AI adoption grows. Enterprises can scale their AI use internally without escalating expenses, ensuring sustainable growth, cost-effective operations, and predictable budgeting.

Local AI Infrastructure with Cloud Convenience

VergeIQ’s OpenAI-compatible service offers a best-of-both-worlds approach, combining the security and privacy of fully private, on-premises infrastructure with the simplicity and familiarity of cloud-based AI interfaces. VergeOS provides enterprises a trusted, secure, fully controlled AI environment that’s immediately accessible and easy to use.

With VergeIQ, organizations gain rapid, secure AI capabilities without sacrificing convenience, compatibility, or performance.

Examples of Tools Compatible with VergeIQ’s OpenAI-Compatible Service

Category	Tool	Description
Desktop Applications	LM Studio	Local AI model runner with OpenAI-compatible API.
	Ollama	CLI tool for local model deployment with built-in API server.
	GPT4All	Desktop application connecting to various local or remote backends.
	Jan	Open-source ChatGPT alternative with API integration.
	AnythingLLM	Application for document-based chat and AI model management.
Web Interfaces	Open WebUI	Web interface for managing OpenAI-compatible API models.
	ChatGPT Next Web	Self-hostable, open-source alternative to ChatGPT.
	LibreChat	Open-source ChatGPT alternative supporting multiple API providers.
	Chatbot UI	Minimalist web-based AI interface supporting various APIs.
Development Tools	Continue.dev	VS Code/JetBrains extension providing AI-powered coding assistance.
	Cursor	AI-powered code editor configurable with custom API endpoints.
	Aider	Command-line coding assistant leveraging AI.
	OpenAI SDKs (Python/Node.js)	Official libraries compatible with VergeIQ’s API endpoint.
Mobile Applications	Mela (iOS)	Mobile chat application supporting custom API endpoints.
	AI Chat (Android)	Android AI chat apps configurable with custom APIs.
Browser Extensions	ChatGPT Box	Browser extension allowing custom API endpoint configuration.
	WebChatGPT	Extension configurable to various API providers.
Command-Line Tools	llm (by Simon Willison)	CLI tool for interacting with AI models using custom endpoints.
	chatgpt-cli	CLI implementations supporting interaction via custom APIs.

Beyond AI: Infrastructure Observability with ioMetrics

The openness of VergeOS extends beyond its AI capabilities. VergeOS includes ioMetrics, a powerful observability and monitoring solution built directly into the platform. ioMetrics enables IT teams to collect real-time data on infrastructure performance, usage patterns, resource allocation, and more.

With ioMetrics, administrators can:

Monitor Infrastructure Performance: Track the performance and utilization of CPUs, GPUs, memory, storage, and networking resources within your data center.
Analyze Resource Trends: Identify trends and usage patterns to optimize resource allocation and predict future infrastructure needs.
Proactively Address Issues: Detect potential bottlenecks or performance issues before they impact users, reducing downtime and maintaining high availability.
Leverage Open Standards: Integrate seamlessly with industry-standard observability tools like Grafana, Prometheus, and other monitoring dashboards.

By combining ioMetrics with VergeIQ’s AI capabilities, organizations can take infrastructure management to another level—using AI-driven analytics and actionable insights to improve decision-making, operational efficiency, and service reliability.

Filed Under: AI Tagged With: AI, VergeIQ