By Brigham Mirabelli, Senior Solutions Engineer, Structured —

At Dell Technologies World this year, one idea surfaced in nearly every keynote, breakout session, and technical conversation I attended: AI is quickly moving from experimentation to operational reality.

Beyond the announcements about hyperscale GPU clusters and massive AI factories, one trend stood out in the hands-on NVIDIA NemoClaw labs I joined:

Enterprise AI is beginning to decentralize.

We are entering the era of deskside AI, where developers, engineers, analysts, and business teams can fine-tune, test, and run AI models locally on compact systems like the Dell Pro Max with NVIDIA GB10 instead of waiting on centralized AI teams or hyperscaler infrastructure.

That shift has major implications for how organizations innovate with AI.

Why You Should Look Beyond Cloud-Only AI

One of the biggest themes at Dell Technologies World was not just how powerful AI models are becoming, but the growing recognition that not every AI workload should run through centralized, cloud-hosted foundation models.

As organizations move from traditional generative AI to more advanced agentic workflows, the economics and operating model change quickly.

Traditional chatbot interactions are relatively lightweight:

• A user submits a prompt
• The model generates a response
• The interaction ends

Agentic AI systems work very differently. Modern AI agents can:

• Continuously reason through tasks
• Execute actions across systems
• Maintain long-running context
• Read files and telemetry
• Retry failed operations
• Chain together multi-step workflows autonomously

That dramatically increases token consumption, API utilization, continuous inferencing requirements, and overall cloud AI spend. At enterprise scale, routing these workloads through hosted-model APIs can become expensive, harder to govern, and more difficult to align with data control requirements.

That is one reason localized AI platforms like the Dell Pro Max with NVIDIA GB10 are drawing so much attention. Running models locally creates several advantages:

• Reduced dependency on external API consumption
• Lower long-term operational AI costs
• Faster localized inferencing
• Greater control over proprietary data
• Improved privacy and DLP alignment
• The ability to operationalize AI even in restricted environments

For most organizations, this is not about replacing centralized AI infrastructure. It is about deciding which workloads belong in hyperscale environments and which are better run privately and locally, closer to users, data, and operational systems.

The Shift from Chatbots to Agentic AI

One of the most useful concepts from the NemoClaw labs was the distinction between traditional chatbot workloads and what Dell and NVIDIA described as the emerging agentic era of AI. As AI workflows become more autonomous, the infrastructure requirements change with them.

One of the biggest takeaways from the labs was seeing these agentic workflows run locally on the Dell Pro Max with NVIDIA GB10. Instead of behaving like a simple chatbot, the AI agent acted more like an operational engineer:

• Investigating infrastructure issues
• Correlating telemetry and logs
• Executing remediation steps
• Reporting operational impact
• Iterating until issues were resolved

That is a fundamentally different workload from traditional conversational AI. It also helps explain why localized AI infrastructure matters as organizations move beyond basic generative AI use cases.

The Rise of Deskside AI

For years, enterprise AI initiatives have largely depended on centralized infrastructure:

• Shared GPU clusters
• Dedicated AI engineering teams
• Long provisioning cycles
• Governance bottlenecks
• Queue-based access to compute resources

That model still makes sense for large-scale production training, but it often slows experimentation and limits how quickly teams can move ideas into production.

What I saw at Dell Technologies World was a different model taking shape, one that brings AI development closer to the people doing the work.

The Dell Pro Max with NVIDIA GB10 demonstrated exactly that. The GB10 architecture delivers:

• 128GB of unified shared memory between CPU and GPU
• High-performance local AI processing
• Localized model fine-tuning and inferencing
• Enterprise AI development in a deskside footprint

Instead of submitting requests to a centralized AI team, users can experiment with models, build workflows, and operationalize ideas much faster. In many ways, this feels like earlier shifts in virtualization and cloud-native development: teams closest to the business problem can move faster and iterate more independently.

AI Development Moves Closer to the User

One of the most compelling demonstrations paired the Dell GB10 with Reachy, an interactive robotics platform running localized AI workloads directly from the deskside system. What made the demo especially interesting wasn’t just the robotics itself — it was how the AI workflow was executed.

Using the GB10 platform, the system locally trained and ran a YOLO-based computer vision model that could recognize individuals in real time. During the demo, it identified specific users and displayed their names as they approached the system.

Work that would traditionally require centralized GPU infrastructure or cloud-based AI resources was happening directly on a compact deskside platform.

That shift matters.

It shows how AI development is becoming more accessible to engineers, developers, and enterprise teams without constant dependence on shared AI clusters or hyperscaler infrastructure.

The ability to:

• Train localized models
• Fine-tune AI workflows rapidly
• Run inference directly at the edge
• Iterate in real time

…creates a much faster path from experimentation to operational value.

While the Reachy demonstration was visually engaging, the broader implication is more important: Organizations can bring sophisticated AI workflows closer to users, devices, and business operations.

Fine-Tuning Enterprise AI Locally

Another NemoClaw lab focused on training and fine-tuning a support-oriented language model locally on the GB10 platform.

The use case addressed a challenge many organizations are actively trying to solve today:

How do you make AI useful with your organization’s internal knowledge?

In the lab, we trained and fine-tuned a technical support chatbot using enterprise documentation and operational knowledge. Once optimized, the model could be deployed broadly to endpoint systems where inference could run efficiently on standard CPUs.

The key takeaway was not just the model training itself. It was the operating model behind it. Instead of relying entirely on cloud-hosted LLMs, organizations can now:

• Fine-tune models locally
• Use proprietary internal documentation securely
• Reduce dependency on centralized GPU resources
• Reduce ongoing API and token consumption costs
• Deploy specialized AI assistants to end users at scale
• Keep sensitive enterprise data closer to the organization

This opens the door to highly targeted internal copilots for:

• IT support
• HR knowledge bases
• Engineering documentation
• Field operations
• Internal application support
• Operational workflows

The future of enterprise AI may not be a single generalized model serving everyone.

It may instead be thousands of specialized models operating securely and efficiently closer to the people who need them.

Agentic AI Moves into Operations

Another lab demonstrated what may become one of the most transformative enterprise AI use cases yet: autonomous infrastructure operations. Using NVIDIA NemoClaw agentic workflows alongside Dell AI Factory infrastructure running Kubernetes, the lab focused on monitoring an AI factory cluster and enabling AI-driven remediation.

The experience felt less like using a monitoring platform and more like collaborating with an operational engineer. Using natural language prompts, the system could:

• Identify infrastructure issues
• Analyze operational impact
• Recommend remediation
• Execute corrective actions
• Report outcomes and risks

Examples included prompts like:

• “Find the issue affecting cluster performance.”
• “Remediate the problem.”
• “What workloads are impacted?”
• “Summarize the operational risk.”

This represents a meaningful step beyond traditional observability platforms.

We are beginning to see operational AI systems that can do more than detect issues; they can reason through context and act. Enterprise AI is moving beyond simple conversational interfaces toward autonomous operational workflows.

Why This Matters

What stood out most at Dell Technologies World was not just the scale of AI infrastructure. It was the democratization of AI capability.

The combination of:

• Dell AI Factory infrastructure
• NVIDIA Grace Blackwell systems
• NemoClaw agentic frameworks
• Localized AI development workflows
• Deskside AI platforms like the GB10

…is helping create a future where organizations can distribute AI innovation directly to teams and individuals.

That changes the pace of experimentation dramatically.

It also changes the role of infrastructure and IT teams. Instead of serving only as gatekeepers to scarce GPU resources, they can become enablers of distributed AI platforms across the enterprise.

The result is faster iteration, lower friction, and potentially much broader AI adoption across organizations.

Summary Thoughts on Deskside AI

There is still a critical role for centralized AI factories and hyperscale GPU environments. Large-scale model training and enterprise-scale inference will continue to depend on them. But what I experienced at the conference was equally important: AI development is becoming decentralized.

Deskside AI gives organizations a practical way to move intelligence closer to users, data, and business workflows.

The organizations that succeed with AI over the next several years may not be the ones with only the largest clusters. They may be the ones that enable the most people to build, test, and operate AI where it delivers the most value.

About the Author

Brigham Mirabelli is a senior solutions engineer with more than 15 years of experience designing and delivering resilient, modernized and AI-ready IT infrastructure across datacenter, cloud, and hybrid environments. He specializes in compute, storage, virtualization, and cloud platforms — including Microsoft Azure, AWS, and VMware — and is known for building secure, scalable solutions that support mission-critical operations and long-term business continuity.