Enterprise AI costs are spiraling out of control in the cloud, and a new generation of on-premises tools is offering a radical alternative. Dell Technologies and NVIDIA have introduced Dell Deskside Agentic AI, a production-ready system that lets organizations deploy and scale AI agents locally on high-performance workstations rather than relying on expensive cloud APIs. The economics are compelling: companies can break even versus public cloud costs in as little as three months and reduce spending by up to 87% over two years (Source 1, 2).

The problem driving this shift is straightforward but severe. Unlike simple chatbots, agentic AI systems are designed to run continuously and autonomously, executing complex multi-step tasks without human intervention. Each step these agents take consumes inference tokens, which are the computational units cloud providers charge for. As these systems scale, token usage compounds rapidly, creating unpredictable and unsustainable bills. One developer at a Dell customer site generated a $3,400 bill in a single day after exhausting one billion tokens, illustrating how quickly costs can spiral.

What Makes Local AI Agents More Cost-Effective Than Cloud?

The fundamental economics favor on-premises deployment for agentic workloads. Cloud-only strategies force organizations to pay per token for every inference step, and when agents run continuously, those costs multiply. Local systems, by contrast, shift the cost model from per-token consumption to upfront hardware investment with predictable operational expenses. This works particularly well because roughly over 50% of agentic workflows run on open-weight models, which are freely available and can be deployed on local hardware without licensing fees (Source 1, 2).

The Dell-NVIDIA solution addresses three critical pain points enterprises face with cloud-only approaches: economics, security, and data sovereignty. Organizations keep inferencing local, maintain control over sensitive data, and avoid the latency and bandwidth costs associated with sending every inference request to a remote cloud provider.

How to Deploy Local Agentic AI Across Your Organization

• Start Small with Compact Workstations: The Dell Pro Max with GB10 is a compact, power-efficient system designed for individual agent prototyping and small-scale workloads, supporting models from 30 billion to 200 billion parameters.
• Scale to Enterprise Workstations: The Dell Pro Precision 9 features Intel Xeon 600 processors and up to five NVIDIA RTX PRO Blackwell Workstation Edition GPUs, providing scalable performance for workhorse-class AI workloads supporting models from 30 billion to 500 billion parameters.
• Deploy Frontier-Level Models Locally: The Dell Pro Max with GB300, powered by the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip and Dell's MaxCool cooling technology, is purpose-built for inference of frontier-level AI models ranging from 120 billion up to 1 trillion parameters.
• Use NVIDIA NemoClaw as Your Foundation: This open-source reference stack provides a secure foundation for managing always-on AI agents, combining NVIDIA Nemotron models for reasoning and coding with OpenShell's secure runtime, all part of the NVIDIA Agent Toolkit.
• Leverage Professional Services: Dell Services provides end-to-end guidance through the full agentic AI lifecycle, from initial strategy and hardware deployment to workflow alignment, agent prioritization, and ongoing optimization.

The NVIDIA NemoClaw reference stack is the technical backbone of this approach. It combines high-performance NVIDIA Nemotron open models for reasoning and coding tasks with OpenShell, a secure runtime environment that provides sandboxed execution for AI agents. This stack is built on OpenClaw, an agentic framework that powers persistent, autonomous, multi-step AI workflows on local hardware (Source 1, 2).

How Does NVIDIA OpenShell Secure Local AI Agents?

Security and governance are critical concerns when deploying AI agents that operate autonomously. NVIDIA OpenShell addresses this by providing a sandboxed environment where developers and IT teams can build, deploy, and govern AI agents with privacy and security controls enforced at runtime. The runtime is now supported across the entire Dell AI Factory with NVIDIA, spanning from deskside workstations through Dell PowerEdge XE servers running on Canonical Ubuntu and Red Hat AI (Source 1, 2).

This unified security layer is significant because it eliminates the need for separate governance frameworks at different infrastructure tiers. Organizations get consistent policy enforcement whether agents run on a developer's workstation or in a data center, reducing complexity and security gaps.

"The most efficient token is the one produced closest to the data, and most enterprise data isn't in the cloud. Dell Deskside Agentic AI gives every workgroup a secure local environment to run agents, keep costs predictable and keep IP inside the building. What works at the desk scales to the data centre," said Jeff Clarke, Chief Operating Officer at Dell Technologies.

Jeff Clarke, Chief Operating Officer, Dell Technologies

For regulated industries, Dell and NVIDIA have developed the Dell-NVIDIA AI-Q 2.0 Reference Architecture, powered by Dell AI Data Platform. This architecture is engineered specifically for demanding on-premises workloads in financial services, public sector, and manufacturing, where data residency and compliance requirements make cloud deployment impractical (Source 1, 2).

Why Are Enterprises Shifting Away From Cloud AI APIs?

The shift reflects a maturation in how organizations think about AI infrastructure. Early AI adoption focused on experimentation and proof-of-concept work, where cloud APIs made sense because they required no upfront capital investment. But as companies move from pilots to production deployments, the cost dynamics change dramatically. Agentic AI systems that run continuously and autonomously generate token consumption patterns that cloud pricing models were never designed to handle efficiently (Source 2, 3).

Dell reports that its AI Factory lineup, which bundles servers, NVIDIA processors, software, and services, has grown to 5,000 customers, up from 4,000 in February 2025. Major customers include Eli Lilly, Honeywell International, and Samsung Electronics, suggesting that enterprise adoption of on-premises AI infrastructure is accelerating.

"As enterprises reshape and scale the future of work with agentic AI, they're seeking infrastructure that spans the full enterprise from our desks where work happens to the AI factories where intelligence scales. With NVIDIA OpenShell across the Dell AI Factory with NVIDIA, enterprises can develop locally, scale securely and deploy agentic AI on one consistent platform," explained Justin Boitano, Vice President of AI Platforms at NVIDIA.

Justin Boitano, Vice President, AI Platforms, NVIDIA

The availability of open-weight models in the 30 billion to 284 billion parameter range has been crucial to this shift. These models perform bulk reasoning efficiently enough to drive operations forward without requiring the largest frontier models, which are often only available through cloud APIs. This means organizations can achieve production-grade performance with models they can run locally (Source 1, 2).

Dell's assessment of the broader market challenge is telling. According to Sam Grocott, Senior Vice President of Product Marketing at Dell, "Most enterprises don't have an AI ambition problem. They have an AI execution problem." This suggests that the bottleneck isn't strategy or vision, but rather the practical ability to deploy AI systems that work reliably, securely, and cost-effectively at scale.

The Dell Deskside Agentic AI solution, paired with NVIDIA's NemoClaw stack and OpenShell runtime, represents a direct response to this execution challenge. By making local deployment as straightforward as cloud deployment, and by delivering superior economics, the partnership is reshaping how enterprises think about AI infrastructure investment. The three-month break-even timeline and 87% cost reduction over two years suggest that the economics of local agentic AI are now compelling enough to drive significant shifts in enterprise purchasing decisions (Source 1, 2).