What Is the Best Way to Run Agentic AI Locally?

For a growing number of organizations, the best way to run agentic AI is locally. But running it well requires the right foundation — and that’s exactly what Dell Deskside Agentic AI is built for. Here’s what makes local the right approach and how to get started.

Why local matters for agentic AI

Cloud-based AI works well for conversational use cases. You send a prompt, a remote server processes it and you get a response. Simple.

Agentic AI doesn’t work in single exchanges. Agents run multi-step workflows that loop, branch and adapt. Every step generates tokens — and compared to standard chat interactions, agentic workloads easily consume 4x to 15x more tokens.² Every token sent to a cloud API adds cost, latency and data exposure. When agents are making hundreds of calls per workflow across a team, it all adds up quickly — and unpredictably.

Running agentic AI locally changes that dynamic in three important ways:

• Predictable costs. Once your infrastructure is in place, every model call is free. There are no per-token fees, no surprise bills at the end of the month and no pressure to limit how often your agents run. To learn more about the cost advantages of running agentic AI locally, read the full analysis from Signal65: The Economics of Agentic AI: Cost Advantages of Dell AI Factory with NVIDIA.
• Speed. Local inference eliminates the round-trip latency of cloud API calls. For agents executing dozens of sequential steps, removing even small delays at each step compounds into significantly faster end-to-end performance. There are no queue wait times, no rate limiting and no throttling mid-workflow. Your agents run as fast as your hardware allows — every time.
• Privacy. Your prompts, documents, workflows and enterprise data never leave your environment. For industries with strict compliance and data residency requirements, this isn’t a nice-to-have. It’s essential.

The question isn’t whether local agentic AI makes sense. For most organizations, it clearly does. The real question is what it takes to run it well.

What local agentic AI actually demands

This is where many organizations underestimate the challenge.

Running a chatbot locally is one thing. Running an autonomous agent that reasons, plans, calls tools and executes across applications is a fundamentally heavier workload. Your system needs to sustain performance across long-running, multi-step processes — not just handle a single inference call.

Here’s what matters most:

• Memory. Agentic workflows keep large model weights, context windows and intermediate reasoning states in memory simultaneously. More memory gives your agents more room to operate — and larger models or multi-agent deployments will demand significantly more.
• GPU acceleration. Agentic AI workloads involve sustained, repeated inference across long multi-step sessions. The more capable your GPU, the faster your agents reason and act. For these workloads, professional-grade GPU power isn’t optional.
• Storage speed. Models need to load into memory quickly, and agents working with local documents, databases or application data benefit from fast storage throughput. High-speed SSDs with ample capacity make a noticeable difference in agent responsiveness.
• A purpose-built software stack. You need an optimized software environment that handles model deployment, agent orchestration, secure runtimes and ongoing management — integrated and ready to go, not assembled from scratch.

Getting all of these right on general-purpose systems is possible, but it often means compromises. Purpose-built systems eliminate those tradeoffs.

The best way to run agentic AI locally: Dell Deskside Agentic AI

Dell Deskside Agentic AI, part of the Dell AI Factory with NVIDIA, is purpose-built for exactly this challenge. It gives organizations a local, secure and cost-predictable foundation to build, deploy and scale agentic AI workflows — right at the desk. Dell Deskside Agentic AI solutions can achieve up to 89% savings versus public cloud alternatives.²

Dell Deskside Agentic AI matches the right system to the right stage of your agentic AI journey:

• Dell Pro Max with GB10 and Dell Pro Precision T2 Tower are a powerful starting point for building and testing AI agents locally. With up to 128GB of unified memory, they support models up to 200 billion parameters and up to 8 concurrent agents.
• Dell Pro Precision T4 Tower delivers professional-grade GPU-accelerated performance for more demanding agent workloads, supporting models up to 500 billion parameters and up to 40 concurrent agents — scaling with multiple GPUs as your needs grow.
• Dell Pro Max with GB300 and Dell Pro Precision T6 Tower are designed for organizations ready to maximize multi-agent performance locally. Supporting up to 1 trillion parameters and up to 150 concurrent agents, they enable production-grade agentic AI deployment across teams and departments at enterprise scale.

Powering every Dell Deskside Agentic AI system is the NVIDIA NemoClaw software stack — an open-source reference stack that securely manages always-on AI assistants and agentic workflows through a single, integrated runtime. It brings together:

• OpenClaw — an open-source agentic framework that powers persistent, autonomous, multi-step AI workflows running locally.
• OpenShell — a secure runtime that isolates agents in sandboxed environments and enforces strict, policy-based privacy and security guardrails.
• NVIDIA Agent Toolkit — the toolkit used to build, orchestrate and manage long-running AI agents safely and efficiently across the environment.
• Nemotron models — a family of optimized open models designed for high-quality reasoning, coding and complex task execution in agentic workflows.

Seamlessly integrated with CrowdStrike Falcon endpoint protection, the full stack delivers continuous protection from BIOS to sandbox — providing a trusted and robust foundation for deploying long-running autonomous agents on local infrastructure.

To help organizations move from setup to production, Dell Services provides end-to-end guidance — from initial strategy and architecture through deployment and ongoing optimization.

Start local, scale with confidence

The best way to run agentic AI locally comes down to matching your systems to the workload. Agentic AI is more demanding, more sustained and more resource-intensive than any previous AI use case. General-purpose systems will get you started, but purpose-built systems will get you to production.

And when you’re ready to grow beyond the deskside, the Dell AI Factory with NVIDIA provides a clear path to scale from your desk all the way to enterprise-grade data center infrastructure — no starting over. The organizations that bring agentic AI closer to where work happens — whether fully local or as part of a hybrid approach — will move faster, keep their data closer and build AI capabilities that scale on their terms.

Ready to run agentic AI locally?

Learn how Dell Deskside Agentic AI can help you build, deploy and scale agentic AI at the desk.

👉 www.dell.com/desksideagenticai

From your first agent prototype to a fleet of 150 running across your organization, the right local infrastructure changes everything. Start building today.

_{Futurum Research, 1H 2026 AI Platforms Decision Maker Survey, March 2026, as cited in Signal65, The Economics of Agentic AI: Cost Advantages of Dell AI Factory with NVIDIA vs Public Cloud from Pilot to Scale, 2026.
Signal65, The Economics of Agentic AI: Cost Advantages of Dell AI Factory with NVIDIA vs Public Cloud from Pilot to Scale, 2026.}