“AI inference can only be done in the cloud”: 5 myths debunked about deskside agentic AI development

Whatever your approach to AI development, you’ll be more than a little concerned by the spiraling costs of tokens. Analysis from Signal65 found agentic workloads can consume anywhere between 4x to 15x more tokens compared to traditional chat-based interactions.

This doesn’t mean that the cloud should be avoided for all AI workloads—far from it. But it does shake up the cloud-only paradigm that has formed around AI inference, security, scalability, and maintenance.

Fortunately, there is another way. With Dell Deskside Agentic AI, businesses are able to run production-grade agentic AI locally while keeping costs in check.

Enterprises exploring agentic AI need to look beyond the myths that continue to hold them back. In this article, we’ll take a look at five of the most common myths and how Dell’s solutions can help businesses cut costs and speed up development. There’s no one-size-fits-all approach when it comes to agentic AI. Look to the cloud for elastic burst; data centers for scale; and deskside for real-time, data-sensitive work.

TL;DR

• Agentic AI generates persistent, compounding compute loops rather than bounded, single-prompt token interactions, making cloud API rental economics unsustainable
• The Dell Pro Max with GB10 acts as the prototype explorer (handling 30B-200B parameter models and up to 8 agents), while the Dell Pro Max with GB300 provides data-center-class execution at the desk (handling 120B-1T parameter models and up to 150 agents)
• Local inference can reduce data-exposure risks by keeping sensitive data on-premises, supported by OpenShell sandboxing and policy guardrails, CrowdStrike Falcon protection, and BIOS-level security
• Dell’s portfolio uses a single consistent software platform (NVIDIA NemoClaw and OpenShell), helping workloads scale from the desk to Dell PowerEdge servers with minimal recoding
• The Dell Pro Max with GB300 could reduce two-year deployment costs by up to 87%* versus public-cloud APIs with an estimated breakeven in as little as three months

Myth 1: AI inference requires the cloud

While cloud environments were essential for training foundational models and serving single-prompt chatbots during the reasoning era, modern deskside workstations have evolved into dedicated local AI execution platforms capable of running continuous inference privately and securely.

Systems like the Dell Pro Max with GB10 allow individuals and small teams to run private, low-latency inference locally, while the Dell Pro Max with GB300 brings data-center-class capability directly to the desk, supporting complex, multi-agent systems without cloud round-trips.

To find out more about the Dell Pro Max with GB10, read this full IT Pro review.

Myth 2: Local agentic AI is less secure and harder to govern

There’s a persistent view that moving AI away from cloud-managed APIs to local devices leaves IT leaders blind, with processes difficult to govern, impossible to sandbox, and vulnerable to endpoint threats.

In reality, local agents can run in OpenShell sandboxes with policy-based guardrails, CrowdStrike Falcon protection and BIOS-level visibility—while sensitive data remains on-premises. With recent studies highlighting that AI agent-related incidents are no longer the exception (65% of survey respondents reported facing at least one in the past year), moving AI agents on-premises minimizes the risk of data exposure.

Myth 3: Deskside AI is only suitable for small models and prototypes

No longer true. It was once believed that local workstations could only handle lightweight AI models, like 7B to 13B parameter architectures. But the truth is that modern workstations can be small and mighty at the same time.

Local deskside infrastructure is no longer just for prototyping; it is a fully-fledged AI execution platform capable of delivering mid-size to frontier-class models at production scale. For instance, the Dell Pro Max with GB10 comfortably runs 30B to 200B-class models locally, supporting up to 8 concurrent agentic tasks for individual developers or small teams. The Dell Pro Max with GB300, meanwhile, can support 120B to 1T-parameter models and up to 150 concurrent agents.

Myth 4: Deskside AI creates another maintenance silo and must be rebuilt when it scales

Not at all. By standardizing on a consistent NVIDIA software environment and OpenShell runtime across the portfolio—from Dell Pro Max with GB10 and Dell Pro Precision desktop workstations to Dell Pro Max with GB300 and Dell PowerEdge servers—organizations can develop locally and scale towards production with minimal recoding. Supported end-to-end by Dell Services, deskside nodes can integrate with existing IT management frameworks rather than operating as isolated infrastructure islands.

Myth 5: Cloud APIs are always the most economical option

Sometimes, but in many cases deskside is better value. With public cloud APIs allowing businesses to lease tokens on demand, upfront capital expenditures (CapEx) are avoided, meaning you only pay for what you use.

However, the agentic AI era has seen the pay-as-you-go model come under significant pressure, with many organizations paring back their use of AI because of surging operating costs.

Unlike single-prompt queries, autonomous agents operate in persistent, multi-step loops—chaining actions and retrying code—which creates compounding, runaway token consumption. When agents run continuously, pay-per-token cloud costs can become unpredictable and escalate, and renting cloud infrastructure becomes an unpredictable and escalating operational expense.

Independent analysis by Signal65 demonstrates that local deskside deployment fundamentally alters token economics, with the Dell Pro Max with GB300 delivering savings of up to 87%, depending on workload intensity.

The right workload in the right place

Both the Dell Pro Max with GB10 and GB300 are part of the Dell AI Factory with NVIDIA portfolio, providing a consistent architecture from desk to data center and cloud, with NVIDIA NemoClaw and OpenShell plus CrowdStrike Falcon integration. Workloads can scale without wholesale rearchitecting while sensitive data remains local when required. It’s the best of both worlds.

If you think Dell Pro Max with GB10 or GB300 are the right solutions for your AI development workflows, find out more on the Dell website: US readers click here.