$3k a day on tokens? Dell’s deskside agentic AI Accelerators will save you money on AI development

Agentic AI can automate complex work but, under certain circumstances, repeated cloud API calls can lead to significant token consumption and some unexpectedly large bills.

As enterprises move from experiments to persistent deployments, Dell Pro Precision with GB10 and Dell Pro Precision with GB300 AI accelerators offer a local alternative. By running suitable open-weight models on deskside hardware, organizations can replace variable per-token charges with upfront infrastructure costs.

TL;DR

• Rising AI costs are prompting a rethink of cloud-based token consumption
• The Dell Pro Precision AI accelerators are designed specifically for local AI inference and agent orchestration
• Developers can markedly reduce token consumption costs with dedicated AI hardware

AI costs have become a recurring pain point for enterprises over the past 18 months, with surging token consumption rates resulting in huge fees for organizations of all sizes.

However, rising costs are largely down to how the AI is used in 2026, rather than the technology itself. While previous iterations of AI, such as AI chatbots and assistants, provide answers to a single query or request, agents work differently – invariably resulting in higher token consumption.

That’s because agents use multi-step reasoning to achieve their goal, meaning they essentially operate in loops to conduct tasks or provide answers to queries. Each step consumes tokens – the more complex the task, the more tokens are consumed.

The result is that individual developers or IT specialists using an agent (or multiple agents) can end up consuming a significant amount of tokens, racking up huge bills.

In fact, a recent study from Signal65 found that, “compared to standard chat interactions, agentic workloads consume orders of magnitude more tokens with agents easily utilizing 4x to 15x more tokens.”

With this in mind, enterprises ramping up agentic AI adoption face critical considerations in terms of costs. There are, however, ways to curtail high expenditures and leverage agents in a more cost-efficient manner.

On-device AI use is growing in popularity, often in direct response to growing financial-related concerns. Dedicated AI accelerators like Dell Pro Precision towers and Dell Pro Precision with GB10 and GB300 are designed specifically for local AI use and agent orchestration. This offers several advantages for enterprises, particularly in helping to bolster safety and security.

Running AI on devices and contained within local infrastructure helps users innovate securely and keep mission-critical data safe.

Given costs are front of mind for IT leaders in 2026, however, how can AI accelerators help solve this dilemma?

The shift to inference: Where does AI compute actually go?

While high-profile model training dominates public headlines, the day-to-day reality of enterprise AI centers on inference – the continuous execution of pre-trained and fine-tuned models. For most organizations, the primary operational challenge is not building proprietary foundation models from scratch, but deploying them efficiently at scale.

Inference is where cost, latency, and data security decisions compound. When autonomous agents operate in multi-step loops to complete tasks, inference demand ceases to be periodic and becomes sustained. Routing all this ongoing inference compute through cloud-based APIs creates an escalating cost model that directly impacts enterprise margins.

Tackling tokenomics head-on

Devices such as Dell Pro Precision with GB10 specifically address a key recurring issue when it comes to agentic AI: the unpredictable costs of cloud-based consumption billing.

Agents rely on inference, using sustained compute power to help an agent carry out complex, multi-step tasks. This places significant strain on cloud infrastructure, compounding costs for enterprises.

A recent Gartner® study shows that “AI inference costs per agentic workflow will increase more than fivefold through 2028,” going on to say that “as a result, inference cost management has become a top priority for product leaders.”

Put simply, using cloud-based tokens, a single developer can burn through a significant number of tokens and place huge strain on cloud infrastructure over the course of a single day.

This is something Dell Technologies has direct experience with. At the company’s annual conference this year, Jon Seigal, SVP of client solutions group and online marketing, said a ‘super user’ racked up a bill of just $3,400 in 24 hours.

How can you cut token consumption?

Reducing token consumption – and thereby costs – can be achieved using AI accelerators such as the Dell Pro Precision and Dell Pro Precision range. These AI accelerators allow users to run large language models (LLMs) and orchestrate agents directly from the device. They are, in essence, a core component of modern on-premises AI infrastructure.

According to Signal65 analysis, this offers huge advantages, providing an alternative to costly cloud-based APIs without per-token pricing. “Across all tested profiles, on-premises AI infrastructure demonstrated a commanding financial lead, offering a substantial reduction in TCO (total cost of ownership) compared to cloud deployments for both small-scale assistants and complex agentic fleets,” the researchers found.

In one example, a software developer using 60 agents with Dell Pro Precision with GB300 was modeled to save up to 87% under the study’s assumptions. In another, a sales agent using four agents through Dell Pro Precision with GB10 recorded 76% lower costs compared to public cloud-based APIs.

Hybrid AI architecture: The new enterprise standard

To balance cost predictability, low latency, and robust data governance, enterprise architectures are consolidating around a four-tier hybrid AI model: on-device, deskside, data center, and cloud.

In this architecture, the desk serves as a strategic execution point. Local deskside infrastructure handles real-time, data-sensitive agent workloads and continuous inference, removing per-token API fees while preserving data privacy. Meanwhile, the data center supports heavy shared services and scale-out training, and the public cloud provides elastic capacity for temporary burst demand.

Drawing on the Dell ecosystem

Both Dell Pro Precision with GB10 and GB300 function within a broader ecosystem of Dell products.

Interoperability with Dell Pro Precision 9 series desktop workstations, for example, offers advantages in terms of compute and token consumption, as well as performance. Users are able to offset tasks and workflows to Dell Pro Precision 9 series desktop workstations, reducing the strain placed on Dell Pro Precision AI accelerators.

Similarly, NVIDIA Grace Blackwell architecture plays a significant role, particularly in terms of efficiency. For example, the Blackwell architecture is designed specifically for “data center-scale reasoning AI workflows”, offering up to 30x better energy efficiency compared to the prior Hopper GPU generation.

If you think Dell's deskside AI accelerators are the right call for your business, find out more on the Dell website: US readers click here.

Gartner Press Release, Gartner Predicts AI Inference Costs Per Agentic Workflow Will Increase More Than Fivefold Through 2028, August 17, 2026

GARTNER is a trademark of Gartner, Inc. and/or its affiliates.

FAQs

What are the Dell Pro Precision with GB10 and Dell Pro Precision with GB300?

They are dedicated deskside AI accelerators designed for local AI inference and agent orchestration, offering a cost-effective alternative to variable cloud API fees.

How do local AI accelerators help reduce enterprise token costs?

By running open-weights models locally on deskside hardware, organizations swap variable, per-token cloud API fees for predictable, upfront infrastructure costs.

What is a hybrid AI architecture?

It balances workloads across four execution points: on-device, deskside local compute for real-time inference, data center for shared scale, and public cloud for temporary burst capacity.