How BenchSmart reveals what AI PCs actually deliver

Reading Time: 5 minutes The adoption of PCs built to run AI apps and features is taking hold. Gartner® analysts forecast AI PC shipments will total 143 million units and are projected to represent 55% of the total PC market in 2026.¹

Unlike standard computers, AI PCs include a chip that’s dedicated to AI. On most systems, this chip is called a neural processing unit (NPU). NPUs are fast and highly efficient. They can handle tasks like image generation and real-time transcription directly on the device, allowing organizations to increase performance and privacy without draining device batteries.

However, distinguishing between brands can be difficult. Manufacturers often buy the same chips and make similar claims, promising the fastest NPU, the longest battery life, and the highest peak performance. On paper, these devices look nearly identical. In reality, they’re not.

With endpoint budgets tightening, the gap between generic marketing and enterprise need has never felt wider — or more expensive. SHI built BenchSmart to close this gap with an independent, evidence-based view of AI PC performance.

What BenchSmart is and what makes it different

BenchSmart is SHI’s proprietary platform for testing AI device performance under real-world enterprise conditions. Our engineers run BenchSmart in our Next-Gen Device Lab, where a local test agent executes directly on each device to capture performance and power data across consistent workloads.

Unlike synthetic benchmarks or vendor-supplied claims, BenchSmart evaluates devices exactly the way your users experience them, running real workloads under real constraints. Two devices with similar published specs can perform very differently when running local AI models, operating on battery power, or handling multiple workloads simultaneously.

BenchSmart provides a neutral, workload-level view across devices, manufacturers, and silicon architectures to enable consistent, evidence-based performance comparisons. In a market shaped by rising hardware costs and aggressive AI PC marketing, that independent perspective helps you avoid expensive procurement missteps.

The metrics that matter

The fastest way to lose confidence in a device decision is to base it on numbers that don’t reflect how people in your organization work. BenchSmart focuses on distinct signals that directly impact user experience, productivity, and IT costs:

Real workload performance and device fit

BenchSmart tests how a device performs when running real enterprise AI tasks across across four critical workload types:

• Text: Tracks how quickly a device runs language models, measuring inference time (seconds) and inference rate (tokens per second).
• Audio: Evaluates processing speed for transcription and voice tools, tracking synthesis time (seconds) and real-time factor (RTF).
• Image: Measures creative and design performance by capturing image generation time (seconds) and image generation rate (pixels per second).
• Video: Gauges heavy multimedia and streaming tasks through inference time (seconds) and frames processed per second (FPS).

These tasks are tested at light, medium, and heavy data loads. This allows us to not only compare device speed, but also understand which device works best for your unique user personas.

Sustained operation under constraints

BenchSmart also measures how efficiently AI workloads use system resources and where processing is occurring across CPUs, GPUs, and NPUs. Key system resource and utilization metrics help you understand whether a device can sustain efficiency and performance under real-world conditions.

• Total system power: Overall device power consumption during AI workloads
• CPU metrics: RAM used (GB), power consumption (watts), and utilization (%)
• GPU metrics: RAM used (GB), power consumption (watts), and utilization (%)
• NPU metrics: RAM used (GB), power consumption (watts), and utilization (%)
• Workload distribution: AI processing allocation across the CPU, GPU, and NPU

We test devices both on battery power and when plugged into an outlet. A device that performs well when plugged in but degrades quickly on battery delivers a very different experience for mobile or field users, regardless of manufacturer claims.

Real-world AI performance validation

Peak TOPS (tera operations per second) is one of the most commonly promoted AI PC performance metrics, but it is not a useful metric without further context. It represents theoretical NPU performance under ideal conditions. TOPS can be misleading because it only reflects a device’s peak compute capability on paper. It ignores real-world factors like memory bandwidth, precision requirements, and workload efficiency that ultimately determine actual performance.

BenchSmart measures what happens when devices are running actual workloads to mimic how they would be used by employees.

What’s inside a BenchSmart report

Every BenchSmart report is designed to answer critical questions organizations ask when planning a hardware refresh:

• Which devices truly meet our workload needs?
• Where are we over- or under-investing?
• How will these devices hold up as our AI footprint grows?

BenchSmart reports provide side-by-side comparisons across manufacturers and configurations, showing you exactly where AI PCs earn their price tag.

Each report includes:

• Performance insights across 48+ device attributes
• Side-by-side comparisons aligned to enterprise workloads
• Visual dashboards and analysis for faster interpretation

How IT leaders turn reports into action

Organizations typically use BenchSmart in three key ways:

1. Identifying hidden performance gaps

As AI workloads move into production, many existing devices struggle in ways that aren’t visible during traditional testing. Devices that perform well in standard productivity scenarios may experience significant slowdowns when running local AI models, creating bottlenecks that impact adoption and user experience.

2. Aligning spend to actual value

With rising memory costs, over-buying hardware has become an expensive mistake, while under-buying remains risky. BenchSmart shows exactly where AI PCs deliver measurable performance gains, enabling smarter budget allocation across device tiers.

3. Matching devices to user personas

The legacy “one standard device for everyone” approach no longer works. The gap between user needs is widening rapidly with AI. BenchSmart helps you match devices to specific roles:

• AI-powered devices: For heavy AI users requiring sustained performance.
• AI-enabled devices: For knowledge workers with moderate, intermittent needs.
• Basic devices: For standard users where cost efficiency and basic productivity matter most.

A clearer path forward

Spec sheets and synthetic benchmarks were built for a device market without local AI processing, component shortages, and budget scrutiny. BenchSmart was built for the market you’re buying in today. It provides an independent, workload-driven, and persona-based approach to device procurement that can give you the data-backed insights you need to make confident decisions.

NEXT STEPS:

Find the device that works best for you. Request sample BenchSmart reports.

1. Gartner press release, Gartner Says AI PCs Will Represent 31% of Worldwide PC Market by the End of 2025, August 28, 2025
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