The market for AI-powered laptops and portable computers capable of running intelligent agents locally is projected to grow from $4.3 billion in 2026 to approximately $86.2 billion by 2035, representing a compound annual growth rate of 39.5% over the next nine years. This explosive expansion reflects a fundamental shift in how artificial intelligence is being deployed, moving away from cloud-dependent systems toward devices that can think and act independently on your personal computer.

What's driving this transformation? The answer lies in three converging forces: better hardware, smarter software, and a growing demand for privacy and speed. Users no longer want to send every request to distant data centers. They want their laptops to handle complex AI tasks locally, without constant internet connectivity, and without exposing sensitive information to cloud servers.

What Are Neural Processing Units and Why Do They Matter?

At the heart of this shift sits a relatively new type of computer chip called a Neural Processing Unit, or NPU. Unlike traditional processors that handle general computing tasks, NPUs are specialized silicon designed specifically for artificial intelligence workloads. They excel at the repetitive mathematical operations that power machine learning models, allowing laptops to run sophisticated AI agents without draining battery life or requiring constant cloud connectivity.

The performance improvements in NPUs have been dramatic. Qualcomm's Snapdragon X2 Elite platform now delivers up to 85 TOPS (trillion operations per second) of AI processing power, while AMD's Ryzen AI 400 and PRO 400 Series processors offer up to 60 TOPS. These numbers matter because they determine how complex an AI model can be while still running smoothly on your device. For context, higher TOPS means your laptop can handle larger language models and more sophisticated reasoning tasks without lag.

How Are Companies Building Local AI Agents Into Portable Computers?

• Hardware Architecture: Laptops now combine traditional CPUs and GPUs with dedicated NPUs, creating a three-tier system where different types of AI tasks are routed to the processor best suited for them. Hardware accounted for 55.8% of the portable computer on-device AI agent market in 2026, reflecting the critical importance of capable processors, memory, and power-efficient system designs.
• Operating System Integration: Windows has emerged as the leading platform with a 55.9% market share, supported by native AI capabilities and enterprise-grade security features. Microsoft's Copilot+ PCs exemplify this approach, combining local processing with optional cloud escalation for demanding workloads. Windows also includes an On-Device Registry that allows AI agents to securely connect to local files, applications, and business tools through standardized interfaces.
• Model Optimization: Smaller language models accounting for 43.7% of the market are purpose-built for local execution. AMD's Ryzen AI Halo platform, for example, can support local models of up to 200 billion parameters under supported configurations, dramatically expanding the complexity of AI applications that can run without constant cloud access.

One concrete example of this evolution is Perplexity's Portable Computer agent, which launched on August 25, 2026. This system runs its orchestrator, planner, tool router, scheduler, and local search index directly on NVIDIA DGX Spark and RTX PCs. The key innovation is that complex agent workflows that previously required cloud infrastructure can now execute on personal computers, with cloud escalation occurring only when necessary and authorized.

Why Are Enterprises Driving This Market?

Enterprises account for 58.2% of the portable computer on-device AI agent market, driven by demand for secure local processing, workflow automation, and employee assistance tools. Companies are attracted to on-device AI because it eliminates the need to send confidential documents, customer data, or proprietary information to external cloud services. This is particularly important in regulated industries like finance, healthcare, and government.

The hybrid local-cloud deployment model has captured 56.8% of the market, offering a practical compromise. This approach allows latency-sensitive and privacy-critical tasks to run locally on the device while larger models or compute-intensive workflows can be routed to cloud infrastructure when needed. This balance provides responsiveness, privacy, model capability, and battery efficiency all at once.

What's the Difference Between AI Chips and Traditional Processors?

Understanding why specialized AI chips matter requires grasping how they differ fundamentally from the processors that power traditional computers. A standard CPU (central processing unit) excels at handling one complex task at a time, like running your operating system or opening a web browser. An AI chip, by contrast, has thousands of smaller cores that work simultaneously on simpler, repetitive tasks.

This parallel processing capability is essential because artificial intelligence models rely on matrix multiplication repeated billions or trillions of times within milliseconds. A traditional CPU would be painfully slow at this task. AI chips are built from the ground up to perform thousands of computational operations simultaneously, which is exactly what neural networks require.

However, there's a tradeoff. While AI chips are incredibly efficient per computation, the sheer volume of calculations they perform means they consume eight to ten times more power than a CPU. This is why memory bandwidth, not just raw processing cores, has become the focus of chip designers. Moving data from memory to the processing cores is often the bottleneck, so optimizing this data flow is as important as adding more cores.

How Does the Market Break Down by Device Type and Use Case?

AI PCs and laptops represent 61.7% of the portable computer on-device AI agent market, supported by increasing integration of dedicated AI accelerators into personal and enterprise computing devices. These systems combine conventional CPU and GPU resources with dedicated AI acceleration, enabling local summarization, content generation, intelligent search, meeting assistance, and privacy-sensitive workflows.

Productivity and knowledge work agents hold a 35.8% share by agent type, driven by growing use of AI agents for document creation, research, summarization, scheduling, information retrieval, and workplace task automation. This reflects the reality that most users interact with AI through productivity tools rather than standalone applications.

North America leads the market with a 39.9% regional share, representing approximately $1.72 billion in 2026. This dominance is supported by early adoption of AI PCs, strong enterprise technology spending, advanced computing infrastructure, and a mature ecosystem for on-device AI applications.

"Portable computers with on-device AI agents are gaining adoption as users seek faster, more private, and always-available AI assistance without depending entirely on cloud processing. Buyers are prioritizing low-latency performance, data privacy, battery efficiency, offline capability, and seamless software integration, while suppliers with optimized AI hardware and efficient local models are positioned for wider adoption," stated a Principal Consultant at Globe Market Research.

Principal Consultant, Globe Market Research

What Does This Mean for the Future of Computing?

The shift toward on-device AI agents represents a fundamental reimagining of what personal computers are designed to do. Rather than serving primarily as passive devices for consuming content or running applications, laptops are evolving into local AI execution platforms capable of understanding user goals, accessing approved information, calling applications, and carrying out complex workflows without routing every step through a remote cloud.

This transformation is being enabled by convergent improvements across multiple dimensions: higher NPU performance, larger unified memory pools, local language model runtimes, standardized agent connectivity protocols, and operating system-level agent frameworks. Each of these advances individually would be noteworthy; together, they represent a wholesale restructuring of AI infrastructure.

The market's projected growth to $86.2 billion by 2035 suggests this is not a niche trend but a fundamental shift in how computing will work. As NPU performance continues to improve and software ecosystems mature, expect on-device AI to become as standard in laptops as graphics processors are today.