What if the key to unlocking humanity’s next era of innovation lies in two acronyms that are reshaping our digital world? GPUs (graphics processing units) and NPUs (neural processing units) are at the heart of the artificial intelligence (AI) revolution, driving breakthroughs in industries as diverse as healthcare, automotive, entertainment and edge computing. Today, we’re at a critical juncture where understanding and adopting these tools isn’t just an advantage — it’s a necessity.
A dual revolution in computing
GPUs have long reigned as the champions of parallel processing. Initially engineered to render life-like graphics in gaming, they’ve evolved into powerhouse tools for AI and machine learning (ML) model training. Now NPUs, a more recent technological marvel, are boldly stepping into the arena. Unlike GPUs, NPUs are purpose-built for AI-specific computations, excelling in lightweight, energy-efficient AI inferencing, particularly on edge devices like smartphones and sensor-based Internet of Things (IoT) systems. NPUs also power AI PCs, which offer quieter, longer-lasting performance and enable continuous AI task processing, transforming everyday operations.
Industry data highlights this evolution. According to Gartner, global revenue from NPUs — also referred to as AI semiconductors or AI chips — is expected to reach $71 billion in 2024, a 33% increase over 2023. AI PCs are predicted to make up 22% of total PC shipments in 2024, growing to 100% of enterprise purchases by 2026. Driven largely by generative AI (GenAI), the demand for GPUs is also on the rise. By the end of 2024, the market for these specialized server accelerators will be valued at $21 billion and grow to $33 billion by 2028.[1]
GPU vs. NPU comparative analysis
To fully understand their roles, here is a comparative analysis:
Use cases and strengths: GPUs excel in tasks requiring raw computational power, like AI model training and 3D rendering. NPUs are optimized for repetitive AI tasks such as voice recognition, offering unmatched speed and efficiency at lower power consumption.
Energy efficiency: One of NPUs’ greatest advantages is their low-power design. While GPUs often demand significant energy resources, NPUs operate efficiently in devices where power and heat management are critical — like mobile phones and edge servers.
Scalability: GPUs shine in cloud environments, scaling effortlessly across large data centers. Meanwhile, NPUs are tailored for edge computing, enabling real-time decision-making in drones, vehicles and even home devices.
The promise of GPUs and NPUs across industries
The implications of these technologies extend far beyond hardware innovation. They’re opening new realms of opportunity across industries, such as:
Gaming and creative: GPUs have revolutionized gaming, enabling features like real-time ray tracing that make virtual worlds almost indistinguishable from reality. For creators, GPUs power high-definition rendering, animation workflows and video editing, delivering speeds and visual fidelity unparalleled in human history.
AI research and development: Cutting-edge AI applications demand immense computational power for training models on vast datasets — a domain where GPUs dominate. But NPUs are emerging as the ideal complement, enabling AI inferencing to run efficiently in real time on compact devices. This is crucial for tasks like on-device natural language processing (NLP) or facial recognition.
Healthcare and life sciences: From medical imaging to drug discovery, GPUs and NPUs work hand-in-hand to analyze massive data sets. For instance, GPUs enable rapid image analysis for CT scans, while NPUs power wearable devices that monitor patient vitals in real time.
Autonomous vehicles: The complex task of navigating real-world environments in autonomous cars requires GPUs to process vast sensor data, while NPUs assist in real-time decision-making. Together, they form a foundation for safer, smarter mobility.
Charting the future of AI infrastructure
What lies ahead for GPUs and NPUs? Edge computing will continue to rise in importance, as real-time AI decision-making becomes pivotal. The future may bring hybrid processors that combine the raw power of GPUs with the efficiency of NPUs, enabling seamless performance across a variety of workloads. Imagine precision parking systems in autonomous vehicles making split-second calculations based on NPU-powered inference, or GPU-fueled metaverse environments offering fully immersive experiences.
While the use cases for GPUs and NPUs are inspiring, there are a few considerations impacting adoption. Many organizations face challenges in efficiently integrating these technologies into their operations. This is where purpose-built edge computing solutions, like Dell NativeEdge, come into play. Dell NativeEdge democratizes access to advanced AI infrastructure at the edge by seamlessly provisioning, deploying and orchestrating applications across devices equipped with either CPUs or GPUs, all with zero-touch and zero-trust capabilities. This approach ensures efficient management of computational tasks and supports on-device AI inferencing. By facilitating these capabilities, Dell NativeEdge accelerates innovation across various verticals, including smart cities, healthcare, retail and manufacturing, ultimately enhancing operational efficiency, reducing costs and driving competitive advantage.
These innovations in edge computing are paving the way for the future. By leveraging both GPUs and NPUs, Dell Technologies helps businesses deploy intelligent systems where they’re most needed, from connected factories to energy-efficient smart buildings.
Empower your business for the AI age
The age of AI isn’t arriving — it’s here. GPUs and NPUs are no longer optional, they’re integral to staying competitive in a tech-driven world. For enterprises, innovators and tech professionals, the question isn’t if you’ll harness this revolution, it’s how.
At Dell Technologies, we’re not just driving the narrative — we’re shaping the future. Explore how we’re democratizing AI infrastructure and reshaping edge computing with GPUs and NPUs in our blog, Democratizing the AI Infrastructure Market through NPUs.