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 advanced computing. Today, we find ourselves at a critical moment where understanding and adopting these tools is not just a benefit, it’s a necessity.
A double IT revolution
GPUs have long reigned as the champions of parallel processing. Originally designed to render realistic graphics in games, they have become powerful tools for training AI and machine learning (ML) models. Today, NPUs, a newer technological marvel, are boldly entering the arena. Unlike GPUs, NPUs are purpose-built for AI-specific computation, excelling in lightweight, power-efficient AI inference, especially on edge devices like smartphones and Internet of Things (IoT) systems. ) based on sensors. NPUs also power AI PCs, which deliver quieter, more durable performance and enable continuous processing of AI tasks, transforming daily operations.
Industry data highlights this development. According to Gartner, global revenue from NPUs – also known as AI semiconductors or AI chips – is expected to reach $71 billion in 2024, an increase of 33% from 2023. AI PCs are expected to account for 22% of total PC shipments in 2024., reaching 100% of enterprise purchases by 2026. Driven largely by AI generative technology (GenAI), the demand for GPUs is also on the rise. increase. By the end of 2024, the market for these specialized server accelerators will be valued at $21 billion and will reach $33 billion by 2028.(1)
GPU vs. NPU Benchmarking
To fully understand their roles, here is a comparative analysis:
Use cases and highlights: GPUs excel at tasks requiring raw computing power, like training AI models and 3D rendering. NPUs are optimized for repetitive AI tasks such as speech recognition, delivering unmatched speed and efficiency with lower power consumption.
Energy efficiency: One of the biggest advantages of NPUs is their low-power design. While GPUs often require significant power resources, NPUs operate efficiently in devices where power and heat management are critical, such as mobile phones and edge servers.
Scalability: GPUs shine in cloud environments, scaling effortlessly in large data centers. Meanwhile, NPUs are designed 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 open up new areas of opportunity across all sectors, such as:
Games and creation: 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 unprecedented in human history.
AI research and development: Cutting-edge AI applications require immense computing power to train models on large datasets – an area where GPUs dominate. But NPUs appear to be the ideal complement, allowing AI inference to run efficiently in real time on compact devices. This is crucial for tasks like on-device natural language processing (NLP) or facial recognition.
Health and life sciences: From medical imaging to drug discovery, GPUs and NPUs work hand-in-hand to analyze massive data sets. For example, GPUs enable rapid image analysis for CT scans, while NPUs power wearable devices that monitor patients’ vital signs in real time.
Autonomous vehicles: The complex task of navigating real-world environments in self-driving cars requires GPUs to process vast sensor data, while NPUs facilitate real-time decision-making. Together, they form the basis for safer and smarter mobility.
Charting the future of AI infrastructure
What lies ahead for GPUs and NPUs? Advanced computing will continue to grow in importance, as real-time AI decision-making becomes crucial. The future could bring hybrid processors combining 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 performing split-second calculations based on NPU-powered inference, or GPU-powered metaverse environments delivering fully immersive experiences.
While the use cases for GPUs and NPUs are inspiring, there are a few considerations that impact their adoption. Many organizations face challenges in effectively integrating these technologies into their operations. That’s where purpose-built edge computing solutions like Dell NativeEdge come in. Dell NativeEdge democratizes access to advanced AI infrastructure at the edge by seamlessly provisioning, deploying and orchestrating applications on devices equipped with CPUs or GPUs, all with contactless and trustless capabilities. This approach ensures efficient management of computational tasks and supports on-device AI inference. By facilitating these capabilities, Dell NativeEdge accelerates innovation across various verticals, including smart cities, healthcare, retail and manufacturing, thereby improving operational efficiencies, reducing costs and creating competitive advantage.
These edge computing innovations are paving the way for the future. By leveraging both GPUs and NPUs, Dell Technologies helps businesses deploy intelligent systems where they are needed most, from connected factories to energy-efficient smart buildings.
Empower your business to adapt to the AI era
The age of AI is not coming, it is here. GPUs and NPUs are no longer optional, they are an integral part of maintaining competitiveness in a technology-driven world. For businesses, innovators and technology professionals, the question is not if you will exploit this revolution, it is how.
At Dell Technologies, we don’t just drive the narrative: we shape the future. Learn how we’re democratizing AI infrastructure and reshaping edge computing with GPUs and NPUs on our blog, Democratizing the AI infrastructure market with NPUs.