NPU vs. GPU: What's the difference?

GPUs contain thousands of cores to achieve the fast, precise computational tasks needed for graphics rendering. NPUs prioritize data flow and memory hierarchy for better processing AI workloads in real-time.

Both types of microprocessors excel at the types of parallel processing used in AI, but NPUs are purpose-built for machine learning (ML) and artificial intelligence tasks. 

Neural processing units (NPUs) are having a moment, but why is this nearly decade-old tech suddenly stealing the spotlight? The answer has to do with recent advancements in generative AI (artificial intelligence) reigniting public interest in AI applications—and by extension—AI accelerator chips such as NPUs and GPUs. 

NPU architecture differs significantly from that of the CPU or GPU. Designed to execute instructions sequentially, CPUs feature fewer processing cores than GPUs, which feature many more cores and are designed for demanding operations requiring high levels of parallel processing. 

Whereas CPUs struggle with parallel-processing tasks and GPUs excel at the cost of high energy consumption, NPU architecture thrives by mimicking the way human brains process data. More than simply adding additional cores, NPUs achieve high parallelism with less energy consumption through a number of unique features and techniques:

• Specialized compute units: NPUs integrate dedicated hardware for multiplication and accumulation operations—essential for training and inference of neural network models.

• High-speed on-chip memory: To minimize bottlenecks related to memory access, NPUs feature high-speed integrated memory, allowing rapid access to model data and weights.

• Parallel architecture: NPUs are designed to perform thousands of parallel operations, making them extremely efficient in processing data batches.

When comparing NPUs and GPUs, it can be useful to assess performance across key features.

• GPUs are designed to break down demanding image-processing tasks into smaller operations that can be processed in parallel.   

• NPUs are designed to mimic the human brain with modules to speed up multiplication and addition while improving on-chip memory.

• GPUs offer excellent parallel computing capabilities, but it comes at the cost of high-power consumption. 

• NPUs offer equal (or even better) parallelism, especially when it comes to short, repetitive calculations. Designed to handle the types of AI algorithms used in neural networks, NPUs are particularly well suited for processing large-scale data sets requiring matrix multiplications.

• GPUs, although more specialized than CPUs, are more suited to general-purpose computing.

• NPUs are specialized processors purpose-built for AI and machine learning tasks. They shed some of the excess features used by GPUs to optimize for energy efficiency.

• GPUs, as a predecessor to NPUs, benefit from a more developed environment and are widely available on the consumer market. Available to professionals and hobbyists, Nvidia’s CUDA language allows for easy GPU programming with open-source compiling for various operating systems. 

• NPUs are newer than GPUs and are generally less accessible. Many proprietary NPUs, such as Google’s Tensor Processing Unit (TPU) or Qualcomm’s Snapdragon (used by Apple), might not be available to the broader market. NPU chips produced by manufacturers such as Intel or AMD have comparatively less community resources.

• GPUs are frequently used in gaming and computer animation, where graphics cards are responsible for image-processing optimization. They are also effective in other applications demanding high levels of parallelism, such as in data centers, crypto mining or AI model training.

• NPUs are used in a more focused scope and offer exceptional parallelism while requiring less power. Typically combined with GPUs, NPUs offload the more demanding AI tasks and are best suited for machine learning tasks such as processing AI workloads in large language models (LLM), deep learning image recognition or blockchain and AI. 

Incorporating NPUs into integrated systems offers a number of salient advantages over traditional processors in terms of speed, efficiency and convenience. Benefits include the following:

• Localization: Processing AI applications requires significant compute resources, and, for this reason, it is often relegated to the cloud. However, relying on a distant server can slow down operations and leave sensitive information vulnerable to possible data leaks. NPUs allow for the localized, real-time processing of AI tasks, lowering latency for critical applications such as voice or face recognition, medical diagnostics and automated driving systems. 

• Resource management: Commonly integrated NPUs can help optimize overall system resources by shouldering the repetitive tasks necessary for AI applications. Offloading these types of tasks to an NPU frees up GPU resources to process large data volumes for more general computations.

• Efficiency: While GPUs are capable of handling many demanding tasks associated with AI, NPUs are purpose-built for these requests and can meet similar or even better performance benchmarks while requiring exponentially less power—a particularly valuable feature for battery-powered devices with finite capacity. 

As a coprocessor, NPUs have been in use for a number of years, typically integrated with GPUs to provide support for specific repetitive tasks. NPUs continue to be valuable in consumer-level tech (such as Microsoft Windows’ AI Copilot) and various Internet of Things (IoT) devices (such as smart speakers that use NPUs for processing speech recognition). 

However, recent developments in AI technology have put a brighter spotlight on this type of processor as more advanced AI models have brought consumer-grade AI tools into the popular conversation. Specifically designed for demanding AI tasks such as natural language processing, as interest in consumer-grade AI grows, so has interest in NPUs. 

Main use cases for NPUs include the following:

• Artificial intelligence and large language models: NPUs are purpose-built to improve the performance of AI and ML systems, such as large language models (LLMs) that require low-latency adaptive processing to interpret multimedia signals, perform speech recognition and generate natural response. NPUs are also adept at AI-enabled video processing tasks, such as blurring the background on video calls or automatically editing images.   

• Internet of Things (IoT) devices: Low profile and energy efficient, NPUs are a powerful coprocessor for small smart devices including smartphones, mobile devices and wearables where battery power comes at a premium and efficiency is prioritized.

• Data centers: Known for processing demanding workloads, data centers benefit from the efficient resource optimization offered by NPUs. 

• Autonomous vehicles and robotics: From self-driving cars to autonomous aerial vehicles (drones), NPUs add value to autonomous piloting systems through best-in-class parallelism and improved signal processing speeds. Low-latency NPUs are an excellent choice for applications requiring computer vision, and they help autonomous vehicles respond in real-time to sudden traffic and environmental conditions. AI-enabled robotics—from home assistants to automated surgical tools—rely on NPUs to develop the ability to detect, learn from and react to their environments. 

• Edge computing and edge AI: Edge computing and edge AI seek to bring critical data and compute resources physically closer to users. This reduces latency, mitigates energy consumption and bolsters privacy. Requiring less energy and offering a smaller physical footprint, NPUs are becoming a valuable component in edge computing and on-device AI. 

Predating NPUs, GPUs have long been favored for computing tasks requiring performance-intensive parallel processing. Originally designed to handle complex graphics for video games and image/video software, GPUs continue to be used in PC and console gaming, as well as virtual and augmented reality, high-performance computing (HPC), 3D rendering, data centers and other applications. 

Here’s a closer look at some of the most important, modern applications of GPU technology:

• Artificial intelligence (AI), machine learning (ML) and deep learning (DL): Although not specifically designed for AI, ML or DL tasks, GPUs power many leading AI applications—such as IBM’s cloud-native AI supercomputer Vela—that require high-speed parallelism to process large data sets for training. Through parallel processing, GPUs can simulate the decision-making process of the human brain used in ML and DL. 

• Cloud computing: In recent years, cloud computing has become a crucial part of IT infrastructure across all major industries. The ability to offload major computing tasks to powerful servers stored offsite requires immense data processing capabilities. GPUs enable cloud computing infrastructure by accelerating big data analytics and database queries through parallel computing. 

• Visualization and simulation: Purpose-built to process graphics, GPUs add tremendous value across industries for tasks requiring complex visualizations or simulations, including product walkthroughs, engineering CAD drawing, medical imaging and seismic and geophysical modeling. Elsewhere, climate scientists use simulations powered by GPUs to predict weather conditions, while theoretical physicists use them to model the behavior of particles on the quantum level.  

• Blockchain: Blockchain technologies rely heavily on GPU technology, especially when it comes to validating "proof of work." In many widely used blockchain applications, such as the cryptocurrency Bitcoin, proof-of-work computations are performed in order to confirm that any updates made to the overall ledger are accurate. This level of computation is very demanding because it impacts the entire blockchain, and it wouldn’t be possible without modern GPUs.

• Gaming and the metaverse: As the gaming industry continues to skyrocket, so has the demand for better graphics, larger massively multiplayer online (MMO) games, and compute-intensive rendering like the kind that enables virtual and augmented reality games. Game developers and computing manufacturers rely on GPUs to power cutting-edge gaming features, such as high image refresh rates and the advanced ray-tracing used in rendering hyperrealistic environments. 

• Video processing and content creation: Since their introduction, GPUs have steadily reduced frustrating rendering times for popular video editing products including Final Cut Pro and Adobe Premiere. Today, GPUs equipped with integrated NPUs dramatically speed up video creation and editing tasks for everything from the professional editing suites used by major Hollywood studios to smartphone apps used by YouTubers and TikTokers.

NPUs are best used within integrated systems that optimize operations to allocate specific types of resources to specific types of processors. Designed for precise, linear computing, CPUs are best allocated to general-purpose processes such as system and resource management, while GPUs are specialized for intense workloads that benefit from parallel computing.

As artificial intelligence applications become more prevalent, even more specialized NPUs are best deployed as a complement to CPUs and GPUs for handling AI and ML-specific tasks with low-latency and highly energy-efficient parallel processing.