What to Know About AI Hardware Accelerators: NPUs, TPUs, and Beyond
AI hardware accelerators are specialized processors that handle AI workloads faster and more efficiently than general-purpose CPUs. The three main types—NPUs for on-device AI in laptops and PCs, TPUs for large-scale machine learning in the cloud, and GPUs for versatile AI development and graphics rendering—each serve different users and different needs. Understanding how they differ will help you determine which one is right for you, or whether you need one at all.
What Is Hardware Acceleration?
Hardware acceleration is the use of specialized processors to perform AI computing tasks faster and more efficiently than a general-purpose CPU. While CPUs handle a wide variety of tasks using structured data (such as files and databases with specific formats), AI workloads require processing massive amounts of unstructured data like text and images. This fundamental difference is why AI computing demands purpose-built hardware.
AI accelerators—including NPUs, TPUs, and GPUs—are optimized for application-specific workloads where the same mathematical operation must be performed trillions of times simultaneously. A standard CPU simply cannot match this level of parallel efficiency for AI tasks.
What Is an NPU (Neural Processing Unit)?
An NPU, or Neural Processing Unit, is a specialized microprocessor designed to accelerate on-device AI tasks. NPUs are built into AI PCs, AI laptops, and mobile devices, enabling them to run AI workloads locally without relying on cloud servers.
With an NPU-equipped device, you can:
- Take full advantage of Windows 11 AI features and use Microsoft Copilot efficiently, including real-time transcription and voice commands
- Make AI-enhanced video calls with features like background blur and auto-framing
- Work on images, videos, and text using on-device AI tools for photo editing, video enhancement, and more
How Does Edge AI Processing Work with NPUs?
Devices without an NPU can still perform some AI tasks, but they must send workloads to remote servers (cloud processing) and relay the responses back to the user. A device with an NPU handles these tasks locally using hardware acceleration. This is called edge AI processing: the inference phase happens on the device itself rather than in the cloud, meaning the AI model runs locally.
Edge AI processing delivers faster response times, better privacy (since data stays on-device), and the ability to use AI features without an internet connection.
What Does TOPS Mean for NPU Performance?
NPU architecture is based on parallel processing: it repeats the same mathematical operation trillions of times while moving data as little as possible. NPU performance is measured using the TOPS metric, which stands for "trillions of operations per second." For example, 1 TOPS means the NPU can perform one trillion operations per second, while 99 TOPS means it can execute ninety-nine trillion operations per second.
Think of TOPS as the NPU's equivalent of horsepower. For most users, 40 TOPS is sufficient, and it is the minimum value required for a device to earn Copilot+ certification.
NPU TOPS by Device:
| Model | CPU | NPU |
|---|---|---|
| HP EliteDesk 8 Mini G1a Desktop AMD |
AMD Ryzen™ AI 5 PRO 340
|
59 TOPS
|
| HP OmniBook 5 Laptop Snapdragon |
Snapdragon® X Plus
|
45 TOPS
|
| HP OmniBook X Intel |
Intel® Core™ Ultra 7 256V
|
47 TOPS
|
Keep in mind that a high TOPS value alone does not guarantee optimal AI performance. Factors like system integration, available memory bandwidth, and the quality of software optimization also play a role. The TOPS you need will vary based on whether your priority is energy efficiency, stable model behavior, or raw compute strength. While 40 TOPS is currently sufficient for Windows 11 AI features, consider your specific use case when choosing a device.
What Is a TPU (Tensor Processing Unit)?
A TPU, or Tensor Processing Unit, is an AI accelerator chip developed by Google and designed specifically for machine learning workloads. TPUs are optimized for tensor mathematics—large matrix multiplications and accumulations—which are the core operations behind training and running AI models.
What Is the Difference Between AI Training and Inference?
Training is the process of teaching an AI model by feeding it data, making predictions, measuring accuracy, and repeating this cycle millions of times. Inference is the process of using a trained model to produce results. TPUs are designed for both training and inference, while NPUs are used only for inference.
TPUs are used in data centers, machine learning training environments, and large-scale inference processes. Because of this, they are not found in consumer devices. If you are not working in data center operations or enterprise machine learning, you do not need a TPU.
TPU vs. GPU: How Do They Compare for Machine Learning?
| Feature | GPU | TPU |
|---|---|---|
| Primary Goal |
Parallel computing
|
Tensor math acceleration
|
| Use Case |
Machine learning and graphics rendering
|
Machine learning only
|
| Strength |
Massive parallel computing power
|
High efficiency for matrix multiplications
|
| Best For |
Custom AI models and mixed workloads
|
Large-scale deep learning
|
| Energy Efficiency |
Medium
|
Excellent
|
| Cloud Availability |
Wide (AWS, Azure, and others)
|
Google Cloud
|
How Are GPUs Used for AI Workloads?
GPUs, or Graphics Processing Units, were originally developed for graphics rendering, but their parallel processing capabilities also make them well-suited for AI computing. For example, the CUDA cores in NVIDIA GPUs divide tasks into smaller sub-tasks and execute them simultaneously, which is ideal for the repetitive mathematical operations that AI workloads require.
GPUs are a strong choice for AI tasks in the following scenarios:
- Training AI models, which involves repeating the same calculations millions of times
- Analyzing images, videos, or audio files, which are uniform data types that can be processed in parallel
- Developing applications based on large pre-trained models (such as a ChatGPT-style assistant), where massive matrix multiplications are the core operation
However, GPUs consume significantly more energy than NPUs. If high energy consumption is not a concern, you do not need low-latency edge processing, and you are working with large models, GPUs are a strong choice. For maximum performance and stability, consider the HP Elite AI PC Desktops collection and HP workstations, which feature NVIDIA GPUs.
NPU vs. GPU: Which Is Better for Consumer AI Tasks?
| Feature | NPU | GPU |
|---|---|---|
| Primary Goal |
Edge (on-device) AI
|
Parallel computing
|
| Strength |
Low-latency inference with low power consumption
|
Parallel computing power and flexibility
|
| Best For |
Always-on background AI
|
Coding or graphics-adjacent AI
|
| Energy Efficiency |
Excellent
|
Medium
|
| Battery Impact |
All-day battery life
|
Drains battery quickly
|
NPU vs. TPU vs. GPU: Key Differences at a Glance
| Accelerator Type | Best For | Key Advantages | Typical Use Cases | Availability |
|---|---|---|---|---|
|
NPU
Neural Processing Unit
|
On-device AI inference
|
Power efficiency, low latency, privacy
|
AI PCs, laptops, real-time features
|
Consumer devices
|
|
TPU
Tensor Processing Unit
|
Large-scale ML training and inference
|
Tensor operation optimization, scalability
|
Data centers, cloud AI, research
|
Cloud services
|
|
GPU
Graphics Processing Unit
|
Versatile AI workloads
|
Flexibility, broad software support
|
Gaming, content creation, ML development
|
Consumer to professional
|
Which AI Accelerator Do You Need?
The right AI accelerator depends on your use case:
- Everyday productivity and AI features → An NPU-equipped AI PC, such as the HP OmniBook Ultra
- Professional content creation and ML development → A GPU-equipped workstation, such as the HP ZGX Nano G1n
- Enterprise ML deployment → Cloud TPU access
If you want an AI assistant that responds to voice commands, need to edit photos and videos on-device, and want full access to Windows 11 AI features, an AI PC with an NPU is the right choice. If you are developing software, working with machine learning models, or using professional content creation tools that leverage CUDA cores (such as Adobe AI), a GPU-equipped workstation is the better option. For enterprise-level machine learning workloads, TPUs provide the scale and efficiency you need.
Frequently Asked Questions About AI Hardware Accelerators
Do I need an NPU in my laptop?
You need an NPU if you want AI tasks to be processed directly on your device rather than in the cloud. This is called edge AI processing, where the AI model runs locally on your PC, laptop, or mobile device. On-device processing provides faster response times, improved privacy, and offline AI capability.
Can a GPU replace an NPU for AI tasks?
A GPU can handle some AI tasks that an NPU performs, but only in specific scenarios—such as when developing software that requires millions of repeated calculations. GPUs are not an ideal replacement for everyday AI use because they consume significantly more energy and are not optimized for the low-latency, always-on background processing that NPUs handle efficiently.
What does TOPS mean, and how much do I need?
TOPS stands for "trillions of operations per second" and measures the number of operations an NPU can execute each second. For most consumers, 40 TOPS is a sufficient baseline. This is also the minimum value required for Copilot+ PC certification.
Are TPUs available in consumer laptops?
No. TPUs are purpose-built for enterprise-level machine learning tasks and are used exclusively in data centers and cloud environments. They are not available in consumer devices.
Take Advantage of AI Acceleration Today
AI accelerators are purpose-built for different scales and use cases: NPUs bring AI to everyday computing, GPUs offer versatility for development and creative work, and TPUs serve enterprise machine learning needs. AI acceleration is rapidly becoming a standard feature in consumer devices. Explore HP's AI PC lineup to experience next-generation computing with integrated NPU technology and bring the power of AI to your everyday and professional life.
