How to Test Your NPU's Performance and Capabilities

Microsoft now requires AI PCs to have at least 40 TOPS for their Copilot+ experience, highlighting the growing importance of neural processing units (NPUs). If you have a modern laptop or desktop, you might be wondering how to test your NPU to see if it's meeting performance expectations.

Quick Summary

This guide covers methods for checking your NPU's presence and performance, from using Windows Task Manager and Device Manager to employing specialized benchmark software like Geekbench AI. It explains how to evaluate NPU capabilities and provides context on performance metrics.

Key Points

• System Check First: Use built-in Windows tools like Task Manager and Device Manager to confirm your NPU is present and recognized by the operating system.

• Leverage Benchmarking Software: Run dedicated NPU benchmarks such as Geekbench AI or Burnout Benchmark to obtain reliable performance metrics under simulated AI workloads.

• Analyze Key Performance Metrics: Pay attention to metrics like TOPS (Tera Operations Per Second) for raw speed and TOPS/W for power efficiency, especially for mobile devices.

• Ensure Software Compatibility: Verify your NPU drivers are current and that you are using AI applications or benchmarks compatible with your hardware's software framework, such as Intel's OpenVINO or AMD's Ryzen AI.

• Evaluate for Real-World Tasks: Test your NPU using applications that model actual AI tasks, like image processing or natural language processing, to gauge its practical performance.

• Monitor Thermals During Stress Tests: If using a stress-testing tool, monitor temperatures to ensure your system can sustain AI workloads without severe thermal throttling.

Step 1: Confirming the Presence of an NPU

Before you can test your NPU, you need to verify that your system has one. NPUs are typically integrated into modern CPUs, including Intel Core Ultra, AMD Ryzen AI, and Qualcomm Snapdragon X series processors. For Windows 11 users, there are two straightforward methods to confirm its existence.

Using Task Manager

The simplest method is to use the Windows Task Manager. This tool provides a quick overview of your system's performance, including dedicated hardware for AI processing.

1. Right-click the Start button on the taskbar and select Task Manager.
2. Click the Performance tab. You may need to click 'More details' if the window is minimized.
3. Look for a dedicated NPU entry alongside your CPU, GPU, and Memory. It may be listed as 'NPU Intel AI Boost' or 'AMD IPU Device'.

Using Device Manager

For a more detailed look, the Device Manager can also confirm your NPU and its driver status.

1. Right-click the Start button and select Device Manager.
2. Scroll down and expand the Neural processors category.
3. Here you should see your NPU listed, such as 'Intel(R) AI Boost' or 'AMD IPU Device'. If the category isn't there, your system likely doesn't have an integrated NPU.

Step 2: Benchmarking Your NPU with Third-Party Tools

Once you've confirmed your NPU's presence, benchmark software can provide detailed metrics on its performance under different AI workloads. These tools simulate real-world AI tasks to give you a comprehensive performance score.

Geekbench AI

This cross-platform benchmark suite is one of the most popular tools for measuring AI performance across different hardware, including CPUs, GPUs, and NPUs.

• How it works: Geekbench AI runs a series of ten AI workloads, mimicking tasks like object detection and image recognition, to provide detailed performance scores.
• Cross-platform comparison: It runs identical workloads on Windows, macOS, Android, and iOS, allowing for direct comparison of NPU performance across devices.
• Metrics: It provides multi-dimensional scores based on different data types (Single Precision, Half Precision, and Quantized), giving a nuanced view of your NPU's capabilities.

Burnout Benchmark

For a more intense, stress-oriented test, the Burnout Benchmark is an excellent choice. It pushes your system's components to their limits to evaluate performance under heavy load.

• What it measures: The tool evaluates performance sustainability, thermal throttling behavior, and power efficiency under a full 100% load on the CPU, GPU, and NPU.
• Advanced options: The Pro version offers more in-depth testing, allowing you to examine how different components interact under simultaneous, sustained workloads.
• Platform: Primarily targeted at mobile SoCs and available on Android, it is also useful for testing NPUs on other platforms.

AI-Benchmark

Another robust option, AI-Benchmark is a Python library and mobile app designed for evaluating the AI performance of various hardware platforms. It relies on the TensorFlow machine learning library.

• Versatile workloads: AI-Benchmark uses a wide range of tasks, including photo reconstruction, video super-resolution, and natural language processing (NLP) tasks, to generate performance metrics.
• Professional focus: It provides precise and lightweight solutions for assessing both inference and training speed for key ML models, which is valuable for developers and enthusiasts.

Comparison Table: NPU Performance Metrics and Tools

Understanding the output from NPU benchmarks requires familiarity with key metrics. This table breaks down common terms and compares the benchmark tools discussed.

Metric/Tool	Description	Primary Application	Best For	Limitations
TOPS (Tera Operations Per Second)	Measures the raw processing power of the NPU, representing trillions of operations per second.	Theoretical peak performance assessment.	Quick hardware comparison based on manufacturer claims.	Doesn't account for software optimization, memory bandwidth, or real-world workload efficiency.
TOPS/W (TOPS per Watt)	Measures the power efficiency of the NPU, indicating how many operations it can perform per unit of power consumed.	Assessing energy efficiency, crucial for battery-powered devices.	Mobile devices, edge computing, and laptops where power draw is critical.	Doesn't tell the whole story; a higher TOPS/W doesn't guarantee better overall performance.
Geekbench AI	Cross-platform benchmark running real-world AI tasks to score NPU, CPU, and GPU performance.	Overall AI performance scoring and cross-platform hardware comparison.	Consumers and reviewers who want an easy-to-understand comparative score.	Scores are synthetic and may not perfectly reflect performance in every specific AI application.
Burnout Benchmark	Stress test that loads CPU, GPU, and NPU to measure thermal throttling and performance sustainability.	Evaluating long-term performance under sustained, heavy AI workloads.	Hardware enthusiasts and developers testing system stability and thermal management.	Can cause overheating if not monitored closely.
AI-Benchmark	Python library and app using TensorFlow to measure inference and training speeds on various AI models.	In-depth, model-specific AI performance analysis.	Developers and researchers who need granular control over testing specific models.	Primarily for Python/TensorFlow; requires more technical knowledge to run effectively.

Conclusion

Testing your NPU is a multi-step process, beginning with basic system checks and progressing to more sophisticated software analysis. Tools like Windows' built-in Task Manager and Device Manager offer a quick and easy way to confirm your NPU's presence. For a deeper understanding of its raw power, efficiency, and real-world performance, benchmark applications like Geekbench AI, Burnout Benchmark, and AI-Benchmark provide crucial data. By combining these methods, you can gain a clear picture of your hardware's AI capabilities and ensure your AI PC is performing as expected. As AI integration expands, understanding and testing your NPU will become a standard part of evaluating your device's overall performance. For developers, accessing and testing NPU functionality often requires specialized APIs from hardware manufacturers like Intel's OpenVINO or AMD's Ryzen AI Software, allowing for targeted workload optimization.

Note: Benchmarking results can vary significantly depending on the specific hardware, drivers, and software frameworks used. Always ensure your drivers are up-to-date for accurate testing.

Key Takeaways

• Verify with System Tools: Use Task Manager or Device Manager in Windows to quickly confirm if your computer has a dedicated NPU listed under the 'Performance' tab or 'Neural processors' category.
• Benchmark with Specialized Software: For a comprehensive performance test, use cross-platform applications like Geekbench AI to run standardized AI workloads.
• Stress Test for Stability: Use tools like Burnout Benchmark to evaluate NPU performance under prolonged, heavy load and test for thermal throttling.
• Understand Key Metrics: Interpret benchmark results by understanding metrics like TOPS (raw speed) and TOPS/W (power efficiency), which provide different perspectives on performance.
• Driver and Software Matter: Ensure your NPU drivers and AI software frameworks are up-to-date, as they significantly impact performance and testing results.
• Compare Across Platforms: Geekbench AI allows for meaningful comparisons of NPU performance between different operating systems and hardware.
• Consider Workload Type: Different benchmarks use varying AI workloads, so choose a tool that best reflects your intended use case, whether for general AI tasks or specific deep learning models.

Frequently Asked Questions

What is an NPU and why is it important?

An NPU, or Neural Processing Unit, is a specialized processor designed to accelerate artificial intelligence (AI) and machine learning tasks with high efficiency and low power consumption. This is crucial for real-time, on-device AI applications like background blur during video calls or advanced photo editing.

How do I check if my Windows 11 PC has an NPU?

You can check for an NPU on Windows 11 by opening the Task Manager, navigating to the Performance tab, and looking for an NPU entry alongside your other hardware. Alternatively, check the Device Manager under the 'Neural processors' category.

What are the best benchmark tools for testing an NPU?

For comprehensive AI performance testing, consider Geekbench AI for cross-platform comparison, Burnout Benchmark for stress testing, and AI-Benchmark for in-depth analysis using TensorFlow workloads.

Is an NPU more important for a laptop or a desktop?

While beneficial for all AI-enabled PCs, NPUs are particularly important for laptops and other mobile devices due to their high energy efficiency. They handle AI tasks without draining the battery excessively, making them ideal for on-the-go AI processing.

What is the difference between an NPU and a GPU for AI tasks?

While both can handle parallel processing for AI, NPUs are purpose-built for AI tasks and are more energy-efficient for inference, especially in edge devices. GPUs are more general-purpose and used for a broader range of parallel computing, including AI model training.

What is TOPS and how is it used to measure NPU performance?

TOPS stands for Tera Operations Per Second and is a metric for measuring the raw, theoretical processing power of an NPU. However, it does not account for real-world efficiency, so complementary metrics like TOPS/W and actual benchmark scores are also important.

Why would my NPU not appear in Task Manager?

If your NPU is not visible in Task Manager, it could be due to outdated drivers, a disabled setting in the BIOS, or because your system is older and lacks the necessary integrated hardware. Ensuring your system and drivers are updated is a good first step.