Neural Processing Units (NPUs), also called Neural Engines, are dedicated chips designed to accelerate machine learning tasks directly on your device without sending data to the cloud. Apple introduced the Neural Engine in 2017 with the A11 Bionic chip, and the technology has since become the foundation for on-device artificial intelligence across iPhones, iPads, and Macs. Today, every major chipmaker from Qualcomm to Samsung to MediaTek is racing to build their own NPU, signaling a fundamental shift in how consumer devices handle AI workloads.

What Exactly Is a Neural Engine, and Why Should You Care?

The Neural Engine is Apple's branded name for a Neural Processing Unit, a piece of specialized hardware built into the main processor that handles artificial intelligence and machine learning tasks. Unlike your device's main CPU (central processing unit), which handles general computing, the Neural Engine is optimized specifically for the mathematical operations that power AI models. This separation allows your phone or Mac to run complex AI features like Face ID recognition, photo search, and real-time language processing without draining your battery or slowing down everyday tasks.

Apple's Neural Engine handles several critical functions on your device:

• Image Processing: The Photos app uses the Neural Engine for face detection, scene recognition, and visual search without uploading your images to Apple's servers.
• Speech and Language: Siri dictation, keyboard predictions, and live captions all run locally on the Neural Engine, keeping your voice data private.
• Camera Features: Portrait mode, Night mode, and Deep Fusion computational photography all rely on Neural Engine calculations to enhance your photos in real time.
• Apple Intelligence: Apple's new personal intelligence system announced in June 2024 depends heavily on the Neural Engine to run on-device language models without cloud processing.

The performance gains have been staggering. Apple's first Neural Engine in the A11 Bionic chip, released in 2017, could perform about 600 billion operations per second. The latest M4 Neural Engine reaches 38 trillion operations per second, a 60-fold increase in just seven years. To put that in perspective, that's enough computing power to run billion-parameter language models directly on your device, something that would have required a cloud connection just a few years ago.

How Has the Neural Engine Evolved Across Apple's Product Line?

The Neural Engine's journey reveals Apple's long-term commitment to on-device AI. Every iPhone since the iPhone 8 has included a Neural Engine, and when Apple introduced its custom silicon for Mac computers in 2020 with the M1 chip, it brought a 16-core Neural Engine to the desktop for the first time. This meant that MacBook users could finally run the same on-device AI features that iPhone users had enjoyed for years.

The progression shows a clear pattern of increasing capability:

• 2017 Launch: The A11 Bionic Neural Engine debuts in iPhone 8 and iPhone X, capable of 600 billion operations per second for basic face recognition and photo analysis.
• 2020 Expansion: The M1 Mac brings a 16-core Neural Engine to laptops and desktops, enabling professional creative applications to use on-device AI acceleration.
• 2024 Peak Performance: The M4 Neural Engine reaches 38 trillion operations per second, enabling full-featured on-device language models and Apple Intelligence features.

All current MacBooks and Mac desktops with Apple Silicon, including the M1, M2, and M3 families, include a 16-core Neural Engine. However, older Intel-based Macs lack this hardware entirely, which means users with pre-2020 Macs cannot access any Neural Engine features or Apple Intelligence capabilities. This creates a clear divide between Apple's AI-capable devices and older hardware.

Why Are Competitors Building Their Own Neural Engines?

Apple's success with the Neural Engine has triggered an industry-wide response. Qualcomm's Snapdragon processors include the Hexagon NPU, Samsung integrates NPUs into its Exynos chips for Galaxy devices, and MediaTek has built NPUs into its Dimensity line of processors. Google designs its own Tensor Processing Unit (TPU) for Pixel phones. This isn't coincidence; it's a recognition that on-device AI has become a core feature that consumers expect.

The competitive landscape shows different approaches to the same problem. Qualcomm's Hexagon NPU is optimized for Android devices and runs through Qualcomm's Neural Processing SDK. MediaTek's NPU, found in Dimensity chips, focuses on camera AI tasks, voice recognition, and gaming optimizations, with performance claims exceeding 4 trillion operations per second on flagship designs. Samsung takes a dual-NPU approach in its Exynos processors, handling camera and voice tasks separately for better parallelization.

What's striking is that Apple's per-core efficiency tends to lead in first-party benchmarks, but competitors are catching up fast. For consumers, this means on-device AI features that once differentiated premium phones will soon become standard across all flagship devices. The privacy argument for local processing, which was once Apple's unique selling point, may become less distinctive as the entire industry moves toward on-device AI.

How to Maximize Your Device's Neural Engine Performance

If you own an Apple device with a Neural Engine, there are practical ways to take advantage of this hardware:

• Enable Apple Intelligence Features: On compatible devices, turn on Apple Intelligence in Settings to unlock on-device language models, image generation, and writing tools that run entirely on your Neural Engine without cloud processing.
• Use Native Apple Apps: Apps like Photos, Siri, and Mail are optimized to use the Neural Engine for tasks like face detection, voice recognition, and smart suggestions, delivering faster performance and better privacy.
• Upgrade to Apple Silicon for Creative Work: If you use professional applications like Pixelmator Pro, DaVinci Resolve, or Adobe Lightroom, switching to an Apple Silicon Mac will give you Neural Engine acceleration for tasks like object masking, image upscaling, and subject selection.
• Check Device Compatibility: Verify that your device has Apple Silicon (M1 or later for Mac, A11 or later for iPhone) to ensure you have a Neural Engine and can access on-device AI features.

The broader chip industry is also investing heavily in NPU infrastructure. A 19-company coalition within the Open Compute Project has unveiled a plan for standardized silicon photonics-ready infrastructure for AI systems, and data-center network fabrics are undergoing architectural shifts as optical interconnects assume a larger role in AI-cluster connectivity. This suggests that NPU technology will extend beyond consumer devices into enterprise and data-center environments.

What Does the Future Hold for Neural Engines?

The trajectory is clear: every flagship processor will eventually include an NPU. Apple's M4 Neural Engine reaching 38 trillion operations per second represents the current performance ceiling, but each generation widens the gap between older and newer devices. This creates a cycle where older devices become obsolete for on-device AI tasks, pushing users toward upgrades.

The stakes are high for Apple's strategy. Apple Intelligence, the company's new personal intelligence system, relies heavily on the Neural Engine to run on-device foundation models. For complex requests that exceed the Neural Engine's capabilities, Apple Intelligence can fall back to Private Cloud Compute while still protecting user privacy. But this fallback option only works if the Neural Engine can handle the majority of tasks locally.

As competitors close the performance gap and on-device AI becomes a standard feature rather than a differentiator, the real competition will shift to software and ecosystem integration. Apple's closed approach to Neural Engine optimization through its Core ML framework and proprietary neural network tools may limit third-party adoption, but the hardware trend is unmistakable. The Neural Engine has evolved from a smartphone secret weapon into the brain of every Apple Silicon device, and the rest of the industry is following the same path.