Apple's dedicated AI processor, called the Neural Engine, has become 63 times more powerful over seven years, jumping from 0.6 TOPS (tera operations per second) in 2017 to 38 TOPS in 2024. This means your iPhone or Mac can now run sophisticated language models and image processing tasks without sending data to the cloud, a fundamental shift in how mobile devices handle artificial intelligence .

What Exactly Is the Neural Engine, and Why Does It Matter?

The Neural Engine is a specialized hardware block built directly into Apple's custom chips, separate from the main processor and graphics processor. Unlike general-purpose chips that handle everything from email to gaming, the Neural Engine is purpose-built for one job: running machine learning models with extreme efficiency . When Apple introduced it in the A11 Bionic chip in 2017, it was powerful enough only to unlock your phone with Face ID. Today, it can run transformer-based large language models, the same technology behind ChatGPT, entirely on your device.

This matters because on-device AI means privacy, speed, and reliability. Your data never leaves your phone. There's no network latency waiting for a response from a distant server. And if you're offline, the AI features still work .

How Did Apple Achieve This 63x Performance Jump?

The growth wasn't linear. Apple made strategic leaps at specific moments. The biggest single jump came in 2018 with the A12 Bionic chip, when Apple expanded the Neural Engine from 2 cores to 8 cores and moved to TSMC's 7nm manufacturing process, the first 7nm mobile chip in the industry. This single generation delivered an 8.3x performance increase, transforming the Neural Engine from a niche feature into a platform-wide capability .

By 2020, the A14 Bionic introduced a 16-core Neural Engine architecture that became the template for all future chips. The A17 Pro in 2023 pushed that same design to 35 TOPS on TSMC's cutting-edge 3nm process, approaching the performance of entry-level discrete AI accelerators. The M4 in 2024 reached 38 TOPS, optimized specifically for generative AI workloads .

Apple invested over $20 billion in semiconductor research and development from 2015 to 2026, growing its silicon engineering team from approximately 500 engineers in 2015 to more than 3,000 by 2024 to execute this transition .

What Are the Key Technologies Powering This Growth?

• Advanced Manufacturing Nodes: Apple moved from 16nm processes in 2015 to 3nm in 2023, allowing more transistors in the same physical space. The M2 Ultra contains 134 billion transistors, compared to just 2 billion in the original A9 chip, a 67x increase .
• Unified Memory Architecture: Instead of separate memory pools for the CPU, GPU, and Neural Engine, Apple created a shared high-bandwidth memory system. The M1 Ultra delivers 800 GB/s of unified memory bandwidth, eliminating the data transfer penalties that slow down traditional discrete GPU systems .
• Multi-Die Packaging: Apple's UltraFusion silicon interposer technology connects multiple chip dies at 2.5 TB/s die-to-die bandwidth, enabling massive scaling without sacrificing communication speed. This approach was previously limited to server-class architectures .
• Proprietary Neural Engine Design: While Apple licenses ARM's CPU architecture, the Neural Engine itself is entirely proprietary. Apple has filed 29 core Neural Engine patents between 2018 and 2025, protecting its competitive advantage .
• Memory Supplier Integration: SK Hynix, Samsung Electronics, and Micron provide LPDDR memory modules integrated into Apple Silicon packages. Higher memory bandwidth allows AI models to process large datasets more efficiently as Neural Engine capabilities grow .

How to Optimize Your Device for On-Device AI Performance

• Use Core ML Frameworks: Developers can deploy machine learning models optimized for Apple Silicon using Core ML tools. These models are automatically routed to the Neural Engine for inference workloads, ensuring maximum efficiency without manual configuration .
• Enable Background AI Features: iOS, iPadOS, macOS, and visionOS integrate Core ML frameworks that automatically assign machine learning tasks to the Neural Engine when available. Features like real-time camera processing, live transcription, and predictive system behavior rely on this integration .
• Monitor Thermal Conditions: Apple's Neural Engine improvements emphasize energy efficiency rather than peak wattage. On-device AI demands high compute density within constrained thermal envelopes, so keeping your device cool allows sustained AI performance without throttling .
• Update to Latest OS Versions: Operating system updates optimize how the Neural Engine handles new AI workloads. Staying current ensures you benefit from the latest architectural refinements and software-driven machine learning capabilities .

What Does This Mean for the Future of Mobile AI?

Apple's roadmap through 2026 suggests Neural Engine improvements will focus on architectural refinement, memory compression efficiency, and deeper integration with GPU resources rather than dramatic increases in raw core count alone . Future applications include generative text suggestions, advanced image enhancement, voice isolation, and contextual system automation, all running locally on your device.

The shift from Face ID to on-device language models represents a fundamental change in mobile computing. For the first time, your phone has enough dedicated AI processing power to understand natural language, generate text, and perform complex visual analysis without relying on cloud infrastructure. That's not just a feature improvement; it's a new computing paradigm .

Apple's vertical integration between hardware and software differentiates its approach from competitors. The Neural Engine is not a standalone accelerator card; it is embedded into system-level orchestration, meaning every app can benefit from AI acceleration without developers needing to understand the underlying hardware complexity .