Apple M4 Chip Deep Dive: The New Engine Powering On-Device AI

Apple M4: A New Era of AI-Centric Mobile Computing

The launch of the Apple M4 chip marks a pivotal moment in mobile computing — and the defining characteristic of this generation is its unprecedented focus on artificial intelligence. As the processor debuting in the latest iPad Pro, the M4 achieves a significant performance leap through an entirely new chip architecture and a more advanced manufacturing process.

Unlike previous generations that emphasized peak CPU benchmark scores, the M4 is designed around enabling devices to run complex AI tasks efficiently on-device. This philosophical shift reflects a broader industry trend: moving away from dependence on cloud computing toward on-device intelligence.

Across-the-Board Performance Upgrades: CPU, GPU, and Neural Engine

CPU: Up to 1.5x Faster Than M2

The M4's CPU delivers up to 1.5x better performance compared to the previous-generation M2. This improvement makes heavy workloads and multitasking noticeably smoother — whether you're running professional creative apps or demanding productivity workflows. For the iPad Pro, which targets professional users, this kind of CPU uplift has immediate, practical value.

GPU: Hardware-Accelerated Ray Tracing

The M4's graphics processing unit (GPU) features an entirely new dynamic architecture, with its most impressive addition being hardware ray tracing acceleration. Previously a feature reserved for high-end discrete GPUs, this capability is now integrated into a mobile SoC — meaning graphics-intensive apps and games can render more realistic lighting, shadows, and reflections.

From an industry perspective, bringing ray tracing to a mobile platform is a significant step for Apple in closing the gap between mobile and desktop-class graphics.

Hardware Ray Tracing is a rendering technique that simulates the physical behavior of light — calculating shadows, reflections, and refractions by tracing how rays travel through a scene. Compared to traditional rasterization, it produces far more realistic global illumination, soft shadows, and mirror-like reflections, but at a substantial computational cost. NVIDIA first introduced dedicated RT Core hardware with its Turing architecture GPUs in 2018, after which it became a hallmark of high-end graphics cards. By integrating ray tracing acceleration units into the M-series chips, Apple ensures these calculations don't compete with general-purpose shader resources, dramatically improving efficiency. This is significant for developers building games and 3D creative apps on the Metal graphics framework, and provides a stronger graphics foundation for Apple's own Reality Pro spatial computing devices.

Neural Engine: 4x Faster, 38 TOPS

The true AI heart of the M4 is its redesigned Neural Engine, which runs up to 4x faster than the M2's — capable of executing up to 38 trillion operations per second (38 TOPS). This level of compute opens the door to more complex, more responsive AI applications: from image analysis to natural language processing, all running locally on-device at high speed.

This isn't just a numbers game. What matters is which AI capabilities can now realistically make it into everyday user experiences.

TOPS (Tera Operations Per Second) is the standard metric for measuring AI accelerator throughput — specifically describing the integer and low-precision floating-point operations (such as matrix multiplications and convolutions) used in neural network inference. 38 TOPS means the M4 Neural Engine can complete 38 trillion such operations every second. For reference, the Qualcomm Snapdragon 8 Gen 2's AI engine delivers approximately 45 TOPS, while Intel's Meteor Lake NPU — designed for PC-based AI workloads — delivers around 11 TOPS. Note that measurement methodologies vary across vendors, so direct comparisons should be made with caution. TOPS also only reflects peak throughput; real-world AI performance depends on precision (INT8/FP16), memory bandwidth, and software optimization. One of Apple's key advantages with the Neural Engine is its deep integration with the Core ML framework, which allows developers to efficiently offload model inference to dedicated hardware — rather than relying on raw compute numbers alone.

Efficiency and Manufacturing: The Advantage of Second-Generation 3nm

The M4 is built on a second-generation 3nm process, achieving excellent power efficiency alongside its dramatic performance gains. In other words, the higher compute doesn't come at the cost of battery life.

For a device like the iPad Pro — where thinness and long battery life are non-negotiable — efficiency improvements matter just as much as raw performance. The advanced process node is both the foundation of Apple's performance lead and the prerequisite for continuing to stack AI compute into mobile devices.

The so-called "second-generation 3nm process" — referred to by Apple and TSMC as N3E — is an optimized, higher-yield iteration of TSMC's original N3 node. The node number (e.g., 3nm) isn't a literal measurement of transistor spacing; it's an industry label for a generational performance tier. Compared to the previous 5nm generation, a 3nm process packs more transistors into the same area, or delivers the same performance at significantly lower power. The M3 series was the first to use first-generation 3nm (N3B), while the M4 upgrades to N3E, which improves process maturity and manufacturing efficiency. This is what enables Apple to fit more compute into the thinner iPad Pro chassis, and is a key physical enabler of the Neural Engine's performance leap.

Broader Implications for the Apple AI Ecosystem

The M4's arrival reinforces Apple's deepening commitment to AI. The benefits extend beyond iPad Pro users — more importantly, it gives developers a hardware platform to build smarter, more innovative applications.

With powerful local compute, generative AI and other AI features can run directly on-device without relying on the cloud. This on-device AI approach offers several compelling advantages: lower latency, stronger privacy protection, and offline availability. For Apple — a company with a strong track record on user privacy — on-device AI is both a technical choice and an extension of its product philosophy.

Questions That Remain

Despite the M4's impressive leap, some questions remain open.

First, when and how Apple will expand the M4 to its broader product lineup is still unclear. Second, in-depth head-to-head comparisons against competing AI chips — such as Intel Lunar Lake and Qualcomm Snapdragon X Elite — will require more real-world benchmark data to properly evaluate.

With the entire industry racing to build on-device AI compute, whether the M4 can establish a meaningful and lasting lead will ultimately be answered by real-world product availability and everyday usage.

Conclusion

Apple's AI-first approach with the M4 represents an important direction for mobile chip design: pushing compute to the edge, balancing performance with efficiency, and laying the groundwork for generative AI applications. A 1.5x CPU uplift, a 4x faster Neural Engine with 38 TOPS, hardware ray tracing, and second-generation 3nm manufacturing together form the chip's competitive foundation. Whether it genuinely transforms the AI experience on iPad Pro and across Apple's ecosystem remains to be seen — but the architectural intent is clear.