Apple’s latest innovation in the world of silicon, the M3 Pro chip, has created quite a buzz in the tech community. With claims of unprecedented speed and efficiency, it is essential to dive into the technical details to understand how this chip differs from its predecessors. In this article, we explore the M3 Pro’s memory bandwidth, core ratios, and Neural Engine performance, shedding light on the changes Apple has made compared to previous models.
Memory Bandwidth
The M3 Pro chip, built on a cutting edge 3-nanometer technology, has certainly made strides in performance. Apple boasts a 40% increase in speed when comparing it to the 16-inch MacBook Pro with the M1 Pro chip. However, a closer look reveals a surprising fact – the M3 Pro chip actually offers 25% less memory bandwidth than the M1 Pro and M2 Pro chips. While the M1 Pro and M2 Pro boast 200GB/s of memory bandwidth, the M3 Pro is capped at 150GB/s.
The M3 Max, on the other hand, is marketed as capable of “up to 400GB/s.” However, this figure becomes more intriguing when we consider that the scaled down M3 Max, featuring a 14-core CPU and 30-core GPU, only offers 300GB/s memory bandwidth, unlike the equivalent M2 Max, which has 400GB/s bandwidth. These changes in memory bandwidth might raise questions about the actual impact on performance in real world usage scenarios.
M3 Pro chip: Altered Core Ratios
Another significant change in the M3 Pro chip is the reconfiguration of core ratios when compared to its predecessor, the M2 Pro. The M3 Pro’s 12-core CPU has 6 performance cores and 6 efficiency cores, in contrast to the M2 Pro’s 8 performance cores and 4 efficiency cores. In terms of the GPU, the M3 Pro features 18 cores, one core less than the M2 Pro.
These changes in core ratios might appear counterintuitive at first glance, but they have the potential to impact performance across various applications and tasks. Apple’s reasoning behind these changes remains a subject of curiosity and debate.
M3 Pro chip: Neural Engine Performance
The Neural Engine is a critical component of Apple’s chips, crucial for features like computational photography and Face ID. The M3 chip, despite boasting a 16-core Neural Engine, falls short in terms of maximum achievable throughput when compared to the A17 Pro Neural Engine, which debuted in the iPhone 15 Pro series. The A17 Pro Neural Engine can achieve 35 TOPS (trillions of operations per second), while the M3 Neural Engine maxes out at 18 TOPS.
It’s worth considering that the iPhone 15 Pro may require a higher-performing Neural Engine due to its demanding features, while the M3 might compensate with its additional GPU cores.
Understanding the implications of this difference in Neural Engine performance is crucial to assess how the M3 chip will perform in various machine learning tasks and AI applications.
While Apple has presented impressive performance gains with the M3 Pro chip, the real-world implications of these changes remain somewhat opaque. Apple’s introduction of Dynamic Caching memory allocation technology adds another layer of complexity to performance evaluation, as it dynamically allocates memory based on specific tasks, optimizing efficiency.
Apple’s marketing strategy, focusing on comparisons with the M1 Pro and M1 Max rather than the more recent M2 variants, adds to the mystery surrounding the M3’s real-world performance. The absence of comprehensive third-party benchmarks further complicates the assessment of this new chip.
The release of the M3 Pro chip in the new MacBook Pro models represents a significant leap in Apple’s silicon technology. While memory bandwidth, core ratios, and Neural Engine performance have seen alterations compared to previous models, the actual impact on real-world performance remains a subject of debate. As the new MacBook Pro models become available, we eagerly await thorough third-party benchmarks to unravel the true potential of the M3 Pro chip.
