NVIDIA vs MacOS Metal GPU: Performance Benchmark for AI/ML

When choosing a device for machine learning, one of the biggest factors to consider is hardware acceleration. GPUs drastically improve the speed of training deep learning models, making them essential for any serious machine learning workflow. However, the choice of GPU depends on the platform, NVIDIA's CUDA ecosystem has been the industry standard, but Apple's Metal framework has emerged as an alternative for Mac users.

For those wondering whether a MacBook with an Apple GPU can compete with an NVIDIA-powered deep learning rig, this benchmark compares performance across different setups, including a Windows desktop (using Ubuntu Bash) with an NVIDIA GPU, MacBooks with Apple Silicon GPUs, and Google Colab's free cloud GPUs.

By running the same deep learning model on these different hardware configurations, we can evaluate their training speed and see how practical each option is for machine learning workloads.

Test Setup and Architecture

The benchmark uses a convolutional neural network (CNN) trained on synthetic image data to compare different devices fairly. The CNN architecture includes five convolutional layers, similar to those commonly used in research and prototyping.

The model is trained using PyTorch on a batch of 512 RGB images, each with a resolution of 64x64 pixels. Training is performed for 50 epochs, measuring the total training time for each device.

The test covers the following hardware setups:

• Desktop / Deep Learning Rig: AMD Ryzen 9 5950X, NVIDIA RTX 4060 Ti 16GB  
• MacBook Pro M3 Pro: 12-core CPU, 18-core GPU  
• MacBook Air M1: 8-core CPU, 7-core GPU  
• Google Colab (Free Tier): 1-2 CPU cores, NVIDIA T4 GPU  

The benchmark runs each test three times, averaging the results to account for any variability in performance.

Performance Results

The results below show the total training time for 50 epochs on each device, with tests done on CPU and GPU.

Device	CPU Time (sec)	GPU Time (sec)
Desktop (Ryzen 9, RTX 4060 Ti 16GB)	173.58	6.48
MacBook Pro M3 Pro (12C CPU, 18C GPU)	110.27	13.35
MacBook Air M1 (8C CPU, 7C GPU)	216.94	37.38
Google Colab (Free, T4 GPU)	1260.44	8.45

Results Analysis

1. GPU Acceleration is Essential for Training Speed

The most striking, and probably already expected, takeaway is that GPU acceleration is several times faster than CPU training. Even a high-performance CPU like the Ryzen 9 5950X takes much longer than an NVIDIA or Apple GPU. Training deep learning models on a CPU alone is impractical for most workflows, especially as model sizes increase.

2. MacBook GPUs Offer Competitive Performance

Apple's Metal GPU acceleration performs surprisingly well. The M3 Pro's 18-core GPU completes training in 13.35 seconds, more than twice as slow as the RTX 4060 Ti but still fast enough for practical machine learning work. Even the M1 MacBook Air, with only 7 GPU cores, is significantly faster than CPU-only training.

For users already in the Apple ecosystem, MacBooks with higher-end GPU configurations provide a viable alternative to NVIDIA GPUs, especially for prototyping and smaller-scale projects.

3. Google Colab Provides an Accessible Alternative

The Google Colab Free Tier T4 GPU offers solid performance, training the model in 8.45 seconds. This is close to what a local NVIDIA desktop GPU can achieve. While Colab has limitations, such as session timeouts and limited GPU availability, it remains a good option for those who do not have a dedicated deep-learning rig.

4. RAM Matters for MacOS Metal Training

One limitation observed on the MacBook Air M1 (8GB RAM) was high swap usage when training on the GPU. Since Apple Silicon uses unified memory, having a higher RAM configuration is critical for performance. The 16GB or 24GB RAM options on newer MacBooks are recommended to avoid bottlenecks.

Conclusion

For those considering which machine to use for machine learning:

• A dedicated NVIDIA GPU is still the fastest option for deep learning workloads. If speed is a priority, a deep learning rig with an NVIDIA GPU will outperform MacBooks.
• MacBooks with Apple Silicon GPUs are viable alternatives, especially higher-end configurations like the M3 Pro. While they are not as fast as NVIDIA GPUs, they still provide a significant speedup over CPU training.
• Google Colab is a great option for cloud-based training. Even the free-tier T4 GPU performs similarly to a local deep-learning rig.

Ultimately, the right choice depends on budget, portability, and use case. A MacBook can be a compelling choice for those working on research or smaller models. NVIDIA GPUs or cloud-based solutions like Colab remain the best options for large-scale training.