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 and paid 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
Google Colab (Paid, A100 GPU): Cloud-based NVIDIA A100 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 5950X, 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
Google Colab (Paid, A100 GPU)	-	1.36

Results Analysis

1. GPU Acceleration is Essential for Training Speed

The results confirm, unsurprisingly, that GPU acceleration is necessary for deep learning, with massive speed improvements over CPU training. Even a high-performance CPU like the Ryzen 9 5950X takes significantly longer than any GPU. Training purely on a CPU is impractical for most workflows, especially as model sizes grow.

2. NVIDIA A100: The Benchmark

The Google Colab Paid Tier A100 GPU has remarkable training time, making it the fastest option by a wide margin. This demonstrates the advantage of high-end data center GPUs for deep learning workloads. Compared to the RTX 4060 TI, the A100 is roughly 4.7 times faster, highlighting its suitability for offloading to the cloud for large-scale training.

3. MacBook GPUs Offer Competitive Performance

Apple's Metal GPU acceleration holds up well. The M3 Pro's 18-core GPU completes training in 13.35 seconds, slower than the RTX 4060 Ti but within a reasonable range for practical machine-learning prototyping and research tasks. Even the M1 MacBook Air achieves 37.38 seconds, much faster than any CPU-based training.

For researchers and developers in the Apple ecosystem, MacBooks with higher-end GPU configurations offer a viable alternative for prototyping and small-to-medium deep learning tasks.

4. Google Colab Provides Accessible GPU Options

Google Colab remains a valuable option for machine learning practitioners who do not have a dedicated deep learning rig. The free-tier T4 GPU performs close to an RTX 4060 Ti, making it a great resource for experiments and smaller models.

However, the A100 GPU available in the Colab Paid Tier is an extremely powerful option for those needing serious performance. It provides nearly 5x the speed of a high-end consumer GPU like the RTX 4060 Ti, making it ideal for large-scale deep learning workloads.

5. RAM Matters for MacOS Metal Training

One observed limitation on MacBooks, especially the M1 MacBook Air (8GB RAM), was high swap usage when training on the GPU. Because Apple Silicon uses unified memory, having at least 16GB or 24GB RAM is recommended to avoid performance bottlenecks. Insufficient memory leads to slower performance and potential training instability.

Conclusion

You don’t need high-end hardware to start learning machine learning. While dedicated deep-learning rigs with powerful GPUs offer the best performance, cloud-based solutions like Google Colab provide an accessible alternative. Even the free tier with a T4 GPU delivers good performance, making it a great option for experimentation and smaller models. If you need top-tier, cutting-edge power, you can always rent an A100 GPU.

Apple Silicon GPUs are surprisingly competitive for deep-learning tasks for MacBook users, especially with higher-end models like the M3 Pro. While they don’t match NVIDIA GPUs in absolute performance, they are still much faster than CPU training and well-suited for prototyping and research.

Ultimately, the best setup depends on your needs. If you require maximum performance, an NVIDIA GPU or a cloud-based A100 is the best choice. But if you’re just getting started or working on smaller models, a MacBook or Google Colab can be more than sufficient. In conclusion, you don’t need expensive hardware to learn and experiment with machine learning.

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