GPU Compute on the Mac Pro: Metal and Beyond

How the Mac Pro's dual Vega II GPUs handle compute workloads and where they fit in the GPU computing landscape.

The Hardware

My Mac Pro has dual AMD Radeon Pro Vega II GPUs, each with 32 GB of HBM2 (High Bandwidth Memory). Together, that is 64 GB of GPU memory with massive bandwidth. These are workstation GPUs designed for sustained compute loads, not gaming.

The Infinity Fabric Link between the two GPUs allows them to share memory and work together on compute tasks, which is unusual for consumer/workstation hardware. This effectively gives you a single 64 GB GPU address space for workloads that support it.

Metal for Compute

Apple's Metal API is the primary way to access GPU compute on macOS. Metal Performance Shaders (MPS) provide optimized implementations of common operations like matrix multiplication, convolution, and image processing. These are the building blocks for machine learning inference and media processing.

For video work, the GPUs accelerate ProRes encoding/decoding, color grading, and effects rendering in Final Cut Pro and DaVinci Resolve. The HBM2 memory bandwidth means large frames can be processed without bottlenecking on memory access.

Machine Learning

The Mac Pro can run machine learning inference workloads through Core ML and Metal. For training, it is limited compared to NVIDIA GPUs because the ML ecosystem (PyTorch, TensorFlow) is built primarily around CUDA. AMD's ROCm framework provides some compatibility, but it is not at parity with CUDA.

For inference, the Mac Pro performs well. Apple has invested heavily in optimizing Core ML for their hardware, and many pre-trained models can be converted to Core ML format and run efficiently on the Vega II GPUs.

The NVIDIA Gap

The elephant in the room is that most GPU compute workloads are optimized for NVIDIA CUDA. The Mac Pro does not support NVIDIA GPUs (Apple and NVIDIA parted ways years ago). This means the Mac Pro is excluded from the dominant GPU computing ecosystem.

For specific Apple-optimized workloads (media processing, Core ML inference, Metal compute), the Mac Pro is excellent. For general-purpose GPU computing (CUDA-based ML training, scientific computing), an NVIDIA-equipped server is the better choice.

My Use

I use the Mac Pro's GPUs primarily for video processing and as a learning platform for Metal compute programming. For anything that needs CUDA, I run it on my Dell servers with passthrough GPUs or on cloud instances. The right tool for the right job.