CUDA Support for Apple Silicon (M1 / M2 / M3 / M4)

Why CUDA Is Incompatible with Apple Silicon

• Hardware difference: M1 chips use Apple's integrated ARM GPU with unified memory, not NVIDIA discrete GPUs.


• Driver support: Apple discontinued all NVIDIA driver support in macOS starting with macOS 10.14 (Mojave).


• eGPU limitations: Apple Silicon Macs do not support external GPUs (eGPUs) over Thunderbolt.

Alternative GPU Acceleration Methods for M1 Mac

To run deep learning and accelerated computing on an M1 Mac, use the native frameworks listed below.

1. Apple Metal (MPS) in PyTorch

PyTorch includes built-in acceleration for Apple Silicon via the Metal Performance Shaders (MPS) backend.

import torch

# Check if Apple Silicon GPU is available
if torch.backends.mps.is_available():
    device = torch.device("mps")
    print("Using Apple Silicon GPU via Metal (MPS)")
else:
    device = torch.device("cpu")
    print("Using CPU")
# Move tensors and models to Apple GPU
x = torch.randn(1000, 1000, device=device)
y = torch.matmul(x, x)

2. TensorFlow with Apple Metal Plugin

# Install Apple Metal plugin for TensorFlow in Terminal:
# pip install tensorflow-macos tensorflow-metal

import tensorflow as tf
print("Physical GPU Devices:", tf.config.list_physical_devices('GPU'))

3. MATLAB on Apple Silicon

MATLAB runs natively on Apple Silicon (ARM64). However, MATLAB functions that explicitly require CUDA (such as gpuArray and CUDA MEX files) do not run on Apple GPUs. MATLAB uses multi-threaded CPU SIMD instructions (NEON/Accelerate framework) for local computation on macOS.

Solutions When You Strictly Need CUDA