Unverified60% confidenceFactExact time
Starting from version 1.12, PyTorch officially supports the MPS (Metal Performance Shaders) backend, enabling GPU acceleration on Apple Silicon.
2
Sources
60%
Confidence
Long-term
Relevance
8/25/2026
First Seen
Sources
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Related Claims
UnverifiedApple's Metal Performance Shaders (MPS) backend has provided GPU acceleration support since PyTorch 1.12, but many libraries that depend on CUDA-specific operators (such as xformers and bitsandbytes) have compatibility issues on MPS.80% similarUnverifiedThe tensorflow-metal plugin maps compute tasks to the Apple GPU for execution via the Metal Performance Shaders (MPS) backend.70% similarUnverifiedPyTorch's Tensor is highly similar to NumPy arrays, but has two key advantages: GPU computation (accelerated via CUDA) and built-in automatic differentiation (Autograd).65% similarUnverifiedIn NVIDIA GPU environments, vLLM or TensorRT-LLM can be used to achieve optimal throughput, while on Apple Silicon devices efficient inference can be achieved via the Metal backend of llama.cpp.64% similarUnverifiedSetting OLLAMA_NUM_GPU=999 enables all GPU layers, and enabling flash attention on Apple Silicon can optimize performance.63% similar
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