π€Reddit r/MachineLearningβ’Stalecollected in 20h
MNIST MLP from Scratch in Metal C++
#apple-metal#gpu-programming#mnist-baselinemnist-metalmnistmetal-cppmlxapple-silicon
π‘Beats MLX on Apple GPU w/ custom Metal C++; repo for hands-on GPU ML
β‘ 30-Second TL;DR
What Changed
Pure Metal implementation: matmul, ReLU, forward/backward kernels
Why It Matters
Highlights API-level choices for Apple GPU ML; useful for on-device inference devs.
What To Do Next
Clone https://github.com/abeleinin/mnist-metal and benchmark on your Apple Silicon Mac.
Who should care:Developers & AI Engineers
Key Points
- β’Pure Metal implementation: matmul, ReLU, forward/backward kernels
- β’Optimizations: buffer residency, single command buffer per batch
- β’Outperforms MLX training on small batch sizes; baselines provided
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Original source: Reddit r/MachineLearning β
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