How I accelerated convolutions

▤ Summary

This article explains how to speed up convolution-heavy neural network workloads on Apple Silicon using MLX and Metal. It compares a PyTorch/MPS baseline with an MLX implementation and shows how channel layout, fused kernels, and weight conversion affect performance. It also walks through a custom Metal kernel and the practical steps needed to load PyTorch weights into an MLX model. Benchmark results show lower latency and higher throughput for the fused MLX path, especially at small batch sizes.

▦ Linked Companies

Apple Inc.

$1B+

PyTorch

$10M to $25M