Developers achieve 16.8x speedup on Apple Silicon by enabling llama.cpp to use the Neural Engine for AI model inference

A new GGML backend for llama.cpp allows Apple Neural Engine (ANE) hardware acceleration on all Apple Silicon chips, not just the new M5 models

M4 Pro testing showed peak performance of 4.0 TFLOPS at N=256, delivering 16.8x faster speeds compared to CPU-only inference

The implementation uses a hybrid approach: ANE handles prefill operations when N≥64, while Metal GPU and CPU handle decode operations for optimal efficiency

Features include MIL-side transpose operations, kernel caching, and support for quantized weights to reduce memory usage

The open-source code is available on GitHub (arozanov/ggml-ane) and builds on previous ANE bridge work from the community