b9026
New ggml optimization cuts inference latency by 30% on Apple Silicon
Deep Dive
The latest llama.cpp release (commit b9026) implements a fast Walsh-Hadamard transform for key-value rotation, with builds available for macOS Apple Silicon, Linux, Windows, and other platforms.
Key Points
- New fast Walsh-Hadamard transform in llama.cpp reduces inference latency by up to 30% for local LLM workloads
- Commit b9026 adds support for Apple Silicon, CUDA 12/13, Vulkan, ROCm, SYCL, and other hardware backends
- Part of ongoing effort to make local AI inference faster and more accessible on edge devices
Why It Matters
Accelerates local LLM inference by 30%, making edge AI deployment more practical for developers