Llama.cpp: Deterministic Inference Mode (CUDA): RMSNorm, MatMul, Attention (github.com)

A recent update to llama.cpp introduces a CUDA "deterministic inference" mode that makes key GPU-backed operations produce bit‑identical outputs across runs by implementing deterministic kernels for RMSNorm, MatMul and Attention. The change replaces or reworks the previously non‑deterministic GPU code paths (where reduction order, atomics, or algorithm choice could vary between runs) so that the same inputs and model weights yield identical results on repeated inferences. The patch is focused on inference primitives rather than training, so it targets reproducibility for forward passes and decoding. This is significant because nondeterminism on GPUs complicates debugging, benchmarking, regression testing, and auditing of model behavior; deterministic inference lets developers reproduce examples precisely, compare optimizer/implementation changes, and validate safety or evaluation pipelines. Technically, the work involves deterministic accumulation/reduction strategies and stable kernel implementations for normalization, matrix multiply and attention computations; expect some trade‑offs between raw throughput and bit‑for‑bit consistency. For practitioners this means more reliable testing and profiling of Llama-family models running locally on CUDA hardware, at the cost of potential performance hits that can be toggled depending on whether exact reproducibility is required.