Apple's new open-source ParaRNN framework shatters the sequential bottleneck of traditional RNN processing by leveraging Newton methods and parallel reduction algorithms. This enables dramatically faster training and inference for recurrent architectures—critical for large language models—while maintaining PyTorch compatibility and offering customizable CUDA acceleration. Developers gain flexible tools to build high-performance RNN cells without sacrificing ease of experimentation.