The recent advancements in speech synthesis and large language models (LLMs) have shown significant progress in both efficiency and quality. Researchers are increasingly focusing on integrating multi-modal capabilities, such as combining continuous audio representations with discrete tokens, to enhance the performance of generative models for speech and music. This hybrid approach not only improves perplexity and negative log-likelihood scores but also addresses the challenges of context length in high-fidelity generative architectures. Additionally, there is a growing emphasis on optimizing streaming speech synthesis, with models now achieving human-parity naturalness and minimal response latency. These developments are particularly notable in the context of edge devices, where efficient resource utilization and low latency are critical. Furthermore, the adaptation of LLMs to specialized domains, such as electrocardiogram (ECG) analysis, demonstrates the versatility of these models, with innovative tokenization techniques enabling end-to-end training and improved interpretability. Overall, the field is moving towards more integrated, efficient, and versatile models that can handle complex real-time applications.