The evolving landscape of large language model (LLM) inference is moving towards distributed serving due to the limitations of single-node GPU optimizations as models grow larger and tasks become more complex. This shift is driven by the need for better resource allocation, smarter GPU usage, lower latency, and reduced costs. Key strategies being explored include PD disaggregation, KV cache utilization-aware load balancing, and prefix-aware routing, which allow for more efficient processing by separating prefill and decode tasks, optimizing load distribution based on cache utilization, and routing requests based on cached prefixes. While promising, these approaches require careful implementation to avoid potential drawbacks like increased data transfer costs or performance drops in small workloads. The open-source community and leading AI teams are actively developing solutions, emphasizing that distributed inference is essential for optimizing LLM deployment and scaling.