The field of large language model (LLM) serving and inference is rapidly advancing towards more efficient, adaptive, and user-intent aware systems. Recent developments focus on optimizing resource utilization, reducing latency, and enhancing system throughput without compromising on the quality of service (QoS) or response accuracy. Innovations include the introduction of speculative decoding techniques for SLO customization, near bubble-free pipeline optimization for end-cloud collaborative inference, real-time knowledge distillation for accelerating LLM serving, and intent-based serving systems that dynamically adapt to user requirements. These advancements collectively aim to address the challenges of high resource demands, latency, and the need for personalized deployment configurations in LLM applications.

Noteworthy papers include:

• AdaServe: Introduces fine-grained speculative decoding for SLO customization, significantly improving SLO attainment and goodput.
• COACH: Proposes a near bubble-free pipeline optimization framework for end-cloud collaborative inference, achieving faster inference and higher system throughput.
• EchoLM: Leverages real-time knowledge distillation for LLM serving, enhancing throughput and reducing latency without compromising response quality.
• iServe: An intent-based serving system for LLMs that dynamically selects optimal deployment configurations, reducing latency and SLO violations while improving GPU throughput.