News

Can Scheduling Overhead Dominate LLM Inference Performance? A Study of CPU Scheduling Overhead on Two Popular LLM Inference Systems

- 12 mins read
Today’s LLM serving systems like vLLM and TGI primarily use a scheduling approach called iterative scheduling (or continuous batching), which decides the batch composition at every round (or every few rounds) of model forwarding. Different from prior serving systems that schedule the next batch after the entire current batch finishes, iterative scheduling promises to improve GPU utilization and LLM serving rate, but with a key assumption: the scheduling overhead can be ignored. While this assumption generally held in the past, it is worth reexamination as today’s LLM inference kernels run much faster than before and as more scheduling tasks and considerations get added. Read More…

Preble: Efficient Prompt Scheduling for Augmented Large Language Models

- 5 mins read
LLM prompts are growing more complex and longer with agents, tool use, large documents, video clips, and detailed few-shot examples. These prompts often have content that is shared across many requests. The computed intermediate state (KV cache) from one prompt can be reused by another for their shared parts to improve request handling performance and save GPU computation resources. However, current distributed LLM serving systems treat each request as independent and miss the opportunity to reuse the computed intermediate state. We introduce Preble, the first distributed LLM serving system that targets long and shared prompts. Preble achieves a 1.5-14.5x average and 2-10x p99 latency reduction over SOTA serving systems. The core of Preble is a new E2 Scheduling that optimizes load distribution and KV cache reutilization. Preble is compatible with multiple serving backends such as vLLM and SGLang. Read More…

Efficient Augmented LLM Serving With InferCept

- 6 mins read
Today’s large language models (LLMs) are being paired with various tools and environments to satisfy increasingly complex user queries. Augmenting models with these capabilities means LLM inference can be intercepted by external actions. We designed InferCept [ICML ‘24], the first serving framework designed for augmented LLMs. InferCept minimizes resource waste and sustains a 1.6x-2x higher serving load, completing twice as many requests compared to state-of-the-art serving systems. Try InferCept here. Read More…