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The deployment and scaling of large language models (LLMs) have becomecritical as they permeate various applications, demanding high-throughput andlow-latency serving systems. Existing frameworks struggle to balance theserequirements, especially for workloads with long prompts. This paper introducesDeepSpeed-FastGen, a system that employs Dynamic SplitFuse, a novel prompt andgeneration composition strategy, to deliver up to 2.3x higher effectivethroughput, 2x lower latency on average, and up to 3.7x lower (token-level)tail latency, compared to state-of-the-art systems like vLLM. We leverage asynergistic combination of DeepSpeed-MII and DeepSpeed-Inference to provide anefficient and easy-to-use serving system for LLMs. DeepSpeed-FastGen's advancedimplementation supports a range of models and offers both non-persistent andpersistent deployment options, catering to diverse user scenarios frominteractive sessions to long-running applications. We present a detailedbenchmarking methodology, analyze the performance through latency-throughputcurves, and investigate scalability via load balancing. Our evaluationsdemonstrate substantial improvements in throughput and latency across variousmodels and hardware configurations. We discuss our roadmap for futureenhancements, including broader model support and new hardware backends. TheDeepSpeed-FastGen code is readily available for community engagement andcontribution.

DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference