In the realm of serving large language models (LLMs), the researchers offer an elegant solution to the classic throughput-latency tradeoff. The paper introduces Sarathi-Serve, an LLM inference scheduler devised to optimize throughput while maintaining low latency under stringent Service Level Objectives (SLOs). The key innovations embodied in Sarathi-Serve include:

• Chunked-prefills enabling stall-free scheduling across batched requests
• Opportunistic inclusion of new requests to improve throughput without hampering ongoing decodes
• Significant throughput enhancement while adhering to desired latency SLOs

Through rigorous testing on state-of-the-art A100 GPUs, Sarathi-Serve demonstrated throughput improvements of up to 2.6x for Mistral-7B and 6.9x for Falcon-180B across an 8-GPU array, offering substantial advancements over existing systems like Orca and vLLM. The authors highlighted:

• Methods for creating efficient inference schedules
• The effectiveness of batching in optimizing GPU utilization
• The potential for parallel processing in LLM serving requests

I believe this paper is critical as it provides a scalable framework for deploying LLMs more effectively in production environments. It showcases the importance of smart scheduling in harnessing GPU power for AI services and sets the stage for future enhancements in server infrastructure for complex model inferences. For further insight, read the full paper here.