Flexible Spatial-Temporal Multiplexing for Efficient Serving of Multiple Large Language Models

Core Concepts

MuxServe, a flexible spatial-temporal multiplexing system, colocates large language models considering their popularity and flexibly colocates prefill and decoding jobs to improve GPU utilization and serve multiple LLMs efficiently.

Abstract

The paper presents MuxServe, a flexible spatial-temporal multiplexing system for efficient serving of multiple large language models (LLMs). The key insights behind MuxServe are:

Colocate LLMs considering their popularity to multiplex memory resources. Different LLMs exhibit varying levels of popularity, and colocating popular and unpopular LLMs can improve memory utilization.

Leverage the distinct characteristics of prefill and decoding phases in LLM inference to flexibly colocate them and multiplex computation resources. The prefill phase heavily utilizes computation resources, while the decoding phase often results in insufficient GPU utilization.

MuxServe formally formulates the multiplexing problem and proposes a novel placement algorithm and adaptive batch scheduling strategy to identify optimal colocations and maximize utilization. It also designs a unified resource manager to enable flexible and efficient multiplexing.
Evaluation results show that MuxServe can achieve up to 1.8x higher throughput or process 2.9x more requests within 99% SLO attainment compared to prior state-of-the-art systems.

Stats

The paper states that serving a single 175B LLM requires eight A100 (80GB) GPUs.
The paper observes that different LLMs exhibit varying levels of popularity, with some LLMs consistently receiving a considerably higher volume of serving traffic compared to others.

Quotes

"Different LLMs typically exhibit varying levels of popularity among users influenced by factors such as output quality, response speed, and usage patterns."
"The incremental decoding phase, which typically plays a significant role in the inference process, often falls short in fully utilizing GPUs."

Key Insights Distilled From

MuxServe

by Jiangfei Dua... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.02015.pdf

Deeper Inquiries

How can MuxServe's techniques be extended to serve a wider range of deep learning models beyond just large language models

MuxServe's techniques can be extended to serve a wider range of deep learning models beyond just large language models by adapting the spatial-temporal multiplexing approach to accommodate the unique characteristics of different types of models. For example, for computer vision models, the prefill phase could involve processing the input image through the convolutional layers, while the decoding phase could focus on the final classification or regression layers. By understanding the specific computation requirements of different types of models, MuxServe could optimize the colocation and scheduling of these phases to maximize GPU utilization and throughput. Additionally, MuxServe could incorporate specialized memory management techniques tailored to the memory access patterns of computer vision models or other deep learning architectures.

What are the potential drawbacks or limitations of the spatial-temporal multiplexing approach used in MuxServe, and how could they be addressed

One potential drawback of the spatial-temporal multiplexing approach used in MuxServe is the complexity of dynamically managing the allocation of resources across multiple LLMs. As the number of models and their varying popularity levels increase, the optimization problem for colocating and scheduling jobs becomes more challenging and computationally intensive. To address this limitation, MuxServe could explore more advanced optimization algorithms, such as reinforcement learning or genetic algorithms, to efficiently solve the placement and scheduling problem. Additionally, MuxServe could implement more sophisticated resource allocation strategies that take into account real-time workload changes and adapt the colocation and scheduling decisions accordingly.

What other system-level optimizations or techniques could be explored to further improve the efficiency and scalability of large language model serving infrastructure

To further improve the efficiency and scalability of large language model serving infrastructure, MuxServe could explore the following system-level optimizations and techniques:

Dynamic resource allocation: Implementing dynamic resource allocation mechanisms that can adjust the allocation of GPUs, memory, and other resources based on real-time workload demands. This adaptive approach can help optimize resource utilization and improve overall system efficiency.
Hybrid multiplexing strategies: Combining spatial-temporal multiplexing with other multiplexing techniques, such as frequency-based multiplexing or workload-aware multiplexing, to achieve a more balanced and efficient resource utilization across different LLMs.
Fine-grained job scheduling: Developing more granular job scheduling algorithms that can optimize the allocation of resources at a finer level, such as at the level of individual layers or modules within the LLMs. This fine-grained approach can help maximize resource utilization and minimize resource contention.
Energy-efficient serving: Introducing energy-efficient serving mechanisms that prioritize low-power modes or dynamic voltage and frequency scaling to reduce energy consumption during periods of low workload or idle times. This can contribute to overall energy savings and sustainability in LLM serving infrastructure.