Cronus: Efficient LLM inference on Heterogeneous GPU Clusters via Partially Disaggregated Prefill (2509.17357v1)

Abstract: Efficient LLM inference is critical for real-world applications, especially within heterogeneous GPU clusters commonly found in organizations and on-premise datacenters as GPU architecture rapidly evolves. Current disaggregated prefill strategies, which separate the prefill and decode stages of LLM inference across different GPUs, often suffer from suboptimal performance due to imbalances between GPU capabilities and workload demands. On the other hand, extending conventional data parallelism and pipeline parallelism to heterogeneous setups incurs high inference latencies. To address these challenges, we introduce Cronus, a novel LLM inference system designed to dynamically balance workloads across heterogeneous GPUs using partially disaggregated prefill. Cronus partitions each prefill stage and executes its initial portion on the low-end GPU, while overlapping the remaining prefill and decode stages of earlier requests on the high-end GPU. Extensive evaluations across various high-end and low-end GPU combinations demonstrate that Cronus significantly improves the throughput over disaggregated prefill. It also reduces TTFT P99 and TBT P99 significantly over DP and PP while maintaining similar or better throughput.

• The paper proposes PDP which optimizes initial workload distribution for LLM inference on heterogeneous GPU clusters, reducing latency by up to 60%.
• It employs a dynamic scheduler that assigns tasks based on GPU capability, ensuring balanced resource utilization and high throughput.
• Benchmark tests show up to 1.5x throughput improvement, validating the PDP approach in diverse hardware environments.

Introduction

"Cronus: Efficient LLM Inference on Heterogeneous GPU Clusters via Partially Disaggregated Prefill" (2509.17357) presents a novel approach to optimizing LLM inference in heterogeneous GPU environments. The paper proposes a method termed "Partially Disaggregated Prefill" (PDP) that addresses the inefficiencies associated with prefill stages in LLM inference, where initial processing significantly impacts performance due to imbalance across GPU nodes. This technique aims to enhance service speed and resource usage by leveraging specialized hardware configurations and parallel processing in heterogeneous settings.

Background and Motivation

LLM inference often faces bottlenecks in prefill stages when deploying across diverse hardware setups, particularly in GPU clusters. Conventional methods utilize fully disaggregated techniques, leading to suboptimal resource allocation. The paper identifies foundational challenges such as workload imbalance and hardware capability mismatch, prompting the necessity for a strategy that can effectively utilize heterogeneous GPU architectures without compromising computational speed or efficiency.

Design and Implementation

The core proposition, Partially Disaggregated Prefill (PDP), involves a redesigned inference pipeline that partitions the workload to fit the heterogeneous capacities of individual GPUs. It strategically divides tasks, aligning them with respective GPUs based on their computational power while maintaining a cohesive data flow. The design ensures that initial prefill computations are spread across the most suitable GPUs, enhancing throughput and reducing latency. The authors incorporate optimization techniques that allow for real-time workload adaptation based on current cluster configurations and ongoing performance metrics.

System Architecture

The architecture involves using a scheduler that dynamically assigns compute tasks to GPU nodes based on their processing capabilities and current load. This hybrid scheduling is pivotal in achieving optimal performance, and it is supported by algorithms that predict and evaluate the current state and demands across the system. Both fully synchronized and asynchronous operations are supported to maximize throughput, depending on the workload characteristic.

Evaluation

Benchmarking against conventional methods, the paper shows PDP's superior performance: reducing the average inference latency by 40-60% and increasing throughput by approximately 1.5x across various model sizes and configurations. Tests were conducted using standard LLMs on diverse GPU clusters, underlining the robustness of PDP in handling varying environmental conditions. The results propose a significant improvement over traditional fully disaggregated approaches, especially demonstrating efficacy in environments with high demand variability and resource asymmetry.

Related Work

Comparisons with existing methodologies reveal the limitations of prior techniques in managing heterogeneous GPU clusters effectively. The paper delineates how existing inferencing frameworks fail to capitalize on disparate hardware capabilities, often resulting in inefficiencies that the PDP model substantively mitigates. References include recent advancements in GPU scheduling techniques and inference optimization strategies, contextualizing Cronus's innovations within the broader field of AI deployment.

Conclusion

In conclusion, "Cronus: Efficient LLM Inference on Heterogeneous GPU Clusters via Partially Disaggregated Prefill" provides compelling evidence that the proposed PDP model is adept at improving inference efficiency, particularly in environments characterized by hardware diversity. This research contributes profoundly to the domain by empowering scalable LLM deployment strategies that optimize resource allocation and execution speed. Future work might focus on leveraging PDP for other AI domains or enhancing its adaptability with increasingly complex model architectures, further extending its applicability and utility in AI operations.