HFX: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling
In the rapidly evolving landscape of artificial intelligence, particularly in large language model (LLM) serving, a new system named HFX has emerged to tackle the dual challenges of meeting user-specific service-level objectives (SLOs) while minimizing computational costs. The research, detailed in arXiv:2508.15919v3, highlights the limitations of existing methodologies that either rely on static scheduling policies or cater to single-task environments, which are inadequate for the complexities of real-world deployments.
The Need for Improved LLM Serving Systems
As organizations increasingly adopt LLMs for various applications, the demand for systems that can handle heterogeneous requests with varying prompt lengths and elastic scaling requirements has never been greater. Traditional approaches often fall short in dynamic, multi-task workloads, leading to inefficiencies and reduced performance.
Introducing HFX
HFX stands as a solution that redefines LLM serving by jointly optimizing request scheduling and elastic scaling across model replicas. This innovative system features two core components:
- Scheduler: HFX incorporates a proactive budget estimation and prioritization mechanism that ensures compliance with SLOs for both new and ongoing requests. By anticipating workload demands, the scheduler maintains a balance between responsiveness and resource allocation.
- Scaler: To address cold-start latency, HFX integrates a device-to-device (D2D) weight transfer capability. This allows for rapid scaling of resources, ensuring that the models are always ready to respond to incoming requests without significant delays.
Flexible Deployment Options
Another key feature of HFX is its support for both colocated and disaggregated prefill/decode deployments. This flexibility enables the system to adapt to various workload patterns and cloud environments, making it suitable for organizations with diverse operational needs.
Performance Evaluation
Extensive experiments conducted on multi-task workloads showcase the superiority of HFX in terms of SLO attainment and resource utilization. Key findings from the research include:
- A consistent increase in SLO attainment compared to state-of-the-art systems, ensuring that user expectations are met without compromise.
- A reduction in end-to-end latency by up to 65.82%, enhancing user experience through faster response times.
- A decrease in NPU usage cost by as much as 49.81%, demonstrating cost-efficiency in resource utilization.
Conclusion
The introduction of HFX marks a significant advancement in the field of LLM serving, offering a robust framework that prioritizes both cost-efficiency and SLO compliance. As organizations continue to explore the potential of AI and machine learning, systems like HFX will play a crucial role in optimizing performance and operational efficiency. By addressing the challenges of multi-task workloads in real-world scenarios, HFX sets a new standard for LLM serving systems, paving the way for future innovations in the field.
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