Towards Multi-Model LLM Schedulers: Empirical Insights into Offloading and Preemption

2026-05-19 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, quick

Summary

An empirical study investigates the challenges of multi-model Large Language Model (LLM) scheduling on shared, heterogeneous hardware, particularly concerning GPU memory constraints requiring CPU-GPU offloading and preemption. The research reveals that offloading leads to strongly non-linear and model-dependent degradation in decode throughput, with smaller models exhibiting sharper sensitivity to reduced GPU residency. Furthermore, preemption incurs substantial overhead, primarily dominated by model state reload rather than key-value cache transfer, and this cost varies significantly across models and hardware platforms. The study also highlights how sequence length and interconnect bandwidth amplify data movement and execution inefficiencies. These findings provide critical guidance for designing next-generation LLM serving systems capable of efficiently managing heterogeneous, multi-model workloads with hybrid CPU-GPU execution.

Key takeaway

For MLOps Engineers designing or optimizing multi-model LLM serving infrastructure, you must move beyond single-model throughput optimizations. Your scheduling decisions should explicitly account for model-specific offloading sensitivities, the substantial overhead of model state reloads during preemption, and the impact of interconnect bandwidth. Prioritize developing schedulers that dynamically adapt to these factors to ensure efficient resource utilization and predictable performance across diverse LLM workloads.

Key insights

Multi-model LLM serving requires schedulers to account for non-linear offloading sensitivity and high preemption costs across diverse models and hardware.

Principles

• Offloading impact on throughput is non-linear and model-dependent.
• Preemption overhead is dominated by model state reload.
• Interconnect bandwidth affects data movement efficiency.

In practice

• Consider model-specific offloading sensitivity.
• Account for workload characteristics in scheduling.
• Evaluate preemption costs for specific models/hardware.

Topics

• Multi-Model LLM Serving
• LLM Scheduling
• GPU Offloading
• Model Preemption
• Resource Allocation
• Heterogeneous Hardware

Best for: AI Architect, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.