FMplex: Model Virtualization for Serving Extensible Foundation Models

2026-06-08 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, quick

Summary

FMplex is a novel serving system designed to virtualize Foundation Model (FM) backbones, addressing inefficiencies in current deployment practices. Existing model-serving systems typically deploy each customized task as an independent model instance, leading to replicated heavyweight backbones, wasted accelerator memory, and missed opportunities for amortizing batching and loading costs. FMplex introduces the concept of a virtual foundation model (vFM), which provides each task with a logically private FM instance supported by a shared physical FM. This abstraction enables independently customized tasks to share a backbone while maintaining task-specific extensions, independent lifecycles, and task-level isolation. The system also incorporates a batch-aware fair-queueing scheduler that combines weighted task-level sharing with inter- and intra-task batching. Implemented as a full serving stack, FMplex was evaluated across 7 FM backbones (16 variants) and 92 downstream tasks, demonstrating latency reductions of up to 80% over spatial partitioning and 33.3% over best-effort co-location, and hosting up to 6x more tasks at cluster scale.

Key takeaway

For AI Architects and Machine Learning Engineers deploying multiple customized foundation models, FMplex offers a critical solution to resource inefficiency. If you are struggling with replicated backbones, wasted accelerator memory, or high latency, consider implementing model virtualization. This approach allows you to host significantly more tasks—up to 6x—on your existing cluster infrastructure while reducing latency by up to 80%, thereby optimizing resource utilization and improving service performance.

Key insights

FMplex virtualizes foundation model backbones to enable efficient sharing across diverse downstream tasks.

Principles

• Treat FM backbones as a virtualization substrate.
• Share physical FMs while preserving task isolation.
• Combine weighted task sharing with inter/intra-task batching.

Method

FMplex implements a serving stack for task construction, sharing-aware deployment, and runtime execution, utilizing vFMs and a batch-aware fair-queueing scheduler.

In practice

• Reduce latency by up to 80% for FM serving.
• Host up to 6x more tasks on existing clusters.

Topics

• Foundation Models
• Model Virtualization
• Model Serving
• Resource Optimization
• Batch Scheduling
• Accelerator Memory

Best for: MLOps Engineer, CTO, VP of Engineering/Data, AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.