Systems for Foundation Models

Treating foundation models as shared infrastructure to serve, compose, and build systems with

Foundation models (FMs) are heavyweight, general-purpose backbones that many downstream tasks reuse at once. That makes them both a workload to serve efficiently and a tool to build systems with. My research treats FM sharing as a first-class systems problem, and also puts foundation models to work as building blocks inside the systems that manage our infrastructure.


Virtualizing Foundation Models

Deploying a separate copy of a backbone for every task wastes accelerator memory and compute. FMvisor (Shastri et al., 2026) treats the FM backbone as a virtualization substrate, the way a hypervisor virtualizes compute. It gives each task a virtual foundation model (vFM), a logically private model that each task can customize and fine-tune on its own. FMvisor multiplexes many vFMs over a single shared physical FM, and a novel batch-aware fair-share scheduler keeps co-located tasks isolated without giving up the throughput gains of batching them together on the shared backbone.

FMvisor gives each task a virtual foundation model (vFM) backed by a single shared physical FM, replacing the instance-per-task approach that replicates the backbone.

Composable Model Pipelines

Building a task on top of a foundation model usually means writing an ad-hoc implementation of the encoder, the backbone, an optional fine-tuning adapter, and a decoder head. FMTK (Shastri et al., 2025) is the first toolkit to standardize these components behind common interfaces. A pipeline can then be composed and fine-tuned across different backbones and tasks with minimal code, and the same pipeline can be swapped and benchmarked under consistent settings.

FMTK composes a pipeline by pairing an FM backbone with interchangeable encoders, adapters, and decoders through standardized interfaces.

Foundation Models as a Systems Tool

Foundation models can also serve as components inside the systems that manage our infrastructure. FM-CAC (Yang et al., 2026) brings a time-series foundation model into carbon-aware resource and battery management for edge AI. The model forecasts the grid’s carbon intensity, and that forecast decides when a battery-buffered device draws from the grid rather than its battery, along with which software pipeline variant and hardware operating point to run.

FM-CAC uses a time-series foundation model to forecast grid carbon intensity, driving battery, pipeline, and hardware decisions on a battery-buffered edge device.

References

2026

  1. Under Review
    FMvisor.jpg
    FMvisor: Efficient Serving of Extensible Foundation Models via Model Virtualization
    Hetvi Shastri, Pragya Sharma, Walid A. Hanafy, and 3 more authors
    Dec 2026
  2. ISLPED
    FM-CAC-ISPLED26.jpg
    FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models
    Kang Yang, Walid A. Hanafy, Prashant Shenoy, and 1 more author
    In 2026 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), Dec 2026

2025

  1. NeurIPS
    FMTK.jpg
    FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines
    Hetvi Shastri, Pragya Sharma, Walid A. Hanafy, and 2 more authors
    In The NeurIPS workshop on Recent Advances on Time Series Foundation Models: Have We Reached the BERT Moment? (BERT^2S), Dec 2025