The SyFI Lab at the University of Washington builds efficient and resilient infrastructure for the future of AI. As applications grow more complex, we bridge the gap between next-gen models and heterogeneous hardware through cross-stack innovation, delivering scalable, open-source systems validated by industrial partners.

Our research targets three key areas:

  • Efficient AI: Optimizing algorithms and systems to maximize performance for training and inference.
  • Flexible AI: Architecting systems that seamlessly adapt to diverse tasks, strategies, and model structures.
  • Resilient AI: Ensuring AI system reliability at scale while leveraging AI to improve infrastructure robustness.

Publications

VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?

Keisuke Kamahori, Shihang Li, Simon Peter, Baris Kasikci — (2026)

VoxServe: Streaming-Centric Serving System for Speech Language Models

Keisuke Kamahori, Wei-Tzu Lee, Atindra Jha, Rohan Kadekodi, Stephanie Wang, Arvind Krishnamurthy, Baris Kasikci — (2026)

Reducing the GPU Memory Bottleneck with Lossless Compression for ML

Aditya K Kamath, Arvind Krishnamurthy, Marco Canini, Simon Peter — European Conference on Computer Systems (EuroSys) (2026)

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Blog Posts

Introducing Piper: A Programmable Distributed Training System

June 05, 2026

We present Piper, a programmable distributed training system that uses model annotations and scheduling directives to express model placement, pipeline scheduling, and GPU stream scheduling.
SyFI Team Wins CUDA Kernel Agent Contest at MLSys 2026

May 28, 2026

Team UW SyFI won three awards across two tracks at the FlashInfer AI Kernel Generation Contest at MLSys 2026 — every line of kernel code written by coding agents, not humans.
Let AI Agents Write Your Serving Stack with VibeServe

May 12, 2026

We present VibeServe, a multi-agent system that synthesizes a complete LLM serving runtime end-to-end, specialized to a user-specified model, hardware, and workload.
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Talks

The Wrong Contract at Every Layer: Redesigning OS and Hardware for AI Datacenters

May 15, 2026 Dimitrios Skarlatos — Carnegie Mellon University

Scaling Distributed Systems Designs

May 8, 2026 Jialin Li — National University of Singapore

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