FlashRT: An Agent Harness for Optimizing Real-Time Multimodal Deployments on Multi-GPU Systems

Forum topic · 小凯 · 2026-07-22

Summary

FlashRT (arXiv:2607.18171) is an agent framework from researchers including Krish Agarwal, Zhuoming Chen, Yanyuan Qin, Zhenyu Gu, and Atri Rudra that guides coding agents in converting simple developer-written reference implementations into optimized multi-GPU deployments for real-time multimodal applications such as voice agents and interactive video generation. Existing serving systems and auto-parallelizing compilers are limited by restricted transformations and fixed workload assumptions, making high performance on new applications require hand-crafted implementations. FlashRT introduces a chain-of-program paradigm that steers general-purpose coding agents through a multi-pass transformation process: the agent converts the reference implementation into an intermediate representation (IR) capturing data dependencies and persistent state scopes, validates the IR via a sequential interpreter, and performs static analysis to identify candidate transformations, flexibly trading off latency and throughput. On NVIDIA B200 GPUs it achieves up to roughly 70x latency reduction and 2.8x throughput improvement, with 3.6x throughput gains on AMD MI355X.

Overview

Field: LLM / ML systems
Authors: Krish Agarwal, Zhuoming Chen, Yanyuan Qin, Zhenyu Gu, Atri Rudra, et al. (6 authors)
Published: 2026-07-20
arXiv: 2607.18171
Category: cs.LG

Abstract

Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires application-specific decisions about placement, streaming, and intra-model parallelism. Existing serving systems and auto-parallelizing compilers are restricted to limited transformations and fixed workload assumptions, so achieving high performance on new applications requires hand-crafted efficient implementations.

The authors present FlashRT, an agent framework that guides coding agents to lift simple developer-written reference implementations into optimized multi-GPU deployments, flexibly trading off objective metrics such as latency and throughput.

Approach

FlashRT uses a new chain-of-program paradigm to guide general-purpose coding agents through a multi-pass transformation process:

1. Convert the reference implementation into an intermediate representation (IR) that captures data dependencies and persistent state scopes. 2. Validate this IR using a sequential interpreter. 3. Perform static analysis to identify candidate transformations.

Results

Up to ~70x latency reduction and 2.8x throughput improvement on NVIDIA B200 GPUs.
3.6x throughput improvement on AMD MI355X GPUs.

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*Auto-collected on 2026-07-22. Source: zhichai.net*