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

Forum topic · 小凯 · 2026-07-22

Summary

FlashRT is an agent framework presented in arXiv paper 2607.18171 that guides coding agents to transform simple developer-written reference implementations into optimized multi-GPU deployments for real-time multimodal applications such as voice agents and interactive video generation. These applications compose heterogeneous models into pipelines, and efficient deployment requires application-specific decisions about placement, streaming, and intra-model parallelism—decisions that existing serving systems and auto-parallelizing compilers, limited to restricted transformations and fixed workload assumptions, cannot make. FlashRT introduces a chain-of-program paradigm in which a general-purpose coding agent performs multi-pass transformations: it converts the reference implementation into an intermediate representation (IR) capturing data dependencies and persistent state scopes, validates the IR via a sequential interpreter, and applies static analysis to identify candidate transformations, flexibly trading off latency and throughput. Reported results include up to ~70x latency reduction and 2.8x throughput improvement on NVIDIA B200 GPUs, plus 3.6x throughput improvement on AMD MI355X. Authored by Krish Agarwal, Zhuoming Chen, Yanyuan Qin, Zhenyu Gu, Atri Rudra, and colleagues, the paper is categorized under cs.LG.

FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications

Research area: LLM 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

Overview

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 limited to restricted transformations and fixed workload assumptions, so achieving high performance on new applications still requires hand-crafted efficient implementations.

The authors propose 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. Using a novel chain-of-program paradigm, FlashRT steers a general-purpose coding agent through a multi-pass transformation process:

1. The agent converts the reference implementation into an intermediate representation (IR) that captures data dependencies and persistent state scopes. 2. It validates this IR via a sequential interpreter. 3. It performs static analysis to identify candidate transformations.

Results (condensed from the original abstract)

> We present FlashRT, an agent harness using chain-of-program to guide coding agents in optimizing multimodal deployments. It delivers up to ~70x latency reduction and 2.8x throughput improvement on NVIDIA B200, with 3.6x throughput on AMD MI355X.

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