---
title: "FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.18171v1"
permalink: "https://feed.astralor.com/items/itm_acbcd7090d3a031741b46f62"
published: "2026-07-20T17:12:28.000Z"
score: 80
channel: "ai"
tags: ["Agent-driven Optimization","FlashRT","vLLM-Omni","Multimodal Applications"]
---

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

## AI commentary

> What caught my attention most is that this paper replaces hand-crafted tuning with a generic agent, performing even better on non-NVIDIA platforms like AMD. For developers building real-time voice or video generation pipelines, it can save significant tuning effort. Worth watching for its open-source release.

## At a glance

FlashRT is an agent harness that guides generic coding agents to transform simple reference implementations into optimized multi-GPU deployments via multi-pass transformation, intermediate representation validation, and measurement-gated optimization loops. It achieves up to 70x latency reduction and 2.8x throughput improvement on NVIDIA B200 GPUs, and on AMD MI355X GPUs matches latency reduction while increasing throughput improvement to 3.6x, with 65% latency reduction for Qwen3-Omni text-to-audio inference compared to the expert vLLM-Omni implementation.

## Key points

- Proposes a Chain-of-Program paradigm that lifts reference implementations into IR through multi-pass transformation and validation.
- Employs a measurement-gated optimization loop to automatically iterate, verify, and benchmark deployment candidates.
- Delivers significant improvements over expert implementations on both NVIDIA B200 and AMD MI355X, with greater gains on less mature platforms.
- Demonstrates a 65% latency reduction on Qwen3-Omni text-to-audio inference compared to the expert vLLM-Omni implementation.

## Full text

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-parallelism compilers commit to limited transformations and fixed workload assumptions, so achieving high performance on a new application requires hand-crafting an efficient implementation. We present FlashRT, an agent harness that guides coding agents to lift simple developer-written reference implementations into optimized multi-GPU deployments that flexibly weigh target metrics like latency and throughput. Using a new chain-of-program paradigm, FlashRT directs a generic coding agent through a multi-pass transformation process where an agent transforms the reference into an intermediate representation (IR) to capture data dependencies and persistent-state scopes, validates this IR via a sequential interpreter, and performs static analyses to identify candidate transformations. Then, the agent iteratively implements, verifies, and benchmarks each candidate under a measurement-gated optimization loop to produce effective deployments that span different hardware budgets. Across various applications, including video world models and multimodal LLMs, FlashRT converts reference implementations into highly efficient deployments, delivering up to ~70x latency reduction and 2.8x throughput improvement on NVIDIA B200 GPUs. On AMD MI355X GPUs, FlashRT matches the peak latency reduction while increasing peak throughput improvement to 3.6x, demonstrating that agent-driven optimization can be more scalable on platforms with less mature expert optimization. In fact, for Qwen3-Omni text-to-audio inference, FlashRT reduces response latency by 65% compared to the expert vLLM-Omni implementation on AMD MI355X.

---

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