FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications
2026-07-20 • Machine Learning
Machine Learning
AI summaryⓘ
The authors introduce FlashRT, a tool that helps transform simple code for real-time apps like voice agents into much faster versions that use multiple GPUs. FlashRT uses an intelligent process where an agent rewrites and tests the code step-by-step to optimize it for better speed and efficiency across different hardware types. This approach significantly reduces delay and boosts processing speed, especially on GPUs that lack expert fine-tuning support. Their method works well on various applications, including video and multimodal language models, showing that automated optimization can match or beat hand-crafted solutions.
multi-GPU deploymentreal-time applicationsmultimodal modelscode optimizationlatency reductionthroughput improvementintermediate representationstatic analysisauto-parallelismagent-driven optimization
Authors
Krish Agarwal, Zhuoming Chen, Yanyuan Qin, Zhenyu Gu, Atri Rudra, Beidi Chen
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-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.