FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications
FlashRT uses coding agents to turn simple multimodal app references into optimized multi-GPU deployments.
The paper describes a “chain-of-program” process that builds an intermediate representation, validates it, finds possible transformations, then benchmarks changes before keeping them. The target is real-time systems such as voice agents, video world models, and multimodal LLMs, where placement, streaming, and parallelism choices affect latency and throughput. In the reported results, FlashRT reaches up to about 70x latency reduction and improves throughput by 2.8x on NVIDIA B200 GPUs and 3.6x on AMD MI355X GPUs. For Qwen3-Omni text-to-audio inference, it cuts response latency by 65% versus vLLM-Omni on AMD MI355X. ArXiv · AI/CL/LG's note
The paper describes a “chain-of-program” process that builds an intermediate representation, validates it, finds possible transformations, then benchmarks changes before keeping them. The target is real-time systems such as voice agents, video world models, and multimodal LLMs, where placement, streaming, and parallelism choices affect latency and throughput. In the reported results, FlashRT reaches up to about 70x latency reduction and improves throughput by 2.8x on NVIDIA B200 GPUs and 3.6x on AMD MI355X GPUs. For Qwen3-Omni text-to-audio inference, it cuts response latency by 65% versus vLLM-Omni on AMD MI355X. ArXiv · AI/CL/LG's note
score 5