FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications
FlashRT uses coding agents to turn simple multimodal reference code into optimized multi-GPU deployments.
The paper says FlashRT guides an agent through IR extraction, validation, static analysis, and measurement-gated optimization. It targets deployment choices such as placement, streaming, and intra-model parallelism for real-time systems like voice agents and video world models. Reported gains include up to about 70x lower latency and 2.8x higher throughput on NVIDIA B200 GPUs, with throughput gains rising to 3.6x on AMD MI355X. For Qwen3-Omni text-to-audio inference on MI355X, it reports 65% lower response latency than the expert vLLM-Omni implementation. HF Daily Papers' note
The paper says FlashRT guides an agent through IR extraction, validation, static analysis, and measurement-gated optimization. It targets deployment choices such as placement, streaming, and intra-model parallelism for real-time systems like voice agents and video world models. Reported gains include up to about 70x lower latency and 2.8x higher throughput on NVIDIA B200 GPUs, with throughput gains rising to 3.6x on AMD MI355X. For Qwen3-Omni text-to-audio inference on MI355X, it reports 65% lower response latency than the expert vLLM-Omni implementation. HF Daily Papers' note
score 5