Automating Real-Time Multimodal Application Deployment

Summary & Methodology Analysis

FlashRT operates by guiding a coding agent to decompose developer-written reference code into a structured intermediate representation. This representation captures data dependencies and persistent-state scopes, allowing the agent to perform static analysis to surface parallelizable nodes and streaming opportunities. By using a chain of program paradigm, the agent identifies and tests various pipeline transformations, verifying correctness through an application-specific test harness that compares output against a baseline. The system manages this process through an iterative, measurement-gated optimization loop that benchmarks latency and throughput, using these results to rank and prioritize future optimization variants via a self-evolving queue. The resulting deployment is capable of balancing specific performance metrics across varying hardware budgets. Results show strong performance on the Qwen3-Omni model, specifically a 65 percent reduction in response latency compared to vLLM-Omni on AMD MI355X hardware and a 25 percent reduction when using 3 GPUs. The system currently supports a range of hardware, including NVIDIA B200 and AMD MI355X, and has been tested with various models like Qwen3-ASR, Qwen3-4B, Qwen3-TTS, and LiveAvatar. Current limitations include the lack of integration for LLM kernel optimization agents. Furthermore, the system is not model-agnostic, as it assumes the underlying agent possesses the native reasoning capabilities required to manage a hierarchical intermediate representation and maintain a hypothesis queue.