Rocks, Pebbles and Sand: Modality-aware Scheduling for Multimodal Large Language Model Inference
Summary: arXiv:2603.26498v1 Announce Type: cross
Multimodal Large Language Models (MLLMs) are transforming the way users interact with technology by integrating text, images, and videos into seamless user experiences. Platforms such as ChatGPT, Gemini, and Copilot leverage these sophisticated models to provide richer, more dynamic interactions. However, the introduction of heterogeneous workloads presents significant challenges, particularly concerning latency and memory usage during the inference process. Existing serving systems, which are primarily optimized for text-only tasks, struggle to accommodate the complexities of multimodal inputs. This results in large requests—such as videos—dominating system resources, leading to severe head-of-line blocking and overall performance degradation.
Key Insights and Challenges
The research team behind RPS-Serve has identified a critical insight regarding the resource demands of multimodal requests. These requests can vary dramatically in the resources they require, which the team has elegantly categorized using a simple analogy: videos are likened to rocks, images to pebbles, and text to sand. This analogy serves as a foundation for understanding how different types of requests interact with system resources and highlights the need for a more nuanced scheduling approach.
Introducing RPS-Serve
To address the challenges posed by multimodal workloads, the team designed RPS-Serve, a modality-aware scheduler that optimally manages the flow of requests through the system. By allowing “sand” (text) to flow quickly alongside “pebbles” (images) and “rocks” (videos), RPS-Serve enhances interactive responsiveness while preventing starvation of lower-resource requests. The scheduler employs a dynamic classification system that prioritizes requests based on their resource demands and applies an aging mechanism to ensure that no request is left waiting indefinitely.
Performance Evaluation
The efficacy of RPS-Serve was evaluated across a variety of state-of-the-art MLLMs, with promising results. The scheduler demonstrated an average reduction in time-to-first-token (TTFT) by 54% overall. Notably, for latency-critical requests, RPS-Serve achieved an impressive 78.5% reduction in TTFT compared to existing systems. These improvements not only indicate the potential for enhanced responsiveness but also suggest that RPS-Serve can make more efficient use of available resources.
Conclusion
As the demand for multimodal interactions continues to grow, the challenges associated with serving these advanced models must be addressed. RPS-Serve represents a significant step forward in modality-aware scheduling, ensuring that MLLMs can deliver LLM-like responsiveness. By categorizing requests based on their resource demands and implementing intelligent scheduling mechanisms, RPS-Serve paves the way for a more efficient and user-friendly multimodal experience.
Future Directions
Looking ahead, further research is needed to refine the algorithms that underpin RPS-Serve and to explore additional optimizations. As the landscape of AI continues to evolve, the ability to effectively manage multimodal workloads will be crucial for maintaining high performance and responsiveness in user interactions.