MServe: An Efficient Serving System for Multimodal Large Model Inference

MServe is a serving system optimized specifically for multimodal large language model inference. It addresses performance bottlenecks and resource scheduling challenges in multimodal model deployment through innovative architectural designs. Its core goal is to maximize hardware utilization and reduce deployment costs while ensuring service quality.

With the evolution of multimodal large models like GPT-4V and Gemini, deployment services face four major challenges:

• Heterogeneous computing requirements: Different modalities require different resources (GPU, TPU, etc.)
• Dynamic load characteristics: Visual tokens change with resolution, leading to unpredictable latency
• Low resource utilization: Traditional systems optimized for single modalities are unsuitable for mixed loads
• High costs: Large parameter sizes lead to soaring costs for inefficient deployments

Core Design Philosophy

1. Modality-aware scheduling: Identify the modality composition of requests and intelligently allocate resources
2. Dynamic batching: Adjust strategies based on input complexity to balance throughput and latency
3. Hierarchical caching mechanism: Design multi-level caching for multimodal features to reduce redundant computations
4. Elastic scaling: Automatically adjust the number of service instances based on real-time load

Key Technologies

1. Visual token dynamic estimation: Pre-estimate computing requirements to avoid retries due to insufficient resources
2. Cross-modal attention optimization: Sparse attention, KV cache reuse, pipeline parallelism
3. Intelligent request routing: Schedule based on modality type, input size, latency requirements, and model version
4. Resource isolation and sharing: GPU partitioning (MIG), memory pooling, priority preemption

MServe performs excellently in multiple metrics:

• Throughput improvement: Multimodal request throughput is 2-4 times higher than traditional frameworks
• Latency reduction: P99 latency is reduced by 30-50%, more significant in high-load scenarios
• Cost savings: Improved resource utilization reduces deployment costs by over 40%
• Scalability: Supports horizontal scaling to hundreds of GPU nodes

Actual Application Scenarios

1. Visual question answering systems
2. Document understanding services (PDF/scanned document parsing)
3. Video analysis platforms
4. Multimodal dialogue robots
5. AI-assisted design tools

Technical Trends and Outlook

• Support for more modalities (3D point clouds, haptics, etc.)
• Edge-cloud collaborative inference
• Integration of automatic model compression and quantization
• Federated learning for distributed multimodal inference

Deployment and usage recommendations:

• Hardware selection: Recommend NVIDIA A100/H100 GPUs that support MIG
• Model adaptation: Convert to supported formats like TensorRT-LLM
• Monitoring metrics: Focus on visual token processing latency, cache hit rate, and GPU memory utilization
• Gradual migration: Pilot with non-critical services first, then gradually expand the application scope