TriInfer: Hybrid EPD Disaggregation for Efficient Multimodal Large Language Model Inference

Existing MLLM inference systems are typically designed based on the architecture of language models, coupling image processing and language processing. This design struggles to accommodate the heterogeneous demands of different stages in terms of computational resources, memory access patterns, and service-level objectives (SLOs), leading to low resource utilization and high request latency, ultimately failing to meet the service requirements of diverse inference scenarios. To address these challenges, we propose TriInfer, an efficient MLLM inference system that adopts a Hybrid Encode-Prefill-Decode (EPD) Disaggregation architecture. By scheduling the three stages — encode, prefill, and decode — onto separate heterogeneous inference instances, the system flexibly reallocates resources across stages, significantly reducing idle computation, alleviating resource bottlenecks, and improving overall system throughput and scalability. In addition, TriInfer supports a stage-level batching strategy that enhances load balancing, enables parallel execution of visual and language models, and further optimizes inference performance. Experiments under real multimodal inference workloads demonstrate that TriInfer can achieve up to 3.7× higher inference throughput compared to state-of-the-art systems (e.g., vLLM, SGLang) while meeting the 90th percentile request SLO.