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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.20655 |
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| _version_ | 1866908793553551360 |
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| author | Chen, June Xu, Neal Huang, Gragas Zhou, Bok Liu, Stephen |
| author_facet | Chen, June Xu, Neal Huang, Gragas Zhou, Bok Liu, Stephen |
| contents | The rapid growth of AI-generated content (AIGC) has enabled high-quality creative production across diverse domains, yet existing systems face critical inefficiencies in throughput, resource utilization, and scalability under concurrent workloads. This paper introduces OnePiece, a large-scale distributed inference system with RDMA optimized for multi-stage AIGC workflows. By decomposing pipelines into fine-grained microservices and leveraging one-sided RDMA communication, OnePiece significantly reduces inter-node latency and CPU overhead while improving GPU utilization. The system incorporates a novel double-ring buffer design to resolve deadlocks in RDMA-aware memory access without CPU involvement. Additionally, a dynamic Node Manager allocates resources elastically across workflow stages in response to real-time load. Experimental results demonstrate that OnePiece reduces GPU resource consumption by 16x in Wan2.1 image-to-video generation compared to monolithic inference pipelines, offering a scalable, fault-tolerant, and efficient solution for production AIGC environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20655 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OnePiece: A Large-Scale Distributed Inference System with RDMA for Complex AI-Generated Content (AIGC) Workflows Chen, June Xu, Neal Huang, Gragas Zhou, Bok Liu, Stephen Distributed, Parallel, and Cluster Computing The rapid growth of AI-generated content (AIGC) has enabled high-quality creative production across diverse domains, yet existing systems face critical inefficiencies in throughput, resource utilization, and scalability under concurrent workloads. This paper introduces OnePiece, a large-scale distributed inference system with RDMA optimized for multi-stage AIGC workflows. By decomposing pipelines into fine-grained microservices and leveraging one-sided RDMA communication, OnePiece significantly reduces inter-node latency and CPU overhead while improving GPU utilization. The system incorporates a novel double-ring buffer design to resolve deadlocks in RDMA-aware memory access without CPU involvement. Additionally, a dynamic Node Manager allocates resources elastically across workflow stages in response to real-time load. Experimental results demonstrate that OnePiece reduces GPU resource consumption by 16x in Wan2.1 image-to-video generation compared to monolithic inference pipelines, offering a scalable, fault-tolerant, and efficient solution for production AIGC environments. |
| title | OnePiece: A Large-Scale Distributed Inference System with RDMA for Complex AI-Generated Content (AIGC) Workflows |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2601.20655 |