Paper
Serving Hybrid LLM Loads with SLO Guarantees Using CPU-GPU Attention Piggybacking
Authors
Zizhao Mo, Junlin Chen, Huanle Xu, Chengzhong Xu
Abstract
Nowadays, service providers often deploy multiple types of LLM services within shared clusters. While the service colocation improves resource utilization, it introduces significant interference risks for latency-sensitive (LS) services-which have strict SLO requirements for inference latency-and severely constrain the service capacity of best-effort (BE) services due to limited available memory. To address interference, existing systems typically rely on reserving headroom to constrain BE resource usage. However, this approach's coarse granularity compromises the SLO compliance of the latency-sensitive service and unnecessarily restricts the generation potential of the best effort service. In this paper, we propose OmniServe, a novel LLM serving system that efficiently harnesses both CPU and GPU resources to mitigate interference and improve throughput. Central to OmniServe is the Attention Piggybacking mechanism, which effectively offloads the Attention computation of BE services to CPUs on the fly. This mechanism also facilitates asynchronous communication between CPU and GPU streams, preventing GPUs from being blocked while aggregating Attention results. Additionally, OmniServe incorporates a dynamic batching control policy to adapt to fluctuating request arrivals, facilitating Dense module computation using layer-wise batching. Experimental results show that OmniServe improves the SLO attainment rate for LS services by up to $1.48\times$ while enhancing BE serving throughput by up to $9.85\times$ compared to state-of-the-art systems.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.12831v1</id>\n <title>Serving Hybrid LLM Loads with SLO Guarantees Using CPU-GPU Attention Piggybacking</title>\n <updated>2026-03-13T09:32:56Z</updated>\n <link href='https://arxiv.org/abs/2603.12831v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.12831v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Nowadays, service providers often deploy multiple types of LLM services within shared clusters. While the service colocation improves resource utilization, it introduces significant interference risks for latency-sensitive (LS) services-which have strict SLO requirements for inference latency-and severely constrain the service capacity of best-effort (BE) services due to limited available memory. To address interference, existing systems typically rely on reserving headroom to constrain BE resource usage. However, this approach's coarse granularity compromises the SLO compliance of the latency-sensitive service and unnecessarily restricts the generation potential of the best effort service.\n In this paper, we propose OmniServe, a novel LLM serving system that efficiently harnesses both CPU and GPU resources to mitigate interference and improve throughput. Central to OmniServe is the Attention Piggybacking mechanism, which effectively offloads the Attention computation of BE services to CPUs on the fly. This mechanism also facilitates asynchronous communication between CPU and GPU streams, preventing GPUs from being blocked while aggregating Attention results. Additionally, OmniServe incorporates a dynamic batching control policy to adapt to fluctuating request arrivals, facilitating Dense module computation using layer-wise batching. Experimental results show that OmniServe improves the SLO attainment rate for LS services by up to $1.48\\times$ while enhancing BE serving throughput by up to $9.85\\times$ compared to state-of-the-art systems.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.DC'/>\n <published>2026-03-13T09:32:56Z</published>\n <arxiv:primary_category term='cs.DC'/>\n <author>\n <name>Zizhao Mo</name>\n </author>\n <author>\n <name>Junlin Chen</name>\n </author>\n <author>\n <name>Huanle Xu</name>\n </author>\n <author>\n <name>Chengzhong Xu</name>\n </author>\n <arxiv:doi>10.1145/3802107</arxiv:doi>\n <link href='https://doi.org/10.1145/3802107' rel='related' title='doi'/>\n </entry>"
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