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DualPace runtime boosts LLM serving throughput with GPU-CPU cooperation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DualPace, a new LLM serving runtime designed to improve inference throughput and reduce latency. DualPace decouples scheduling between GPU and CPU engines, allowing the CPU to independently process requests and utilize host memory more effectively. This cooperative approach enhances serving throughput by up to 90.6% compared to existing systems like vLLM and NEO, while maintaining comparable latency. AI

IMPACT DualPace's approach could significantly improve the efficiency and cost-effectiveness of deploying large language models.

RANK_REASON The cluster describes a new research paper detailing a novel system for LLM serving. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DualPace runtime boosts LLM serving throughput with GPU-CPU cooperation

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The cluster describes a new research paper detailing a novel system for LLM serving. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · YuXuan Lin, KeQiang Wu, Kingsum Chow ·

    DualPace: Decoupled Scheduling and Cooperative GPU--CPU Decoding for LLM Serving

    arXiv:2610.33501v1 Announce Type: cross Abstract: Large language model (LLM) serving relies on batching to improve inference throughput, but the key-value (KV) cache grows with sequence length and limits GPU-resident concurrency. CPU offloading can expand KV capacity using host m…

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