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WiSP enables large MoE models on low-resource GPUs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed WiSP (Working-Set Paging), a novel system designed to enable large Mixture-of-Experts (MoE) models to run on low-resource hardware like a 24GB RTX 3090 GPU. WiSP treats MoE serving as a working-set problem, managing the competition between routed expert weights and the KV cache for limited VRAM. The system achieves up to twice the decode throughput of static offload methods under the same memory constraints. Additionally, a memory allocation strategy called MV-WSA (Marginal-Value Working-Set Allocation) was introduced to dynamically split VRAM between resident experts and the KV cache, improving performance for both prefill and decode operations. AI

IMPACT Enables deployment of large MoE models on consumer-grade hardware, potentially lowering the barrier for local AI applications.

RANK_REASON The cluster contains an academic paper detailing a new technical approach for serving large AI models. [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 →

WiSP enables large MoE models on low-resource GPUs

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The cluster contains an academic paper detailing a new technical approach for serving large AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiamu Zhang, Liang Wu, Mayank Darbari, Liangjie Hong ·

    WiSP: A Working-Set View of Mixture-of-Experts Serving on Extremely Low-Resource Hardware

    arXiv:2606.21868v2 Announce Type: replace Abstract: Modern local and agentic workloads often need large-model capacity at low concurrency, but run on GPUs that cannot keep a frontier-scale model resident. Mixture-of-Experts (MoE) models are a natural fit because they activate onl…

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