Author

Wei-Ling Yang

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Preprint Oct 2026

PDScope: Breaking the Prefill-Decode Seesaw for Memory-Bound MoE Inference

Local deployment of MoE-based LLMs on CPU-GPU nodes is attractive for cost and privacy reasons, yet limited GPU memory forces most experts into CPU memory. Prefill is then compute-bound and benefits from aggressive GPU loading, while decode is I/O-bound and requires conservative PCIe transfers. These demands cannot be...

En-Da Yu, De-Zun Dong, Zhao-Ning Zhang et al. · 0 citations

Preprint Oct 2026

HiNa-MoE: High-Performance, Non-Intrusive MoE Inference on CPUs with Matrix Engines

Mixture-of-Experts (MoE) inference is increasingly deployed in local and on-premise environments, where expert parameters often exceed GPU memory capacity. In latency-sensitive, low-concurrency settings, repeatedly staging routed-expert weights from CPU memory to the GPU can be prohibitive, leaving routed-expert feed-f...

Wei-Ling Yang, Jun-Wen Zhang, De-Zun Dong et al. · 0 citations

Book Open access Sep 2026

AsymFlow: Enabling Long-Context LLM Serving via CPU-GPU Prefill-Decode Disaggregation

This work presents AsymFlow, a prefill-decode disaggregated serving system that runs prefill on the GPU and decode on the CPU, and implements on SGLang and evaluated on a CPU-GPU platform.

Jun-Wen Zhang, Wei-Ling Yang, Jian-Bin Fang et al. · Proceedings of the Internati... · 1 citation

#large language models Book Open access Sep 2026

AsymFlow: Enabling Long-Context LLM Serving via CPU-GPU Prefill-Decode Disaggregation

Driven by cost and privacy constraints, many organizations deploy large language model (LLM) inference on local CPU-GPU servers. For long-context requests, the key-value (KV) cache grows linearly with sequence length and can rapidly exhaust GPU memory, reducing both the maximum supported context and request concurrency...

Jun-Wen Zhang, Wei-Ling Yang, Jian-Bin Fang et al. · Proceedings of the Internati... · 0 citations