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cs.DC2025
Prefill-Decode Aggregation or Disaggregation? Unifying Both for Goodput-Optimized LLM Serving
Chao Wang, Pengfei Zuo, Zhangyu Chen +3
An ongoing debate considers whether prefill-decode (PD) aggregation or disaggregation is superior for serving large language models (LLMs). This has driven optimizations for both a…
cs.DC2025
Huawei Cloud Model-as-a-Service on the CloudMatrix384 SuperPod
Ao Xiao, Bangzheng He, Baoquan Zhang +125
Scaled-out MoE LLMs and scaled-up SuperPods create new systems challenges for production Model-as-a-Service (MaaS), requiring disaggregation, low-latency communication, and decentr…
cs.DC2025
Injecting Adrenaline into LLM Serving: Boosting Resource Utilization and Throughput via Attention Disaggregation
Yunkai Liang, Zhangyu Chen, Pengfei Zuo +3
In large language model (LLM) serving systems, executing each request consists of two phases: the compute-intensive prefill phase and the memory-intensive decoding phase. To preven…