1 citations · 1 across the 5 of their papers we have counts for
5 papers
DualMap: Enabling Both Cache Affinity and Load Balancing for Distributed LLM Serving
Ying Yuan, Pengfei Zuo, Bo Wang +3
In LLM serving, reusing the KV cache of prompts across requests is critical for reducing TTFT and serving costs. Cache-affinity scheduling, which co-locates requests with the same…
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…
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…
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…
Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention
Bin Gao, Zhuomin He, Puru Sharma +6
Interacting with humans through multi-turn conversations is a fundamental feature of large language models (LLMs). However, existing LLM serving engines executing multi-turn conver…