92 citations · 183 across the 27 of their papers we have counts for
7 papers · 1 filter
DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving
Yuhan Liu, Yuyang Huang, Jiayi Yao +9
Compound AI systems, such as agentic systems, are an emerging trend in large-scale enterprise settings, with multiple LLMs specialized for different users, tasks, and/or roles work…
Input-Dependent Power Usage in GPUs
Theo Gregersen, Pratyush Patel, Esha Choukse
GPUs are known to be power-hungry, and due to the boom in artificial intelligence, they are currently the major contributors to the high power demands of upcoming datacenters. Most…
No Request Left Behind: Tackling Heterogeneity in Long-Context LLM Inference with Medha
Amey Agrawal, Haoran Qiu, Junda Chen +6
Deploying million-token Large Language Models (LLMs) is challenging because production workloads are highly heterogeneous, mixing short queries and long documents. This heterogenei…
Intelligent Router for LLM Workloads: Improving Performance Through Workload-Aware Load Balancing
Kunal Jain, Anjaly Parayil, Ankur Mallick +10
Large Language Model (LLM) workloads have distinct prefill and decode phases with different compute and memory requirements which should ideally be accounted for when scheduling in…
DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency
Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri +2
The rapid evolution and widespread adoption of generative large language models (LLMs) have made them a pivotal workload in various applications. Today, LLM inference clusters rece…
Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference
Jovan Stojkovic, Esha Choukse, Chaojie Zhang +2
With the ubiquitous use of modern large language models (LLMs) across industries, the inference serving for these models is ever expanding. Given the high compute and memory requir…