3 citations · 3 across the 4 of their papers we have counts for
4 papers
CloakLM: Obfuscating GPU Memory Layout to Mitigate Model Ex-filtration for Serving
Kunal Jain, Seokjin Go, Divya Mahajan
Large foundation models deployed on third-party and shared accelerator infrastructure face a practical risk of model exfiltration that existing defenses do not fully address. In co…
SageServe: Optimizing LLM Serving on Cloud Data Centers with Forecast Aware Auto-Scaling
Shashwat Jaiswal, Kunal Jain, Yogesh Simmhan +9
Global cloud service providers handle inference workloads for Large Language Models (LLMs) that span latency-sensitive (e.g., chatbots) and insensitive (e.g., report writing) tasks…
Ensuring Fair LLM Serving Amid Diverse Applications
Redwan Ibne Seraj Khan, Kunal Jain, Haiying Shen +12
In a multi-tenant large language model (LLM) serving platform hosting diverse applications, some users may submit an excessive number of requests, causing the service to become una…
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…