8 citations · 20 across the 13 of their papers we have counts for
6 papers · 1 filter
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs
Hao Kang, Srikant Bharadwaj, James Hensman +3
Large language model (LLM) inference demands significant amount of computation and memory, especially in the key attention mechanism. While techniques, such as quantization and acc…
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…
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning
Shivam Shandilya, Menglin Xia, Supriyo Ghosh +4
The increasing prevalence of large language models (LLMs) such as GPT-4 in various applications has led to a surge in the size of prompts required for optimal performance, leading…
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…
Lean Attention: Hardware-Aware Scalable Attention Mechanism for the Decode-Phase of Transformers
Rya Sanovar, Srikant Bharadwaj, Renee St. Amant +2
Transformer-based models have emerged as one of the most widely used architectures for natural language processing, natural language generation, and image generation. The size of t…
Risk-aware Adaptive Virtual CPU Oversubscription in Microsoft Cloud via Prototypical Human-in-the-loop Imitation Learning
Lu Wang, Mayukh Das, Fangkai Yang +11
Oversubscription is a prevalent practice in cloud services where the system offers more virtual resources, such as virtual cores in virtual machines, to users or applications than…