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Found 7 papers, 1 papers with code
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
Learned Best-Effort LLM Serving
Many applications must provide low-latency LLM service to users or risk unacceptable user experience.
Online Speculative Decoding
We develop a prototype of online speculative decoding based on online knowledge distillation and evaluate it using both synthetic and real query data on several popular LLMs.
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
Large Language Models (LLMs) have showcased remarkable impacts across a wide spectrum of natural language processing tasks.
An Evaluation of Memory Optimization Methods for Training Neural Networks
To address the challenge, this paper summarizes the scenarios in which MOMs prove advantageous for model training.
GACT: Activation Compressed Training for Generic Network Architectures
Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.
Long-run User Value Optimization in Recommender Systems through Content Creation Modeling
Content recommender systems are generally adept at maximizing immediate user satisfaction but to optimize for the \textit{long-run} user value, we need more statistically sophisticated solutions than off-the-shelf simple recommender algorithms.
