The high computational and memory requirements of large language model (LLM) inference
make it feasible only with multiple high-end accelerators. Motivated by the emerging …

The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a
groundbreaking paradigm for intelligent edge devices. With their capacity for human-like …

Many NLP tasks benefit from using large language models (LLMs) that often have more than
100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can …

The rapid growth of memory and computation requirements of large language models
(LLMs) has outpaced the development of hardware, hindering people who lack large-scale …

Serving foundation model inference is a pivotal component of contemporary AI applications,
where this service is usually hosted in a centralized data center on a group of homogeneous …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

Decentralized training of large language models has emerged as an effective way to
democratize this technology. However, the potential threats associated with this approach …

Large Language Models (LLMs) like GPT and LLaMA are revolutionizing the AI industry with
their sophisticated capabilities. Training these models requires vast GPU clusters and …

With rapidly increasing distributed deep learning workloads in large-scale data centers,
efficient distributed deep learning framework strategies for resource allocation and workload …