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 …

The widespread adoption of ML has led to a high demand for GPU hardware and
consequently, severe shortages of GPUs in the public cloud. Allocating a sufficient number …

Sparsely-activated Mixture-of-Experts (MoE) architecture has increasingly been adopted to
further scale large language models (LLMs) due to its sub-linear scaling for computation …

AI workloads, often hosted in multi-tenant cloud environments, require vast computational
resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants lack …

Decentralized training has become a resource-efficient framework to democratize the
training of large language models (LLMs). However, the privacy risks associated with this …

As the scale and complexity of distributed workloads grows, performance is no longer the
sole objective sought by application developers and infrastructure operators, as they …

The advent of cloud computing has led to a dramatic increase in the deployment of hyper-
scale, diverse workloads in containerized form on cloud infrastructures. This expansion …