Training large AI models on numerous GPUs consumes a massive amount of energy,
making power delivery one of the largest limiting factors in building and operating …

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 …

Training large Deep Neural Network (DNN) models requires thousands of GPUs over the
course of several days or weeks. At this scale, failures are frequent and can have a big …

The commoditization of machine learning is fuelling the demand for compute required to
both train large models and infer from them. At the same time, scaling the performance of …

Training large Deep Neural Network (DNN) models requires thousands of GPUs for days or
weeks at a time. At these scales, failures are frequent and can have a big impact on training …

As large language models scale, distributed training systems increasingly rely on thousands
of GPUs running for days or weeks. Fault tolerance is essential and periodic model …

Checkpointing (C) and restoring (R) are key components for GPU tasks. POS is an OS-level
GPU C/R system: It can transparently checkpoint or restore processes that use the GPU …

We present a rack-scale compute architecture for ML using multi-accelerator servers
connected via chip-to-chip silicon photonic components. Our architecture achieves (1) multi …

A quantum operating system (QCOS) is a classic software running on classic hardware. The
QCOS is preparing, starting, controlling and managing quantum computations. The reliable …