Enhancing Goodput in Large-Scale LLM Training with Nonuniform Tensor Parallelism
Training LLMs at massive scale brings unique infrastructure challenges, especially as jobs span thousands of GPUs and run for extended periods. The longer these...
Training LLMs at massive scale brings unique infrastructure challenges, especially as jobs span thousands of GPUs and run for extended periods. The longer these jobs run, the greater the likelihood of encountering unscheduled interruptions or resource fluctuations. Even infrequent device unavailability can have outsized effects on tightly interconnected clusters, resulting in slowdowns for a givenβ¦
GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advancesβincluding high bandwidth memory (HBM), NVIDIA...
AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on...
As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization, an...