Orchestrating GPU-based distributed training workloads on AI Hypercomputer
When it comes to AI, large language models (LLMs) and machine learning (ML) are taking entire industries to the next level. But with larger models and datasets, developers need distributed environments that span multiple AI accelerators (e.g. GPUs and TPUs) across multiple compute hosts to train their models efficiently. This can lead to orchestration, resource …
Read more “Orchestrating GPU-based distributed training workloads on AI Hypercomputer”