{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Distributed Training — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Distributed Training</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A training approach that spreads the work of training a large model across multiple machines or processors working in parallel. Distributed training is essential for the largest modern AI models, which would take impractically long to train on a single machine. It encompasses both data parallelism and model parallelism strategies.  See also: data parallelism, GPU, model parallelism.</p><a href=\"https://gaks.ai/glossary/distributed-training\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/distributed-training →</a></div>"}