{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Model Parallelism — 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;\">Model Parallelism</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A distributed training strategy where different parts of a model are placed on different processors or machines, rather than replicating the whole model. Model parallelism is necessary when a model is too large to fit in the memory of a single device.  See also: data parallelism, distributed training.</p><a href=\"https://gaks.ai/glossary/model-parallelism\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/model-parallelism →</a></div>"}