{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"DEV","title":"RevNets: Train Deeper Models Without Running Out of GPU Memory","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/5fb3d414\"></iframe>","width":"100%","height":180,"duration":452,"description":"RevNets flip the script on GPU memory limits by reconstructing activations on the fly instead of caching them — slashing memory use by 40–50% so you can train deeper models on the hardware you already own.","thumbnail_url":"https://img.transistorcdn.com/FjCd-OuusfvO3o_XEB1lBI9M3jCiMFpn2OICEsvCyrs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9kYzVl/MjVhMjFhZGZhOTg4/Zjc1YTFlMGNkZWE1/ZmVhMi5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}