{"id":87806,"date":"2025-10-11T18:00:09","date_gmt":"2025-10-12T01:00:09","guid":{"rendered":"https:\/\/phisonblog.com\/?p=87806"},"modified":"2025-11-19T16:51:41","modified_gmt":"2025-11-20T00:51:41","slug":"how-to-ensure-ai-accuracy-starts-in-the-storage-stack","status":"publish","type":"post","link":"https:\/\/phisonblog.com\/ko\/how-to-ensure-ai-accuracy-starts-in-the-storage-stack\/","title":{"rendered":"AI \uc815\ud655\ub3c4\ub97c \uc2a4\ud1a0\ub9ac\uc9c0 \uc2a4\ud0dd\uc5d0\uc11c \uc2dc\uc791\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

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As enterprises race to improve AI accuracy, many focus on bigger models and more GPUs, but the real opportunity may lie deeper in the data pipeline.<\/p>\n

In a recent Forbes<\/a> article, Phison CEO Michael Wu explores how the storage layer can play an active role in improving AI model accuracy. By offloading key training tasks like error reduction to SSDs equipped with lightweight compute, organizations can unlock significant gains in throughput, power efficiency, and reliability, without rewriting their training pipelines or adding new hardware.<\/p>\n

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As enterprises race to improve AI accuracy, many focus on bigger models and more GPUs, but the real opportunity may lie deeper in the data pipeline. In a recent Forbes article, Phison CEO Michael Wu explores how the storage layer can play an active role in improving AI model accuracy. By offloading key training tasks […]<\/p>\n","protected":false},"author":4,"featured_media":87808,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"

As enterprises race to improve AI accuracy, many focus on bigger models and more GPUs, but the real opportunity may lie deeper in the data pipeline.<\/p>

In a recent Forbes<\/a> article, Phison CEO Michael Wu explores how the storage layer can play an active role in improving AI model accuracy. By offloading key training tasks like error reduction to SSDs equipped with lightweight compute, organizations can unlock significant gains in throughput, power efficiency, and reliability, without rewriting their training pipelines or adding new hardware.<\/p>","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"categories":[23,118],"tags":[22],"class_list":["post-87806","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-posts","category-thought-leadership","tag-long-content"],"acf":[],"_links":{"self":[{"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/posts\/87806","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/comments?post=87806"}],"version-history":[{"count":6,"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/posts\/87806\/revisions"}],"predecessor-version":[{"id":87815,"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/posts\/87806\/revisions\/87815"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/media\/87808"}],"wp:attachment":[{"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/media?parent=87806"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/categories?post=87806"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/phisonblog.com\/ko\/wp-json\/wp\/v2\/tags?post=87806"}],"curies":[{"name":"\uc6cc\ub4dc\ud504\ub808\uc2a4","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}