{"@context":"https://neupai.io/schema/v0.2","@type":"StructuredNewsArticle","identity":{"article_id":"tech42_20260325_google-turboquant-ai-compression","canonical_url":"https://www.tech42.co.kr/%ea%b5%ac%ea%b8%80-ai-%ec%95%95%ec%b6%95-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98-%ed%84%b0%eb%b3%b4%ed%80%80%ed%8a%b8-%ea%b3%b5%ea%b0%9c%eb%a9%94%eb%aa%a8%eb%a6%ac-6%eb%b0%b0-%ec%a4%84/","ai_url":null,"publisher":{"name":"테크42","domain":"tech42.co.kr","type":"online"},"author":"버트","published_at":"2026-03-25T22:45:34.000Z","updated_at":null,"language":"en","article_type":"straight_news","originality":"self_produced"},"content":{"headline":"Google unveils AI compression algorithm 'TurboQuant'... reduces memory by 6x and improves speed by 8x","summary":"Google Research has unveiled a compression algorithm called 'TurboQuant' that reduces AI model memory usage by more than 6 times and improves processing speed by 8 times. This technology is expected to significantly improve the efficiency of large language models and vector search engines.","topics":["artificial intelligence","algorithm","technology","google"],"geography":["US"],"entities":[{"name":"Google Research","canonical_id":"org:us:google-research","type":"organization","role_in_article":"primary_subject","metadata":{"parent":"corp:us:alphabet"}},{"name":"TurboQuant","canonical_id":"product:us:turboquant","type":"product","role_in_article":"primary_subject","metadata":{"parent":null}},{"name":"ICLR 2026","canonical_id":"org:xx:iclr-2026","type":"organization","role_in_article":"mentioned","metadata":{"parent":null}},{"name":"NVIDIA","canonical_id":"corp:us:nvidia","type":"company","role_in_article":"mentioned","metadata":{"ticker":"NVDA","parent":null}},{"name":"Gemini","canonical_id":"product:us:gemini","type":"product","role_in_article":"mentioned","metadata":{"parent":"corp:us:alphabet"}}],"claims":[{"id":"c1","statement":"Google Research unveiled a compression algorithm called 'TurboQuant' that dramatically reduces AI model memory usage on March 24","as_of":"2026-03-24","as_of_explicit":true,"as_of_raw":"March 24","source_type":"company_disclosure","comparison":null,"type":"fact","figures":null,"expiry_hint":null},{"id":"c2","statement":"TurboQuant is scheduled to be presented at the AI academic conference ICLR 2026 held this year","as_of":"2026","as_of_explicit":false,"as_of_raw":"this year","source_type":"company_plan","comparison":null,"type":"future_plan","figures":null,"expiry_hint":"2026-12"},{"id":"c3","statement":"TurboQuant compressed key-value cache down to 3 bits while maintaining no accuracy loss whatsoever","as_of":"2026-03","as_of_explicit":false,"as_of_raw":"March 2026","source_type":"research_paper","comparison":null,"type":"fact","figures":{"value":3,"unit":"비트","approximate":false,"converted":null},"expiry_hint":null},{"id":"c4","statement":"When 4-bit TurboQuant was applied on NVIDIA H100 GPU, it recorded up to 8 times faster processing speed compared to 32-bit uncompressed method","as_of":"2026-03","as_of_explicit":false,"as_of_raw":"March 2026","source_type":"research_paper","comparison":"32bit_uncompressed","type":"fact","figures":{"value":8,"unit":"배","approximate":false,"converted":null},"expiry_hint":null},{"id":"c5","statement":"Memory usage was reduced by more than 6 times compared to existing methods","as_of":"2026-03","as_of_explicit":false,"as_of_raw":"March 2026","source_type":"research_paper","comparison":"previous_method","type":"fact","figures":{"value":6,"unit":"배","approximate":true,"converted":null},"expiry_hint":null}]},"provenance":{"source_chain":["primary_reporting"],"original_source_url":null,"related_articles":[]},"temporal":{"freshness":"archival","next_update_expected":null},"access":{"license":"neupai_standard","attribution_required":true,"structured_data":"free","full_text_available":false,"full_text_access":null}}