[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-think-in-english-act-in-korean-a-111b-agent-built-to-fit-one-gpu":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},3044,"think-in-english-act-in-korean-a-111b-agent-built-to-fit-one-gpu","Think in English, Act in Korean: A 111B Agent Built to Fit One GPU","Cohere and LG CNS adapted a 111-billion-parameter model to handle Korean enterprise tasks without requiring a multi-GPU server farm.","A 111-billion-parameter model that reasons in English but responds in Korean just landed on arXiv, built by Cohere and LG CNS for real enterprise deployment constraints.\n\nThe model, called LuckyStar 111B, starts from Cohere's Command A and never touches pretraining — the team layered on multilingual fine-tuning, reinforcement learning with verifiable rewards, language-consistency nudges to keep user-facing output in Korean, and 4-bit quantization to squeeze the whole thing onto a single GPU. A preamble-conditioning trick lets the model toggle between terse instruction-following and longer tool-oriented reasoning on demand. The result improves math reasoning, function calling, and natural-language-to-SQL performance while holding steady on general Korean and English quality.\n\nThe 4-bit quantization angle is the part that actually matters to enterprise buyers. Cutting a 111B model to single-GPU serving removes a significant infrastructure barrier for companies that want capable agents without renting a rack. The language-consistency reward is also notable: without it, models trained on English-heavy data tend to drift into English mid-response even when the user asked in Korean — a subtle failure mode that erodes trust fast.\n\nCohere has been pitching Command A as a practical enterprise model rather than a benchmark chaser; LuckyStar fits that story. Whether the recipe generalizes cleanly to other language pairs beyond Korean-English is the question this paper leaves open.","[\"ai\",\"multilingual\",\"enterprise\",\"llm\"]","2026-07-01T04:00:00.000Z","2026-07-01T05:51:12.972Z","2026-07-01T05:51:15.854Z","published",null,[],"ai",[24,26,27,28],"multilingual","enterprise","llm",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.31648",0,{"sections":35},[36,40,45,50,55,60,65,70,75,80,85,89,94,99],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":69},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":71,"slug":72,"count":73,"latest_published_at":74},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":86,"slug":87,"count":83,"latest_published_at":88},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]