\ucd5c\uadfc \uba87 \ub144\uac04 \uc778\uacf5\uc9c0\ub2a5(AI) \uae30\uc220\uc740 \ub208\ubd80\uc2e0 \ubc1c\uc804\uc744 \uac70\ub4ed\ud574 \uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uac70\ub300 \uc5b8\uc5b4 \ubaa8\ub378(LLM)\uc758 \ub4f1\uc7a5\uc740 AI\uac00 \ud560 \uc218 \uc788\ub294 \uc77c\uc758 \ubc94\uc704\ub97c \ud601\uc2e0\uc801\uc73c\ub85c \ub113\ud614\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc774\ub7ec\ud55c \ubc1c\uc804\uc758 \uc774\uba74\uc5d0 ‘\uc624\ud508 \ubaa8\ub378’\uc758 \ubc18\uaca9\uc774 \uc2dc\uc791\ub418\uace0 \uc788\ub2e4\ub294 \uc810\uc5d0 \uc8fc\ubaa9\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uacfc\uac70\uc5d0\ub294 \uc18c\uc218\uc758 \uac70\ub300 \uae30\uc220 \uae30\uc5c5\ub9cc\uc774 \ub9c9\ub300\ud55c \uc790\ubcf8\uacfc \ucef4\ud4e8\ud305 \ud30c\uc6cc\ub97c \ud22c\uc785\ud558\uc5ec \ucd5c\ucca8\ub2e8 AI \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0 \uc18c\uc720\ud560 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc774\uc81c\ub294 \uc624\ud508 \ubaa8\ub378 \ucee4\ubba4\ub2c8\ud2f0\uc758 \ud65c\ubc1c\ud55c \ud65c\ub3d9 \ub355\ubd84\uc5d0 \uc77c\ubc18 \uc0ac\uc6a9\uc790\ub4e4\ub3c4 \uc790\uc2e0\uc758 \ucef4\ud4e8\ud130, \uc989 ‘\ub85c\uceec \ud658\uacbd’\uc5d0\uc11c \ucd5c\uc2e0 AI \ubaa8\ub378\uc744 \uc9c1\uc811 \uad6c\ub3d9\ud560 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774\ub7ec\ud55c \ubcc0\ud654\ub294 \ub2e8\uc21c\ud788 \uae30\uc220\uc801\uc778 \uc9c4\ubcf4\ub97c \ub118\uc5b4 AI \uae30\uc220\uc758 \uc811\uadfc\uc131\uc744 \ub192\uc774\uace0, \uac1c\uc778 \uc815\ubcf4 \ubcf4\ud638, \ube44\uc6a9 \ud6a8\uc728\uc131, \ub9de\ucda4 \uc124\uc815 \ub4f1 \ub2e4\uc591\ud55c \uce21\uba74\uc5d0\uc11c \uc911\uc694\ud55c \uc758\ubbf8\ub97c \uc9c0\ub2d9\ub2c8\ub2e4. \ub9c8\uce58 \uac1c\uc778\uc6a9 \ucef4\ud4e8\ud130(PC)\uac00 \uac70\ub300 \uba54\uc778\ud504\ub808\uc784 \uc2dc\ub300\ub97c \ub05d\ub0b4\uace0 \uc815\ubcf4 \uae30\uc220\uc758 \ub300\uc911\ud654\ub97c \uc774\ub04c\uc5c8\ub358 \uac83\ucc98\ub7fc, \ub85c\uceec \uad6c\ub3d9 \uac00\ub2a5\ud55c \uc624\ud508 \ubaa8\ub378 AI\ub294 AI \uae30\uc220\uc758 \ubbfc\uc8fc\ud654\ub97c \uac00\uc18d\ud654\ud560 \uc7a0\uc7ac\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uacfc\uac70\uc5d0\ub294 AI \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uae30 \uc704\ud574 \ud074\ub77c\uc6b0\ub4dc \uae30\ubc18 \uc11c\ube44\uc2a4\uc5d0 \uc758\uc874\ud558\ub294 \uac83\uc774 \uc77c\ubc18\uc801\uc774\uc5c8\uc2b5\ub2c8\ub2e4. OpenAI\uc758 ChatGPT, Google\uc758 Bard(\ud604 Gemini)\uc640 \uac19\uc740 \uc11c\ube44\uc2a4\ub294 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \uc81c\uacf5\ud558\uc9c0\ub9cc, \ub370\uc774\ud130\ub97c \uc678\ubd80 \uc11c\ubc84\ub85c \uc804\uc1a1\ud574\uc57c \ud55c\ub2e4\ub294 \uc810\uc5d0\uc11c \uac1c\uc778 \uc815\ubcf4 \ubcf4\ud638\uc5d0 \ub300\ud55c \uc6b0\ub824\uac00 \uc81c\uae30\ub418\uace4 \ud588\uc2b5\ub2c8\ub2e4. \ub610\ud55c, API \uc0ac\uc6a9\ub8cc\ub098 \uad6c\ub3c5\ub8cc\uc640 \uac19\uc740 \ube44\uc6a9 \ubd80\ub2f4\ub3c4 \uc874\uc7ac\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n
\ud558\uc9c0\ub9cc \uc624\ud508 \ubaa8\ub378 AI\uac00 \ub85c\uceec \ud658\uacbd\uc5d0\uc11c \uad6c\ub3d9 \uac00\ub2a5\ud574\uc9c0\uba74\uc11c \uc774\ub7ec\ud55c \ubb38\uc81c\uc810\ub4e4\uc744 \uc0c1\ub2f9 \ubd80\ubd84 \ud574\uacb0\ud560 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ub85c\uceec AI \uad6c\ub3d9\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uc5ec\ub7ec \uac00\uc9c0 \uc774\uc810\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n
\uac00\uc7a5 \ud070 \uc774\uc810 \uc911 \ud558\ub098\ub294 \uac1c\uc778 \uc815\ubcf4 \ubcf4\ud638\uc785\ub2c8\ub2e4. \ub85c\uceec AI\ub294 \uc0ac\uc6a9\uc790\uc758 \ucef4\ud4e8\ud130 \ub0b4\uc5d0\uc11c \ubaa8\ub4e0 \uc5f0\uc0b0\uc744 \ucc98\ub9ac\ud569\ub2c8\ub2e4. \uc989, \ubbfc\uac10\ud55c \uc815\ubcf4\ub098 \uac1c\uc778\uc801\uc778 \uc9c8\ubb38\uc744 \uc678\ubd80 \uc11c\ubc84\ub85c \uc804\uc1a1\ud560 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uae30\uc5c5\uc758 \ub0b4\ubd80 \ub370\uc774\ud130, \uac1c\uc778\uc801\uc778 \uc77c\uae30, \ucc3d\uc791\ubb3c \ub4f1 \uc678\ubd80 \uc720\ucd9c\uc774 \uc5fc\ub824\ub418\ub294 \ub370\uc774\ud130\ub97c AI\uc640 \ud568\uaed8 \ud65c\uc6a9\ud560 \ub54c \ub9e4\uc6b0 \uc911\uc694\ud55c \uc7a5\uc810\uc785\ub2c8\ub2e4. \ub370\uc774\ud130 \ud504\ub77c\uc774\ubc84\uc2dc\uac00 \uc810\uc810 \ub354 \uc911\uc694\ud574\uc9c0\ub294 \uc2dc\ub300\uc5d0 \ub85c\uceec AI\ub294 \uc0ac\uc6a9\uc790\uc5d0\uac8c \ub354 \ud070 \ud1b5\uc81c\uad8c\uc744 \ubd80\uc5ec\ud569\ub2c8\ub2e4.<\/p>\n
\ud074\ub77c\uc6b0\ub4dc \uae30\ubc18 AI \uc11c\ube44\uc2a4\ub294 \uc0ac\uc6a9\ub7c9\uc5d0 \ub530\ub77c \ube44\uc6a9\uc774 \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \ud2b9\ud788 \ub300\uaddc\ubaa8 \uc5b8\uc5b4 \ubaa8\ub378\uc744 \ube48\ubc88\ud558\uac8c \uc0ac\uc6a9\ud558\uac70\ub098, API\ub97c \ud1b5\ud574 \uc11c\ube44\uc2a4\ub97c \uc5f0\ub3d9\ud558\ub294 \uacbd\uc6b0 \uc0c1\ub2f9\ud55c \ube44\uc6a9\uc774 \ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc18\uba74, \ub85c\uceec AI\ub294 \ucd08\uae30 \ud558\ub4dc\uc6e8\uc5b4 \ud22c\uc790(\uadf8\ub798\ud53d \uce74\ub4dc \ub4f1) \uc774\ud6c4\uc5d0\ub294 \ucd94\uac00\uc801\uc778 \uc0ac\uc6a9\ub8cc \uc5c6\uc774 \ubaa8\ub378\uc744 \uc790\uc720\ub86d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubb3c\ub860 \uace0\uc131\ub2a5 \ud558\ub4dc\uc6e8\uc5b4\uac00 \ud544\uc694\ud560 \uc218 \uc788\uc9c0\ub9cc, \uc7a5\uae30\uc801\uc73c\ub85c \ubcfc \ub54c \ubc18\ubcf5\uc801\uc778 \uad6c\ub3c5\ub8cc\ub098 \uc0ac\uc6a9\ub8cc \uc9c0\ucd9c\uc744 \uc904\uc77c \uc218 \uc788\ub2e4\ub294 \uc7a5\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub85c\uceec AI\ub294 \uc778\ud130\ub137 \uc5f0\uacb0 \uc5c6\uc774\ub3c4 \uc791\ub3d9\ud569\ub2c8\ub2e4. \uc774\ub294 \uc778\ud130\ub137 \ud658\uacbd\uc774 \ubd88\uc548\uc815\ud558\uac70\ub098, \ubcf4\uc548\uc0c1\uc758 \uc774\uc720\ub85c \uc678\ubd80 \ub124\ud2b8\uc6cc\ud06c \uc5f0\uacb0\uc774 \uc5b4\ub824\uc6b4 \ud658\uacbd\uc5d0\uc11c\ub3c4 AI\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\ub2e4\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc624\ud504\ub77c\uc778 \uc0c1\ud0dc\uc5d0\uc11c\ub3c4 \ubb38\uc11c \uc791\uc131\uc744 \ub3d5\uac70\ub098, \ucf54\ub529\uc744 \uc9c0\uc6d0\ubc1b\uac70\ub098, \uc544\uc774\ub514\uc5b4\ub97c \uc5bb\ub294 \ub4f1 \ub2e4\uc591\ud55c \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc624\ud508 \ubaa8\ub378\uc740 \uc18c\uc2a4 \ucf54\ub4dc\uac00 \uacf5\uac1c\ub418\uc5b4 \uc788\uac70\ub098, \ubaa8\ub378 \uac00\uc911\uce58\uac00 \uacf5\uac1c\ub418\uc5b4 \uc788\uc5b4 \uc0ac\uc6a9\uc790\uac00 \uc790\uc2e0\uc758 \ubaa9\uc801\uc5d0 \ub9de\uac8c \uc218\uc815\ud558\uac70\ub098 \ubbf8\uc138 \uc870\uc815(fine-tuning)\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub85c\uceec \ud658\uacbd\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \uc2e4\ud5d8\uc774 \ub354\uc6b1 \uc6a9\uc774\ud569\ub2c8\ub2e4. \ud2b9\uc815 \ub3c4\uba54\uc778\uc5d0 \ud2b9\ud654\ub41c \ub370\uc774\ud130\ub97c \ud559\uc2b5\uc2dc\ud0a4\uac70\ub098, \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc870\uc815\ud558\uc5ec \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\ub294 \ub4f1 \uc790\uc2e0\ub9cc\uc758 AI \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4\ub098\uac08 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uc5f0\uad6c\uc790, \uac1c\ubc1c\uc790, \ud639\uc740 \ud2b9\uc815 \ubd84\uc57c\uc758 \uc804\ubb38\uac00\ub4e4\uc5d0\uac8c \ub9e4\uc6b0 \ub9e4\ub825\uc801\uc778 \ubd80\ubd84\uc785\ub2c8\ub2e4.<\/p>\n
\uc624\ud508 \ubaa8\ub378\uc758 \ud655\uc0b0\uc740 AI \uae30\uc220 \ubc1c\uc804\uc758 \ud61c\uc548\uc744 \ud2b9\uc815 \uae30\uc5c5\uc5d0\ub9cc \uad6d\ud55c\uc2dc\ud0a4\uc9c0 \uc54a\uace0, \ub354 \ub9ce\uc740 \uc0ac\ub78c\ub4e4\uc5d0\uac8c \uae30\uc220 \uc811\uadfc \uae30\ud68c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ub294 AI \uae30\uc220\uc758 \ud601\uc2e0\uc744 \uac00\uc18d\ud654\ud558\uace0, \ub2e4\uc591\ud55c \uc544\uc774\ub514\uc5b4\uac00 \ubc1c\ud604\ub420 \uc218 \uc788\ub294 \uc0dd\ud0dc\uacc4\ub97c \uc870\uc131\ud558\ub294 \ub370 \uae30\uc5ec\ud569\ub2c8\ub2e4. \uac1c\uc778 \uac1c\ubc1c\uc790\ub098 \uc18c\uaddc\ubaa8 \ud300\ub3c4 \ucd5c\ucca8\ub2e8 AI \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uc11c\ube44\uc2a4\ub098 \uc81c\ud488\uc744 \ub9cc\ub4e4 \uc218 \uc788\uac8c \ub418\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\ub85c\uceec AI\ub97c \uad6c\ub3d9\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba87 \uac00\uc9c0 \uc900\ube44\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \ubaa8\ub4e0 AI \ubaa8\ub378\uc774 \ub3d9\uc77c\ud55c \uc0ac\uc591\uc744 \uc694\uad6c\ud558\ub294 \uac83\uc740 \uc544\ub2c8\uc9c0\ub9cc, \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uc694\uc18c\ub4e4\uc774 \uc911\uc694\ud558\uac8c \uc791\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
\uadf8\ub798\ud53d \uce74\ub4dc (GPU):<\/strong> AI \ubaa8\ub378, \ud2b9\ud788 \ub300\uaddc\ubaa8 \uc5b8\uc5b4 \ubaa8\ub378\uc740 \ubc29\ub300\ud55c \uc591\uc758 \ud589\ub82c \uc5f0\uc0b0\uc744 \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \ucc98\ub9ac\ud558\uae30 \uc704\ud574\uc11c\ub294 \uac15\ub825\ud55c GPU\uac00 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. GPU\uc758 VRAM(\ube44\ub514\uc624 \uba54\ubaa8\ub9ac) \uc6a9\ub7c9\uc774 \ud074\uc218\ub85d \ub354 \ud06c\uace0 \uc131\ub2a5 \uc88b\uc740 \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0 \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. NVIDIA\uc758 RTX \uc2dc\ub9ac\uc988(3000\ubc88\ub300, 4000\ubc88\ub300)\ub098 AMD\uc758 Radeon RX \uc2dc\ub9ac\uc988 \ub4f1 \uace0\uc131\ub2a5 \uadf8\ub798\ud53d \uce74\ub4dc\uac00 \uad8c\uc7a5\ub429\ub2c8\ub2e4.<\/p>\n<\/li>\n RAM (\uba54\uc778 \uba54\ubaa8\ub9ac):<\/strong> GPU VRAM\ub9cc\ud07c \uc911\uc694\ud558\uc9c0\ub294 \uc54a\uc9c0\ub9cc, \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \ucda9\ubd84\ud55c RAM \uc6a9\ub7c9\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ucd5c\uc18c 16GB \uc774\uc0c1, \uac00\ub2a5\ud558\uba74 32GB \uc774\uc0c1\uc744 \uad8c\uc7a5\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n CPU:<\/strong> CPU\ub294 GPU\ub9cc\ud07c \uc911\uc694\ud558\uc9c0 \uc54a\uc9c0\ub9cc, \uc804\ubc18\uc801\uc778 \uc2dc\uc2a4\ud15c \uc131\ub2a5\uacfc \ub370\uc774\ud130 \ub85c\ub529 \uc18d\ub3c4\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \ucd5c\uc2e0 \uba40\ud2f0\ucf54\uc5b4 CPU\uac00 \uc720\ub9ac\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n \uc800\uc7a5 \uacf5\uac04 (SSD):<\/strong> AI \ubaa8\ub378 \ud30c\uc77c\uc740 \uc218 GB\uc5d0\uc11c \uc218\uc2ed GB\uc5d0 \ub2ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ube60\ub974\uac8c \ub85c\ub4dc\ud558\uae30 \uc704\ud574 SSD(Solid State Drive) \uc0ac\uc6a9\uc744 \uad8c\uc7a5\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n \uc6b4\uc601\uccb4\uc81c:<\/strong> Windows, macOS, Linux \ubaa8\ub450 \uc9c0\uc6d0\ub429\ub2c8\ub2e4. \uc0ac\uc6a9\ud558\ub824\ub294 AI \ubaa8\ub378 \ubc0f \ud504\ub808\uc784\uc6cc\ud06c\uc5d0 \ub530\ub77c \ud638\ud658\uc131\uc744 \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n AI \ud504\ub808\uc784\uc6cc\ud06c:<\/strong> PyTorch, TensorFlow\uc640 \uac19\uc740 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\uac00 \ud544\uc694\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n \ubaa8\ub378 \uc2e4\ud589 \ub3c4\uad6c:<\/strong> llama.cpp, Ollama, LM Studio\uc640 \uac19\uc774 \ub85c\uceec\uc5d0\uc11c AI \ubaa8\ub378\uc744 \uc27d\uac8c \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0 \uc2e4\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\ub294 \ub3c4\uad6c\ub4e4\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub3c4\uad6c\ub4e4\uc740 \ubcf5\uc7a1\ud55c \uc124\uc815 \uacfc\uc815\uc744 \uac04\uc18c\ud654\ud558\uc5ec \uc0ac\uc6a9\uc790 \uce5c\ud654\uc801\uc778 \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n \ub85c\uceec\uc5d0\uc11c \uad6c\ub3d9\ud560 \uc218 \uc788\ub294 \uc624\ud508 \ubaa8\ub378\uc740 \ub9e4\uc6b0 \ub2e4\uc591\ud569\ub2c8\ub2e4. \uac01 \ubaa8\ub378\uc740 \ud06c\uae30, \uc131\ub2a5, \ud559\uc2b5 \ub370\uc774\ud130, \ub77c\uc774\uc120\uc2a4 \ub4f1\uc774 \ub2e4\ub985\ub2c8\ub2e4.<\/p>\n Llama 3:<\/strong> Meta\uc5d0\uc11c \uacf5\uac1c\ud55c \ucd5c\uc2e0 \ubaa8\ub378\ub85c, \ub2e4\uc591\ud55c \ud06c\uae30(8B, 70B \ub4f1)\ub85c \uc81c\uacf5\ub418\uc5b4 \ub85c\uceec \ud658\uacbd\uc5d0\uc11c\ub3c4 \ud65c\uc6a9\ub3c4\uac00 \ub192\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n Mistral AI \ubaa8\ub378:<\/strong> Mistral 7B, Mixtral 8x7B \ub4f1 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uacfc \ud6a8\uc728\uc131\uc744 \uc790\ub791\ud558\ub294 \ubaa8\ub378\ub4e4\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n Gemma:<\/strong> Google\uc5d0\uc11c \uacf5\uac1c\ud55c \uacbd\ub7c9 \ubaa8\ub378\ub85c, \uac1c\uc778 \ubc0f \uc5f0\uad6c\uc6a9\uc73c\ub85c \uc0ac\uc6a9\ud558\uae30 \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n Phi-3:<\/strong> Microsoft\uc5d0\uc11c \uacf5\uac1c\ud55c \uc18c\ud615 \uc5b8\uc5b4 \ubaa8\ub378(SLM)\ub85c, \uc800\uc0ac\uc591 \ud658\uacbd\uc5d0\uc11c\ub3c4 \uc88b\uc740 \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n \ubaa8\ub378\uc744 \uc120\ud0dd\ud560 \ub54c\ub294 \uc790\uc2e0\uc758 \ud558\ub4dc\uc6e8\uc5b4 \uc0ac\uc591\uacfc \ud544\uc694\ud55c \uc131\ub2a5\uc744 \uace0\ub824\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130 \uc218\uac00 \ub9ce\uc744\uc218\ub85d \uc131\ub2a5\uc774 \uc88b\uc9c0\ub9cc, \ub354 \ub9ce\uc740 VRAM\uacfc \ucef4\ud4e8\ud305 \ud30c\uc6cc\ub97c \uc694\uad6c\ud569\ub2c8\ub2e4.<\/p>\n \ub85c\uceec AI\ub294 \uc774\ubbf8 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc2e4\uc9c8\uc801\uc778 \uac00\uce58\ub97c \ucc3d\ucd9c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ubb38\uc11c \uc791\uc131 \ubc0f \uc694\uc57d:<\/strong> \uae34 \ubcf4\uace0\uc11c\ub098 \ub17c\ubb38\uc744 \uc694\uc57d\ud558\uac70\ub098, \uc774\uba54\uc77c \ucd08\uc548\uc744 \uc791\uc131\ud558\uac70\ub098, \uc544\uc774\ub514\uc5b4\ub97c \ubc1c\uc804\uc2dc\ud0a4\ub294 \ub370 \ub85c\uceec AI\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac1c\uc778\uc801\uc778 \uba54\ubaa8\ub098 \uc77c\uae30\ub97c AI\uc640 \ud568\uaed8 \uc815\ub9ac\ud558\uace0 \ubd84\uc11d\ud558\ub294 \uac83\ub3c4 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n \ucf54\ub529 \uc9c0\uc6d0:<\/strong> \uac1c\ubc1c\uc790\ub294 \ub85c\uceec AI\ub97c \ud1b5\ud574 \ucf54\ub4dc \uc790\ub3d9 \uc644\uc131, \ubc84\uadf8 \ucc3e\uae30, \ucf54\ub4dc \uc124\uba85 \uc0dd\uc131, \uc0c8\ub85c\uc6b4 \uc5b8\uc5b4 \ud559\uc2b5 \ub4f1 \ub2e4\uc591\ud55c \ub3c4\uc6c0\uc744 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uac1c\ubc1c \uc0dd\uc0b0\uc131\uc744 \ud06c\uac8c \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n<\/li>\n \ud559\uc2b5 \ub3c4\uad6c:<\/strong> \uc0c8\ub85c\uc6b4 \uc9c0\uc2dd\uc744 \uc2b5\ub4dd\ud560 \ub54c, \ubcf5\uc7a1\ud55c \uac1c\ub150\uc744 \uc124\uba85\ubc1b\uac70\ub098, \uad00\ub828 \uc815\ubcf4\ub97c \ud0d0\uc0c9\ud558\ub294 \ub370 AI\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n \uc2a4\ud1a0\ub9ac\ud154\ub9c1 \ubc0f \uae00\uc4f0\uae30:<\/strong> \uc18c\uc124, \uc2dc\ub098\ub9ac\uc624, \uac8c\uc784 \uc2a4\ud1a0\ub9ac \ub4f1 \ucc3d\uc791 \ud65c\ub3d9\uc5d0\uc11c \uc601\uac10\uc744 \uc5bb\uac70\ub098, \uc904\uac70\ub9ac\ub97c \uad6c\uccb4\ud654\ud558\uac70\ub098, \ub300\uc0ac\ub97c \uc0dd\uc131\ud558\ub294 \ub370 AI\uc758 \ub3c4\uc6c0\uc744 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n \uc608\uc220 \ubc0f \ub514\uc790\uc778:<\/strong> \uc774\ubbf8\uc9c0 \uc0dd\uc131 AI \ubaa8\ub378\uc744 \ub85c\uceec\uc5d0\uc11c \uad6c\ub3d9\ud558\uc5ec \uc790\uc2e0\ub9cc\uc758 \ub3c5\ud2b9\ud55c \uc544\ud2b8\uc6cc\ud06c\ub098 \ub514\uc790\uc778 \ucee8\uc149\uc744 \ub9cc\ub4e4\uc5b4\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n \uc74c\uc545 \uc791\uace1:<\/strong> AI\ub97c \ud65c\uc6a9\ud558\uc5ec \uba5c\ub85c\ub514 \uc544\uc774\ub514\uc5b4\ub97c \uc5bb\uac70\ub098, \uc545\uae30 \ud3b8\uace1\uc744 \uc2dc\ub3c4\ud558\ub294 \ub4f1 \uc74c\uc545 \ucc3d\uc791\uc758 \uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uc744 \ud0d0\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n \ub370\uc774\ud130 \ubd84\uc11d:<\/strong> \uac1c\uc778\uc801\uc778 \uc5f0\uad6c\ub098 \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130\ub97c AI\ub85c \ubd84\uc11d\ud558\uc5ec \uc778\uc0ac\uc774\ud2b8\ub97c \ub3c4\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n \ud504\ub85c\ud1a0\ud0c0\uc774\ud551:<\/strong> \uc0c8\ub85c\uc6b4 AI \uae30\ubc18 \uc11c\ube44\uc2a4\ub098 \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc758 \uc544\uc774\ub514\uc5b4\ub97c \ub85c\uceec \ud658\uacbd\uc5d0\uc11c \ube60\ub974\uac8c \ud504\ub85c\ud1a0\ud0c0\uc774\ud551\ud558\uace0 \ud14c\uc2a4\ud2b8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n AI \ubaa8\ub378 \uc5f0\uad6c:<\/strong> \uc624\ud508 \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \uc0c8\ub85c\uc6b4 \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud558\uac70\ub098, \uae30\uc874 \ubaa8\ub378\uc744 \uac1c\uc120\ud558\ub294 \uc5f0\uad6c\ub97c \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n \ub9de\ucda4\ud615 \uc815\ubcf4 \ud050\ub808\uc774\uc158:<\/strong> \uad00\uc2ec \uc788\ub294 \uc8fc\uc81c\uc5d0 \ub300\ud55c \ub274\uc2a4\ub97c \uc790\ub3d9\uc73c\ub85c \uc694\uc57d\ud558\uac70\ub098, \ucd94\ucc9c \ucf58\ud150\uce20\ub97c \uc0dd\uc131\ud558\ub294 \ub4f1 \uc790\uc2e0\uc5d0\uac8c \ucd5c\uc801\ud654\ub41c \uc815\ubcf4 \ud658\uacbd\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n \ucde8\ubbf8 \ud65c\ub3d9 \uc9c0\uc6d0:<\/strong> \uc608\ub97c \ub4e4\uc5b4, \ud2b9\uc815 \uac8c\uc784\uc758 \uacf5\ub7b5 \uc815\ubcf4\ub97c AI\uc5d0\uac8c \uc9c8\ubb38\ud558\uac70\ub098, \uc218\uc9d1\ud488 \ubaa9\ub85d\uc744 \uc815\ub9ac\ud558\ub294 \ub4f1 \uac1c\uc778\uc801\uc778 \ucde8\ubbf8 \ud65c\ub3d9\uc744 \ub354\uc6b1 \ud48d\ubd80\ud558\uac8c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n \ub85c\uceec AI \uad6c\ub3d9\uc740 \ub9ce\uc740 \uc7a5\uc810\uc744 \uac00\uc9c0\uc9c0\ub9cc, \uba87 \uac00\uc9c0 \uc8fc\uc758\ud574\uc57c \ud560 \uc810\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uacfc\ub3c4\ud55c \uae30\ub300:<\/strong> \ub85c\uceec\uc5d0\uc11c \uad6c\ub3d9\ud558\ub294 \ubaa8\ub378\uc740 \ud074\ub77c\uc6b0\ub4dc \uae30\ubc18\uc758 \ucd5c\ucca8\ub2e8 \ubaa8\ub378\ubcf4\ub2e4 \uc131\ub2a5\uc774 \ub5a8\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc800\uc0ac\uc591 \ud558\ub4dc\uc6e8\uc5b4\uc5d0\uc11c\ub294 \ucd5c\uc2e0 \ub300\ud615 \ubaa8\ub378\uc744 \uad6c\ub3d9\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n \ud558\ub4dc\uc6e8\uc5b4 \uc694\uad6c\uc0ac\ud56d:<\/strong> \uc55e\uc11c \uc5b8\uae09\ud588\ub4ef\uc774, \uace0\uc131\ub2a5 AI \ubaa8\ub378\uc744 \uc6d0\ud65c\ud558\uac8c \uad6c\ub3d9\ud558\ub824\uba74 \uc0c1\ub2f9\ud55c \ucef4\ud4e8\ud305 \uc790\uc6d0\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc608\uc0b0\uacfc \ubaa9\uc801\uc5d0 \ub9de\ub294 \ud558\ub4dc\uc6e8\uc5b4\ub97c \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n \uc124\uc815\uc758 \ubcf5\uc7a1\uc131:<\/strong> \uc77c\ubd80 \uc0ac\uc6a9\uc790\uc5d0\uac8c\ub294 \ubaa8\ub378 \uc124\uce58 \ubc0f \uc124\uc815 \uacfc\uc815\uc774 \ub2e4\uc18c \ubcf5\uc7a1\ud558\uac8c \ub290\uaef4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. llama.cpp, Ollama\uc640 \uac19\uc740 \ub3c4\uad6c\ub97c \uc0ac\uc6a9\ud558\uba74 \uc774 \uacfc\uc815\uc744 \ud06c\uac8c \ub2e8\uc21c\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n \ubcf4\uc548:<\/strong> \ub85c\uceec AI\ub294 \ub370\uc774\ud130\ub97c \uc678\ubd80\uc5d0 \uc804\uc1a1\ud558\uc9c0 \uc54a\uc9c0\ub9cc, \uc545\uc131 \uc18c\ud504\ud2b8\uc6e8\uc5b4\uac00 \ud3ec\ud568\ub41c \ubaa8\ub378 \ud30c\uc77c\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uac70\ub098, \uc798\ubabb\ub41c \ubcf4\uc548 \uc124\uc815\uc73c\ub85c \uc778\ud574 \uc2dc\uc2a4\ud15c\uc774 \ucde8\uc57d\ud574\uc9c8 \uc704\ud5d8\uc740 \uc5ec\uc804\ud788 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \ucd9c\ucc98\uc5d0\uc11c \ubaa8\ub378\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0, \uc2dc\uc2a4\ud15c \ubcf4\uc548\uc744 \ucca0\uc800\ud788 \uad00\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n \ub77c\uc774\uc120\uc2a4:<\/strong> \uc624\ud508 \ubaa8\ub378\uc774\ub77c\uace0 \ud574\uc11c \ubaa8\ub450 \uc0c1\uc5c5\uc801\uc73c\ub85c \uc790\uc720\ub86d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uac83\uc740 \uc544\ub2d9\ub2c8\ub2e4. \uac01 \ubaa8\ub378\uc758 \ub77c\uc774\uc120\uc2a4\ub97c \ubc18\ub4dc\uc2dc \ud655\uc778\ud558\uace0 \uc900\uc218\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n \ub85c\uceec \uad6c\ub3d9 \uac00\ub2a5\ud55c \uc624\ud508 \ubaa8\ub378 AI\uc758 \ubc1c\uc804\uc740 \uc55e\uc73c\ub85c\ub3c4 \uacc4\uc18d\ub420 \uac83\uc785\ub2c8\ub2e4.<\/p>\n \ubaa8\ub378 \uacbd\ub7c9\ud654 \ubc0f \ud6a8\uc728\uc131 \uc99d\ub300:<\/strong> \ub354 \uc801\uc740 \uc790\uc6d0\uc73c\ub85c\ub3c4 \ub192\uc740 \uc131\ub2a5\uc744 \ub0bc \uc218 \uc788\ub294 \ubaa8\ub378 \uac1c\ubc1c\uc774 \uac00\uc18d\ud654\ub420 \uac83\uc785\ub2c8\ub2e4. \uc774\ub294 \uc800\uc0ac\uc591 \uae30\uae30\uc5d0\uc11c\ub3c4 AI\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uac00\ub2a5\uc131\uc744 \uc5f4\uc5b4\uc90d\ub2c8\ub2e4.<\/p>\n<\/li>\n \uc0ac\uc6a9\uc790 \uce5c\ud654\uc801 \ub3c4\uad6c\uc758 \ubc1c\uc804:<\/strong> \ubcf5\uc7a1\ud55c \uae30\uc220\uc801 \uc9c0\uc2dd \uc5c6\uc774\ub3c4 \ub204\uad6c\ub098 \uc27d\uac8c \ub85c\uceec AI\ub97c \uc124\uce58\ud558\uace0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\ub294 \ub3c4\uad6c\ub4e4\uc774 \ub354\uc6b1 \ubc1c\uc804\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n \ub2e4\uc591\ud55c \ud558\ub4dc\uc6e8\uc5b4 \uc9c0\uc6d0:<\/strong> \uc2a4\ub9c8\ud2b8\ud3f0, \ud0dc\ube14\ub9bf \ub4f1 \ub2e4\uc591\ud55c \ubaa8\ubc14\uc77c \uae30\uae30\uc5d0\uc11c\ub3c4 AI \ubaa8\ub378\uc744 \uc9c1\uc811 \uad6c\ub3d9\ud558\ub824\ub294 \uc2dc\ub3c4\uac00 \ub298\uc5b4\ub0a0 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n AI \uae30\uc220\uc758 \uc735\ud569:<\/strong> \ub85c\uceec AI\ub294 \ub2e4\ub978 \uae30\uc220(\uc99d\uac15 \ud604\uc2e4, \uac00\uc0c1 \ud604\uc2e4, IoT \ub4f1)\uacfc \uc735\ud569\ud558\uc5ec \ub354\uc6b1 \ud601\uc2e0\uc801\uc778 \uc0ac\uc6a9\uc790 \uacbd\ud5d8\uc744 \uc81c\uacf5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n \uc624\ud508 \ubaa8\ub378 AI\uc758 \ubc18\uaca9\uc740 AI \uae30\uc220\uc758 \ubbf8\ub798\ub97c \ud765\ubbf8\ub86d\uac8c \ub9cc\ub4e4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub85c\uceec\uc5d0\uc11c \ucd5c\uc2e0 AI \ubaa8\ub378\uc744 \uc9c1\uc811 \uad6c\ub3d9\ud560 \uc218 \uc788\uac8c \ub418\uba74\uc11c, \uc6b0\ub9ac\ub294 \uac1c\uc778 \uc815\ubcf4 \ubcf4\ud638, \ube44\uc6a9 \ud6a8\uc728\uc131, \ub9de\ucda4 \uc124\uc815 \ub4f1 \uc774\uc804\uc5d0\ub294 \uc0c1\uc0c1\ud558\uae30 \uc5b4\ub824\uc6e0\ub358 \uc774\uc810\ub4e4\uc744 \ub204\ub9b4 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ubb3c\ub860 \ud558\ub4dc\uc6e8\uc5b4 \uc694\uad6c\uc0ac\ud56d\uc774\ub098 \ucd08\uae30 \uc124\uc815\uc758 \ubcf5\uc7a1\uc131\uacfc \uac19\uc740 \ub3c4\uc804 \uacfc\uc81c\ub3c4 \uc874\uc7ac\ud558\uc9c0\ub9cc, \uae30\uc220\uc758 \ubc1c\uc804\uacfc \uc0ac\uc6a9\uc790 \uce5c\ud654\uc801\uc778 \ub3c4\uad6c\uc758 \ub4f1\uc7a5\uc740 \uc774\ub7ec\ud55c \uc7a5\ubcbd\uc744 \uc810\ucc28 \ub0ae\ucd94\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n AI \uae30\uc220\uc758 \ubbfc\uc8fc\ud654\ub294 \uc774\uc81c \ub9c9 \uc2dc\uc791\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc624\ud508 \ubaa8\ub378 AI\ub97c \ud1b5\ud574 \ub204\uad6c\ub098 \uac15\ub825\ud55c AI\ub97c \uc790\uc2e0\uc758 \uc190\uc548\uc5d0\uc11c \uacbd\ud5d8\ud558\uace0 \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uc2dc\ub300\uac00 \uc5f4\ub9ac\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uc9c0\uae08 \ubc14\ub85c \uc2dc\uc791\ud574 \ubcf4\uc138\uc694:<\/strong><\/p>\n Ollama\ub098 LM Studio\uc640 \uac19\uc740 \ub3c4\uad6c\ub97c \uc124\uce58\ud558\uc5ec \ub85c\uceec AI \ubaa8\ub378\uc744 \ud0d0\uc0c9\ud574 \ubcf4\uc138\uc694.<\/strong><\/p>\n<\/li>\n \uc790\uc2e0\uc758 \ud558\ub4dc\uc6e8\uc5b4 \uc0ac\uc591\uc5d0 \ub9de\ub294 \ubaa8\ub378(\uc608: Llama 3 8B, Mistral 7B)\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uc5ec \ud14c\uc2a4\ud2b8\ud574 \ubcf4\uc138\uc694.<\/strong><\/p>\n<\/li>\n \uac04\ub2e8\ud55c \uc9c8\ubb38\uc774\ub098 \uc694\uccad\uc744 \ud1b5\ud574 \ub85c\uceec AI\uc758 \uc131\ub2a5\uc744 \uc9c1\uc811 \uacbd\ud5d8\ud574 \ubcf4\uc138\uc694.<\/strong><\/p>\n<\/li>\n<\/ol>\n AI\ub294 \ub354 \uc774\uc0c1 \uba3c \ubbf8\ub798\uc758 \uae30\uc220\uc774 \uc544\ub2d9\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \ucef4\ud4e8\ud130\uc5d0\uc11c, \ubc14\ub85c \uc9c0\uae08, AI\uc758 \ub180\ub77c\uc6b4 \uac00\ub2a5\uc131\uc744 \uc9c1\uc811 \ub9cc\ub098\ubcf4\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n INTERNAL_LINKS:<\/strong> (\uc720\uc0ac\ud55c \uac8c\uc2dc\uae00 \uc785\ub825)<\/p>\n EXTERNAL_LINKS:<\/strong> Ollama \uacf5\uc2dd \uc6f9\uc0ac\uc774\ud2b8<\/a>, LM Studio \uacf5\uc2dd \uc6f9\uc0ac\uc774\ud2b8<\/a>, Hugging Face<\/a><\/p>\n<\/div>\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n Over the past several years, artificial intelligence (AI) technology has advanced at a remarkable pace. In particular, the emergence of large language models (LLMs) has dramatically expanded the range of what AI can do. Yet amid this progress, it is worth paying attention to the counterattack of open models<\/strong>. In the past, only a handful of major technology companies had the massive capital and computing power needed to develop and own cutting-edge AI models. Now, however, thanks to the active open-model community, ordinary users can directly run the latest AI models on their own computers\u2014in other words, in a local environment<\/strong>.<\/p>\n\n\n\n This shift means more than technical progress alone. It has important implications for AI accessibility, data privacy, cost efficiency, and customization. Just as the personal computer brought the mainframe era to an end and democratized information technology, locally runnable open-model AI has the potential to accelerate the democratization of AI technology.<\/p>\n\n\n\n In the past, it was common to rely on cloud-based services to use AI models. Services such as OpenAI\u2019s ChatGPT and Google\u2019s Bard (now Gemini) offer strong performance, but because they require data to be transmitted to external servers, they have often raised concerns about privacy. There are also financial burdens such as API fees and subscription costs.<\/p>\n\n\n\n As open-model AI becomes runnable in local environments, many of these issues can now be addressed to a considerable extent. Running AI locally offers several key advantages.<\/p>\n\n\n\n One of the biggest advantages is privacy. Local AI processes all computation directly on the user\u2019s computer. That means sensitive information or private questions do not need to be sent to an external server. This is especially important when using AI with data that users do not want exposed outside, such as internal corporate data, personal journals, or creative work. In an era when data privacy matters more than ever, local AI gives users far greater control.<\/p>\n\n\n\n Cloud-based AI services incur costs based on usage. This can become especially expensive when large language models are used frequently or integrated into services through APIs. By contrast, local AI can be used freely after the initial hardware investment, such as purchasing a graphics card, without ongoing usage charges. High-performance hardware may still be necessary, but over the long term, local AI can reduce repeated subscription and usage costs.<\/p>\n\n\n\n Local AI works without an internet connection. This means AI can be used even in environments where internet access is unstable or unavailable, or where security concerns make outside network access difficult. Even offline, users can still draft documents, get coding assistance, or brainstorm ideas with AI.<\/p>\n\n\n\n Open models often provide public source code or model weights, which allows users to modify or fine-tune them for their own purposes. This is especially easy in local environments. Users can train models on domain-specific data or optimize performance by adjusting parameters to create their own AI systems. This is particularly attractive for researchers, developers, and professionals in specialized fields.<\/p>\n\n\n\n The spread of open models ensures that insight into AI development is no longer limited to a small number of companies, but is instead made available to many more people. This helps accelerate AI innovation and fosters an ecosystem in which diverse ideas can emerge. Individual developers and small teams can now use state-of-the-art AI technology to build new services and products.<\/p>\n\n\n\n Running local AI requires some preparation. Not all AI models demand the same specifications, but in general the following elements are important.<\/p>\n\n\n\n Graphics Card (GPU):<\/strong> RAM (System Memory):<\/strong> CPU:<\/strong> Storage Space (SSD):<\/strong> Operating System:<\/strong> AI Frameworks:<\/strong> Model Execution Tools:<\/strong> There is a wide variety of open models that can run locally. Each differs in size, performance, training data, and license terms.<\/p>\n\n\n\n Llama 3:<\/strong> Mistral AI models:<\/strong> Gemma:<\/strong> Phi-3:<\/strong> When choosing a model, users should consider both their hardware specifications and the performance they need. In general, models with more parameters deliver better performance but also require more VRAM and computing power.<\/p>\n\n\n\n Local AI is already creating tangible value across many fields.<\/p>\n\n\n\n Document writing and summarization:<\/strong> Coding assistance:<\/strong> Learning tools:<\/strong> Storytelling and writing:<\/strong> Art and design:<\/strong> Music composition:<\/strong> Data analysis:<\/strong> Prototyping:<\/strong> AI model research:<\/strong> Customized information curation:<\/strong> Support for hobbies:<\/strong> Although running local AI has many advantages, there are also several things to be careful about.<\/p>\n\n\n\n Overly high expectations:<\/strong> Hardware requirements:<\/strong> Complex setup:<\/strong> Security:<\/strong> Licensing:<\/strong> The development of locally runnable open-model AI is likely to continue.<\/p>\n\n\n\n Model lightweighting and increased efficiency:<\/strong> Better user-friendly tools:<\/strong> Support for more hardware types:<\/strong> Convergence with other technologies:<\/strong> The counterattack of open-model AI is making the future of AI technology even more exciting. As it becomes possible to run the latest AI models locally, users can now benefit from privacy protection, cost efficiency, and customization in ways that were previously hard to imagine. Of course, there are still challenges such as hardware requirements and the complexity of initial setup, but advances in technology and the rise of user-friendly tools are steadily lowering those barriers.<\/p>\n\n\n\n The democratization of AI technology has only just begun. Through open-model AI, an era is opening in which anyone can directly experience and use powerful AI right at their fingertips.<\/p>\n\n\n\n AI is no longer a technology of the distant future. On your own computer, right now, the remarkable possibilities of AI are already within reach.<\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" \uc624\ud508 \ubaa8\ub378 AI\uc758 \ubc1c\uc804\uc73c\ub85c \ub85c\uceec \ud658\uacbd\uc5d0\uc11c\ub3c4 \uac15\ub825\ud55c \ucd5c\uc2e0 AI\ub97c \uc9c1\uc811 \uad6c\ub3d9\ud560 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uac1c\uc778 \uc815\ubcf4 \ubcf4\ud638, \ube44\uc6a9 \uc808\uac10, \ub9de\ucda4 \uc124\uc815 \ub4f1 \ub2e4\uc591\ud55c \uc774\uc810\uc744 \uc81c\uacf5\ud558\uba70 AI \uae30\uc220\uc758 \ubbfc\uc8fc\ud654\ub97c \uc774\ub04c\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8\ubb38\uc5d0\uc11c\ub294 \ub85c\uceec AI \uad6c\ub3d9\uc758 \uc911\uc694\uc131\uacfc \uc7a5\uc810, \uadf8\ub9ac\uace0 \uc55e\uc73c\ub85c\uc758 \uc804\ub9dd\uc744 \uc0c1\uc138\ud788 \ub2e4\ub8f9\ub2c8\ub2e4.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[4],"tags":[69,41,78,86,113,34,79,114,126,71,112,47,32,110,22,111],"class_list":["post-57","post","type-post","status-publish","format-standard","hentry","category-ai","tag-ai-technology","tag-ai-","tag-artificial-intelligence","tag-cost-efficiency","tag-llama-3","tag-llm","tag-local-ai","tag-mistral-ai","tag-open-model-ai","tag-privacy-protection","tag-112","tag--ai","tag-32","tag---ai","tag-22","tag--ai-"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/57","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=57"}],"version-history":[{"count":2,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/57\/revisions"}],"predecessor-version":[{"id":83,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/57\/revisions\/83"}],"wp:attachment":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=57"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=57"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=57"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}2. \uc18c\ud504\ud2b8\uc6e8\uc5b4 \ubc0f \ub3c4\uad6c<\/h3>\n
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3. \ubaa8\ub378 \uc120\ud0dd<\/h3>\n
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\ucd5c\uc2e0 \uc624\ud508 \ubaa8\ub378\uc758 \ubc18\uaca9: \ub85c\uceec AI\uc758 \uc2e4\uc81c \ud65c\uc6a9 \uc0ac\ub840<\/h2>\n
1. \uac1c\uc778 \ube44\uc11c \ubc0f \uc0dd\uc0b0\uc131 \ud5a5\uc0c1<\/h3>\n
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2. \ucc3d\uc791 \ud65c\ub3d9 \uc9c0\uc6d0<\/h3>\n
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3. \uc5f0\uad6c \ubc0f \uac1c\ubc1c<\/h3>\n
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4. \uac1c\uc778\ud654\ub41c \uacbd\ud5d8<\/h3>\n
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\ud754\ud55c \uc2e4\uc218\uc640 \uc8fc\uc758\uc0ac\ud56d<\/h2>\n
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\n\n\n\nOpen-Model AI: Why the Latest Locally Runnable Models Are Drawing Attention<\/h1>\n\n\n\n
The Rise of Open-Model AI: Why the Latest Local AI Is Gaining Attention<\/h2>\n\n\n\n
Why Is \u201cLocal\u201d AI Important?<\/h2>\n\n\n\n
1. Stronger Privacy Protection<\/h3>\n\n\n\n
2. Cost Efficiency<\/h3>\n\n\n\n
3. No Internet Connection Required<\/h3>\n\n\n\n
4. Freedom to Customize and Experiment<\/h3>\n\n\n\n
5. Democratization of Technological Progress<\/h3>\n\n\n\n
Preparing to Run Local AI: What Is Needed?<\/h2>\n\n\n\n
1. Hardware Requirements<\/h3>\n\n\n\n
AI models, especially large language models, must perform massive amounts of matrix computation. A powerful GPU is essential for handling this efficiently. The larger the GPU\u2019s VRAM, the larger and more capable the model that can be loaded and run. High-performance graphics cards such as NVIDIA\u2019s RTX series (3000 and 4000 series) or AMD\u2019s Radeon RX series are generally recommended.<\/p>\n\n\n\n
Although not as critical as GPU VRAM, sufficient RAM is still needed to load models and process data. At least 16 GB is recommended, with 32 GB or more being preferable.<\/p>\n\n\n\n
The CPU is not as crucial as the GPU, but it still affects overall system performance and data-loading speed. A modern multi-core CPU is advantageous.<\/p>\n\n\n\n
AI model files can range from several gigabytes to tens of gigabytes. Using an SSD is recommended so models can be stored and loaded quickly.<\/p>\n\n\n\n2. Software and Tools<\/h3>\n\n\n\n
Windows, macOS, and Linux are all supported. Compatibility should be checked depending on the model and framework being used.<\/p>\n\n\n\n
Deep learning frameworks such as PyTorch or TensorFlow may be needed.<\/p>\n\n\n\n
Tools such as llama.cpp<\/strong>, Ollama<\/strong>, and LM Studio<\/strong> make it easier to download and run AI models locally. These tools simplify what would otherwise be complicated setup processes and create a more user-friendly experience.<\/p>\n\n\n\n3. Choosing a Model<\/h3>\n\n\n\n
A recent model released by Meta, available in multiple sizes such as 8B and 70B, making it useful in local environments as well.<\/p>\n\n\n\n
Models such as Mistral 7B and Mixtral 8x7B are known for strong performance and efficiency.<\/p>\n\n\n\n
A lightweight model released by Google, suitable for personal and research use.<\/p>\n\n\n\n
A small language model (SLM) released by Microsoft that performs well even in lower-spec environments.<\/p>\n\n\n\nThe Counterattack of the Latest Open Models: Real-World Uses of Local AI<\/h2>\n\n\n\n
1. Personal Assistance and Productivity<\/h3>\n\n\n\n
Local AI can help summarize long reports or papers, draft emails, and develop ideas. It can also be used to organize and analyze private notes or journals.<\/p>\n\n\n\n
Developers can use local AI for autocomplete, bug detection, code explanation, and learning new programming languages. This can significantly improve development productivity.<\/p>\n\n\n\n
AI can be used to explain complex concepts and explore related information when learning new subjects.<\/p>\n\n\n\n2. Support for Creative Work<\/h3>\n\n\n\n
AI can provide inspiration for novels, screenplays, or game stories, help develop plot structures, and generate dialogue.<\/p>\n\n\n\n
Users can run image-generation AI models locally to create unique artwork or design concepts of their own.<\/p>\n\n\n\n
AI can be used to generate melody ideas, explore instrument arrangements, and open new possibilities in music creation.<\/p>\n\n\n\n3. Research and Development<\/h3>\n\n\n\n
AI can analyze datasets used in personal research or projects and help derive insights.<\/p>\n\n\n\n
New AI-based services or application ideas can be quickly prototyped and tested in a local environment.<\/p>\n\n\n\n
Researchers can build new algorithms or improve existing models using open models as a foundation.<\/p>\n\n\n\n4. Personalized Experiences<\/h3>\n\n\n\n
Users can create a personalized information environment by automatically summarizing news on topics of interest or generating recommended content.<\/p>\n\n\n\n
For example, AI can answer questions about game strategies or help organize a collection catalog, making personal hobbies even richer.<\/p>\n\n\n\nCommon Mistakes and Points of Caution<\/h2>\n\n\n\n
Locally run models may not match the performance of cutting-edge cloud-based models. On lower-end hardware, it can be difficult to run the latest large models at all.<\/p>\n\n\n\n
As noted earlier, smooth use of high-performance AI models requires substantial computing resources. It is important to choose hardware that matches both budget and purpose.<\/p>\n\n\n\n
For some users, model installation and configuration may feel somewhat complicated. Tools such as llama.cpp and Ollama can simplify this process significantly.<\/p>\n\n\n\n
Local AI does not transmit data externally, but risks still remain if users download model files containing malicious software or weaken system security through incorrect settings. Models should only be downloaded from trusted sources, and system security should be carefully maintained.<\/p>\n\n\n\n
Not every open model can be used freely for commercial purposes. The license terms of each model must be checked and followed.<\/p>\n\n\n\nThe Future of Open-Model AI<\/h2>\n\n\n\n
Development will accelerate toward models that deliver strong performance while requiring fewer resources. This opens the possibility of using AI even on lower-spec devices.<\/p>\n\n\n\n
Tools that help people install and use local AI easily, even without advanced technical knowledge, will continue to improve.<\/p>\n\n\n\n
There will likely be more efforts to run AI models directly on mobile devices such as smartphones and tablets.<\/p>\n\n\n\n
Local AI can combine with technologies such as augmented reality, virtual reality, and IoT to deliver even more innovative user experiences.<\/p>\n\n\n\nConclusion<\/h2>\n\n\n\n
Get Started Right Now<\/h2>\n\n\n\n
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