{"id":88575,"date":"2026-08-11T09:43:26","date_gmt":"2026-08-11T06:43:26","guid":{"rendered":"https:\/\/twodots.gr\/?p=88575"},"modified":"2026-08-11T09:43:26","modified_gmt":"2026-08-11T06:43:26","slug":"muse-glimmer-topiki-multimodal-ai-agents","status":"publish","type":"post","link":"https:\/\/twodots.gr\/bg\/muse-glimmer-topiki-multimodal-ai-agents\/","title":{"rendered":"Muse Glimmer: \u03b7 \u03b1\u03bd\u03bf\u03b9\u03c7\u03c4\u03ae multimodal AI \u03c4\u03b7\u03c2 Meta \u03c0\u03bf\u03c5 \u03c6\u03ad\u03c1\u03bd\u03b5\u03b9 \u03c4\u03bf\u03c5\u03c2 agents \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u03a4\u03bf Muse Glimmer \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03c0\u03c1\u03cc\u03c4\u03b1\u03c3\u03b7 \u03c4\u03b7\u03c2 Meta \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1\u03bd open-weight multimodal agent \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 30B \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03ae\u03c3\u03b5\u03b9 \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae.<\/strong><\/p>\n<p>\u03a3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf, \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, tool use, \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf context \u03ba\u03b1\u03b9 failure recovery, \u03b5\u03bd\u03ce \u03bf\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b5\u03c2 quantized \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03c3\u03c4\u03bf\u03c7\u03b5\u03cd\u03bf\u03c5\u03bd \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03bc\u03b5 24 GB \u03ae 32 GB VRAM. \u0397 \u03ba\u03c5\u03ba\u03bb\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03b1\u03bd\u03bf\u03af\u03b3\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03bf local AI \u03c3\u03b5 \u03bb\u03cd\u03c3\u03b7 \u03c7\u03c9\u03c1\u03af\u03c2 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2, \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03ae \u03ba\u03b9\u03bd\u03b4\u03cd\u03bd\u03bf\u03c5\u03c2.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">\u03a0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1<\/div>\n<ul>\n<li><a href=\"#ti-allazei-muse-glimmer\">\u03a4\u03b9 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf Muse Glimmer \u03b3\u03b9\u03b1 \u03c4\u03bf\u03c5\u03c2 local AI agents<\/a><\/li>\n<li><a href=\"#architektoniki-perception-encoder\">\u0391\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 Perception Encoder \u03c7\u03c9\u03c1\u03af\u03c2 \u03c5\u03c0\u03b5\u03c1\u03b2\u03bf\u03bb\u03ad\u03c2<\/a><\/li>\n<li><a href=\"#local-deployment-pragmatikes-apaitiseis\">Local deployment: \u03bf\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2<\/a><\/li>\n<li><a href=\"#tool-use-multimodal-agents\">\u0391\u03c0\u03cc \u03c4\u03b7\u03bd \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03c3\u03c4\u03bf tool use \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1<\/a><\/li>\n<li><a href=\"#quantization-dflash-taxytita\">Quantization \u03ba\u03b1\u03b9 DFlash: \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b5 \u03c3\u03b1\u03c6\u03ad\u03c2 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/a><\/li>\n<li><a href=\"#benchmarks-ti-apodeiknyoun\">\u03a4\u03b9 \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03bf\u03c5\u03bd \u03c4\u03b1 benchmarks<\/a><\/li>\n<li><a href=\"#epiloges-serving-ensomatosis\">\u0395\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 serving \u03ba\u03b1\u03b9 \u03b5\u03bd\u03c3\u03c9\u03bc\u03ac\u03c4\u03c9\u03c3\u03b7\u03c2<\/a><\/li>\n<li><a href=\"#idiotikotita-asfaleia-dikaiomata\">\u0399\u03b4\u03b9\u03c9\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1, \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03ba\u03b1\u03b9\u03ce\u03bc\u03b1\u03c4\u03b1 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03c9\u03bd<\/a><\/li>\n<li><a href=\"#pilot-aksiologisis-epicheirisis\">\u03a0\u03ce\u03c2 \u03c3\u03c4\u03ae\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc pilot<\/a><\/li>\n<li><a href=\"#symperasma\">\u0417\u0430\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435<\/a><\/li>\n<\/ul>\n<\/div>\n<p>\u03a4\u03bf Muse Glimmer \u03b5\u03af\u03bd\u03b1\u03b9 open-weight multimodal \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf 30B \u03c4\u03b7\u03c2 Meta \u03b3\u03b9\u03b1 local AI agents. \u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03b1\u03bd \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03b5\u03b9 \u03c0\u03b9\u03bf \u03ba\u03bf\u03bd\u03c4\u03ac \u03c4\u03b7\u03c2 \u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03bf inference, \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03c3\u03b5\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03bc\u03b5 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03b1 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9 \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c4\u03b7\u03c2 workflow.<\/p>\n<p>\u0397 Meta \u03ba\u03c5\u03ba\u03bb\u03bf\u03c6\u03cc\u03c1\u03b7\u03c3\u03b5 \u03c4\u03b1 weights \u03bc\u03b5 \u03ac\u03b4\u03b5\u03b9\u03b1 Apache 2.0 \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c9\u03c2 dense causal transformer \u03bc\u03b5 dedicated perception encoder. \u0395\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c3\u03c4\u03b1\u03b3\u03bc\u03ad\u03bd\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf Muse Spark \u03ba\u03b1\u03b9 \u03ad\u03c7\u03b5\u03b9 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03b5\u03af \u03b3\u03b9\u03b1 multi-step reasoning, function calling, failure recovery \u03ba\u03b1\u03b9 multimodal \u03ba\u03b1\u03c4\u03b1\u03bd\u03cc\u03b7\u03c3\u03b7. \u0397 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03ba\u03ac\u03c1\u03c4\u03b1 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b1\u03c0\u03cc 100 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b5\u03c2, context 131.072+ tokens \u03ba\u03b1\u03b9 knowledge cutoff \u03c3\u03c4\u03b9\u03c2 4 \u0399\u03b1\u03bd\u03bf\u03c5\u03b1\u03c1\u03af\u03bf\u03c5 2026.<\/p>\n<p>\u0391\u03c5\u03c4\u03ae \u03b7 \u03c3\u03cd\u03bd\u03b8\u03b5\u03c3\u03b7 \u03c4\u03bf \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c4\u03b5\u03af \u03b4\u03af\u03c0\u03bb\u03b1 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03b5\u03c2 <a href=\"https:\/\/twodots.gr\/inkling-open-weight-ai-epicheiriseis\/\">open-weight \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 AI \u03c0\u03bf\u03c5 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd \u03bf\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2<\/a>. \u0397 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b7 \u03ac\u03b4\u03b5\u03b9\u03b1. \u0395\u03af\u03bd\u03b1\u03b9 \u03b7 \u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b5\u03b9\u03b1 \u03bd\u03b1 \u03c7\u03c9\u03c1\u03ad\u03c3\u03bf\u03c5\u03bd agentic \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2, \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03ba\u03b1\u03b9 speculative decoding \u03c3\u03b5 hardware \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03bf, workstation \u03ae \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd.<\/p>\n<h2 id=\"ti-allazei-muse-glimmer\">\u03a4\u03b9 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf Muse Glimmer \u03b3\u03b9\u03b1 \u03c4\u03bf\u03c5\u03c2 local AI agents<\/h2>\n<p>\u0388\u03bd\u03b1\u03c2 local agent \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03ad\u03bd\u03b1 chatbot \u03c7\u03c9\u03c1\u03af\u03c2 \u03c3\u03cd\u03bd\u03b4\u03b5\u03c3\u03b7 \u03c3\u03c4\u03bf cloud. \u03a0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c3\u03c7\u03ad\u03b4\u03b9\u03bf \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd \u03b2\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd, \u03bd\u03b1 \u03ba\u03b1\u03bb\u03b5\u03af \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03b1 \u03bc\u03b5 \u03c3\u03c9\u03c3\u03c4\u03cc schema, \u03bd\u03b1 \u03ba\u03b1\u03c4\u03b1\u03bb\u03b1\u03b2\u03b1\u03af\u03bd\u03b5\u03b9 \u03c0\u03cc\u03c4\u03b5 \u03ad\u03bd\u03b1 tool \u03b1\u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b5\u03c7\u03af\u03b6\u03b5\u03b9 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03c7\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf\u03bd \u03c3\u03c4\u03cc\u03c7\u03bf. \u03a4\u03bf Muse Glimmer \u03ad\u03c7\u03b5\u03b9 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03b5\u03af \u03ba\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b7\u03b8\u03b5\u03af \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03c3\u03b5 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b9\u03ba\u03b1\u03bd\u03bf\u03c4\u03ae\u03c4\u03c9\u03bd.<\/p>\n<p>\u0397 Meta \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03cc\u03c0\u03c9\u03c2 local coding, function calling, LLM-as-a-judge \u03ba\u03b1\u03b9 agentic task completion. \u039f perception encoder \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 interleaved text \u03ba\u03b1\u03b9 image inputs, \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf workflow \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03bf\u03b4\u03b7\u03b3\u03af\u03b1, \u03ad\u03bd\u03b1 screenshot, \u03ad\u03bd\u03b1 \u03ad\u03b3\u03b3\u03c1\u03b1\u03c6\u03bf \u03ae \u03bc\u03b9\u03b1 \u03c6\u03c9\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03af\u03b1 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2. \u0397 \u03ad\u03be\u03bf\u03b4\u03bf\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf, \u03c4\u03bf \u03bf\u03c0\u03bf\u03af\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03c3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03b1\u03c0\u03b5\u03af \u03c3\u03b5 \u03b4\u03bf\u03bc\u03b7\u03bc\u03ad\u03bd\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03ae \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03ba\u03bb\u03ae\u03c3\u03b7 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03bf\u03c5.<\/p>\n<p>\u03a4\u03bf \u00ab\u03c4\u03bf\u03c0\u03b9\u03ba\u03ac\u00bb \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c5\u03c0\u03bf\u03c7\u03c1\u03b5\u03c9\u03c4\u03b9\u03ba\u03ac \u03cc\u03c4\u03b9 \u03cc\u03bb\u03b1 \u03c4\u03c1\u03ad\u03c7\u03bf\u03c5\u03bd \u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd \u03c6\u03bf\u03c1\u03b7\u03c4\u03cc \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03ae. \u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 workstation \u03bc\u03b5 24 GB \u03ae 32 GB VRAM, \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc server \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03ae \u03b9\u03b4\u03b9\u03c9\u03c4\u03b9\u03ba\u03ae \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae \u03c0\u03bf\u03c5 \u03b5\u03bb\u03ad\u03b3\u03c7\u03b5\u03b9 \u03bf \u03bf\u03c1\u03b3\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc\u03c2. \u03a4\u03bf \u03c3\u03c9\u03c3\u03c4\u03cc \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03bf \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc latency, concurrent users, \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd, \u03bc\u03ae\u03ba\u03bf\u03c2 context, \u03b4\u03b9\u03b1\u03b8\u03b5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1\u03c2.<\/p>\n<div class=\"td-article-note\">\n<p><strong>\u0397 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7:<\/strong> open weights \u03ba\u03b1\u03b9 local inference \u03b1\u03c5\u03be\u03ac\u03bd\u03bf\u03c5\u03bd \u03c4\u03bf\u03bd \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ce\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03bc\u03cc\u03bd\u03b1 \u03c4\u03bf\u03c5\u03c2 \u03b9\u03b4\u03b9\u03c9\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1, \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ae \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03b5\u03c2 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b5\u03c2. \u0391\u03c5\u03c4\u03ac \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd \u03b1\u03c0\u03cc \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03b7 \u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<\/div>\n<h2 id=\"architektoniki-perception-encoder\">\u0391\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 Perception Encoder \u03c7\u03c9\u03c1\u03af\u03c2 \u03c5\u03c0\u03b5\u03c1\u03b2\u03bf\u03bb\u03ad\u03c2<\/h2>\n<p>\u0397 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03ba\u03ac\u03c1\u03c4\u03b1 \u03b4\u03af\u03bd\u03b5\u03b9 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 29,6B \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd, \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03bf\u03bc\u03ad\u03bd\u03bf\u03c5 \u03b5\u03bd\u03cc\u03c2 frozen ViT-G\/14 perception encoder \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 1,8B \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd. \u039f language model \u03ad\u03c7\u03b5\u03b9 52 layers \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03c4\u03c1\u03b9\u03ce\u03bd local-attention layers \u03ba\u03b1\u03b9 \u03b5\u03bd\u03cc\u03c2 global-attention layer. \u03a4\u03bf local sliding window \u03b5\u03af\u03bd\u03b1\u03b9 2.048 tokens.<\/p>\n<p>\u0397 attention \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af 32 query heads \u03ba\u03b1\u03b9 \u03b4\u03cd\u03bf key-value heads, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae GQA ratio 16:1. \u0391\u03c5\u03c4\u03cc \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03cd\u03c0\u03c9\u03bc\u03b1 \u03c4\u03bf\u03c5 KV cache \u03c3\u03b5 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03bc\u03b5 \u03bc\u03b9\u03b1 \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7 \u03cc\u03c0\u03bf\u03c5 \u03ba\u03ac\u03b8\u03b5 query head \u03ad\u03c7\u03b5\u03b9 \u03b4\u03b9\u03ba\u03cc \u03c4\u03bf\u03c5 key-value pair. \u0397 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf design \u03ad\u03c7\u03b5\u03b9 \u03bb\u03ac\u03b2\u03b5\u03b9 \u03c5\u03c0\u03cc\u03c8\u03b7 \u03c4\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03c4\u03ac \u03c4\u03bf inference, \u03ad\u03bd\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b1 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ac \u03b5\u03bc\u03c0\u03cc\u03b4\u03b9\u03b1 \u03b3\u03b9\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf context \u03c3\u03b5 local hardware.<\/p>\n<p>\u039f perception encoder \u03b4\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 \u03ad\u03c9\u03c2 4.096 visual tokens \u03b1\u03bd\u03ac \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1. \u03a4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03c4\u03b5\u03af video \u03c9\u03c2 \u03b5\u03c0\u03b9\u03bc\u03ad\u03c1\u03bf\u03c5\u03c2 frames, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03af\u03b4\u03b9\u03b1 \u03b7 Meta \u03c3\u03b7\u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ac \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03bf \u03b3\u03b9\u03b1 video. Audio input \u03ba\u03b1\u03b9 output \u03b4\u03b5\u03bd \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, \u03ad\u03bd\u03b1 workflow \u03b3\u03b9\u03b1 \u03b4\u03b9\u03b1\u03c6\u03b7\u03bc\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03b2\u03af\u03bd\u03c4\u03b5\u03bf \u03ae \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae \u03bf\u03b8\u03cc\u03bd\u03b7\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ae \u03bc\u03b5\u03c4\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae \u03ae\u03c7\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae\u03c2 frames.<\/p>\n<p>\u0393\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7, \u03b1\u03c5\u03c4\u03ad\u03c2 \u03bf\u03b9 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b5\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03ba\u03cc\u03c3\u03bc\u03b7\u03c3\u03b7. \u039a\u03b1\u03b8\u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c0\u03cc\u03c3\u03b5\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03c7\u03c9\u03c1\u03bf\u03cd\u03bd \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 \u03c4\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf, \u03c0\u03cc\u03c3\u03bf \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1 \u03bc\u03b5\u03b3\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 \u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03b1\u03bd \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c4\u03b7\u03c1\u03af\u03be\u03b5\u03b9 document AI, visual quality control \u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03bf\u03b8\u03bf\u03bd\u03ce\u03bd \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b8\u03c5\u03c3\u03b9\u03ac\u03c3\u03b5\u03b9 \u03c4\u03bf \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03bf context.<\/p>\n<h2 id=\"local-deployment-pragmatikes-apaitiseis\">Local deployment: \u03bf\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2<\/h2>\n<p>\u03a3\u03b5 \u03c0\u03bb\u03ae\u03c1\u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1, \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf 30B \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc 55 GB \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2 \u03bc\u03cc\u03bd\u03bf \u03b3\u03b9\u03b1 \u03c4\u03b1 weights. \u0397 Meta \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 4-bit quantization \u03ce\u03c3\u03c4\u03b5 \u03bf language model \u03bd\u03b1 \u03c0\u03ad\u03c3\u03b5\u03b9 \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc 20 GB \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03af\u03bd\u03b5\u03b9 \u03c7\u03ce\u03c1\u03bf\u03c2 \u03b3\u03b9\u03b1 KV cache, perception encoder \u03ba\u03b1\u03b9 DFlash drafter \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 envelope 24 GB \u03ae 32 GB.<\/p>\n<p>\u0397 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c3\u03c4\u03cc\u03c7\u03bf 32 GB VRAM \u03b3\u03b9\u03b1 \u03c4\u03bf K-Quant-Dynamic \u03bc\u03b5 \u03bc\u03ad\u03c3\u03b7 \u03c5\u03c0\u03bf\u03b2\u03ac\u03b8\u03bc\u03b9\u03c3\u03b7 0,2% \u03ba\u03b1\u03b9 \u03c3\u03c4\u03cc\u03c7\u03bf 24 GB VRAM \u03b3\u03b9\u03b1 \u03c4\u03bf K-Quant-17GB \u03bc\u03b5 \u03bc\u03ad\u03c3\u03b7 \u03c5\u03c0\u03bf\u03b2\u03ac\u03b8\u03bc\u03b9\u03c3\u03b7 1,0%. \u0397 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03b5\u03b9 \u03b1\u03c0\u03cc \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf accuracy metrics \u03c3\u03b5 15 benchmarks. \u0394\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf task \u03b8\u03b1 \u03c7\u03ac\u03c3\u03b5\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc.<\/p>\n<p>\u03a4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc memory budget \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b5 \u03c4\u03bf context, \u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c4\u03c9\u03bd \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd, \u03c4\u03bf batch size, \u03c4\u03bf concurrency \u03ba\u03b1\u03b9 \u03c4\u03bf \u03b1\u03bd \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 speculative decoding. \u0388\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03bf\u03c5 \u00ab\u03c7\u03c9\u03c1\u03ac\u00bb \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b7\u03bd \u03ad\u03c7\u03b5\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc throughput. \u0391\u03bd\u03c4\u03af\u03c3\u03c4\u03c1\u03bf\u03c6\u03b1, \u03ad\u03bd\u03b1 quant \u03c0\u03bf\u03c5 \u03c5\u03c3\u03c4\u03b5\u03c1\u03b5\u03af \u03bf\u03c1\u03b9\u03b1\u03ba\u03ac \u03c3\u03b5 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc benchmark \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03b1\u03c1\u03ba\u03ad\u03c2 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03c3\u03c4\u03b5\u03bd\u03cc, \u03ba\u03b1\u03bb\u03ac \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf use case.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<p class=\"td-chart-title\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b5\u03c2 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03ad\u03c2 \u03c0\u03bf\u03c5 \u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf local \u03c3\u03b5\u03bd\u03ac\u03c1\u03b9\u03bf<\/p>\n<p class=\"td-chart-intro\">\u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03bd\u03b1\u03ba\u03bf\u03af\u03bd\u03c9\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03ba\u03ac\u03c1\u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03c4\u03b7\u03c2 Meta. \u0391\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c4\u03b7\u03c2 hardware \u03ba\u03b1\u03b9 \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf, \u03cc\u03c7\u03b9 \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03b3\u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7.<\/p>\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\">\n<div class=\"td-metric-value\">&lt;20 GB<\/div>\n<div class=\"td-metric-label\">Quantized language model<\/div>\n<p>\u03a0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 4-bit weights \u03b1\u03c6\u03ae\u03bd\u03bf\u03c5\u03bd \u03c7\u03ce\u03c1\u03bf \u03b3\u03b9\u03b1 KV cache, \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03ba\u03b1\u03b9 drafter.<\/p>\n<\/div>\n<div class=\"td-metric-card\">\n<div class=\"td-metric-value\">24 GB<\/div>\n<div class=\"td-metric-label\">K-Quant-17GB \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2<\/div>\n<p>\u0397 Meta \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03bc\u03ad\u03c3\u03b7 \u03c5\u03c0\u03bf\u03b2\u03ac\u03b8\u03bc\u03b9\u03c3\u03b7 1,0% \u03c3\u03b5 15 benchmarks.<\/p>\n<\/div>\n<div class=\"td-metric-card\">\n<div class=\"td-metric-value\">131.072+<\/div>\n<div class=\"td-metric-label\">Context tokens<\/div>\n<p>\u03a4\u03bf \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf context \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03b1\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5 KV cache \u03cc\u03c4\u03b1\u03bd \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03ac\u03be\u03b7.<\/p>\n<\/div>\n<div class=\"td-metric-card\">\n<div class=\"td-metric-value\">3,1\u00d7<\/div>\n<div class=\"td-metric-label\">RTX 5090 \u03bc\u03b5 DFlash<\/div>\n<p>Decode speed 233,4 tok\/s \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 74,9 tok\/s \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf test \u03c4\u03b7\u03c2 Meta.<\/p>\n<\/div>\n<\/div>\n<p class=\"td-chart-note\">\u03a3\u03c4\u03b1 M4 Max \u03ba\u03b1\u03b9 M5 Max \u03b7 \u03af\u03b4\u03b9\u03b1 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b5\u03c0\u03b9\u03c4\u03ac\u03c7\u03c5\u03bd\u03c3\u03b7 1,5\u00d7 \u03ba\u03b1\u03b9 1,8\u00d7 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1. \u0397 \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae tokens, \u03c4\u03bf prompt \u03ba\u03b1\u03b9 \u03c4\u03bf workload \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03bf\u03c5\u03bd \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03ad\u03c1\u03b4\u03bf\u03c2.<\/p>\n<\/div>\n<h2 id=\"tool-use-multimodal-agents\">\u0391\u03c0\u03cc \u03c4\u03b7\u03bd \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03c3\u03c4\u03bf tool use \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1<\/h2>\n<p>\u0397 \u03b1\u03be\u03af\u03b1 \u03b5\u03bd\u03cc\u03c2 multimodal agent \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03bc\u03b5\u03c4\u03ac\u03b2\u03b1\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03bd\u03c4\u03af\u03bb\u03b7\u03c8\u03b7 \u03c3\u03c4\u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae, \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b7 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1. \u03a4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03ac\u03c3\u03b5\u03b9 screenshot \u03ae \u03ad\u03b3\u03b3\u03c1\u03b1\u03c6\u03bf, \u03bd\u03b1 \u03b5\u03be\u03b1\u03b3\u03ac\u03b3\u03b5\u03b9 \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03bb\u03ad\u03be\u03b5\u03b9 function \u03bc\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf schema. \u0391\u03c5\u03c4\u03cc \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03b3\u03ad\u03c6\u03c5\u03c1\u03b1 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 unstructured \u03c5\u03bb\u03b9\u03ba\u03cc \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 APIs \u03bc\u03b9\u03b1\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7\u03c2.<\/p>\n<p>\u03a3\u03b5 e-commerce \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd, \u03ad\u03bd\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03c0\u03c1\u03ce\u03c4\u03bf \u03c3\u03b5\u03bd\u03ac\u03c1\u03b9\u03bf \u03b8\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03b5\u03bb\u03ad\u03b3\u03c7\u03b5\u03b9 \u03b1\u03bd \u03bf\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03c9\u03bd \u03c0\u03bb\u03b7\u03c1\u03bf\u03cd\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03bf\u03b4\u03b7\u03b3\u03af\u03b5\u03c2, \u03bd\u03b1 \u03b5\u03bd\u03c4\u03bf\u03c0\u03af\u03b6\u03b5\u03b9 \u03bf\u03c1\u03b1\u03c4\u03ad\u03c2 \u03b1\u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af task \u03b3\u03b9\u03b1 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf review. \u03a3\u03b5 document workflow, \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b5\u03c1\u03b3\u03b1\u03c3\u03c4\u03b5\u03af \u03bc\u03b5 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 <a href=\"https:\/\/twodots.gr\/infinity-parser2-document-ai-epicheiriseis\/\">Document AI \u03b3\u03b9\u03b1 \u03b5\u03be\u03b1\u03b3\u03c9\u03b3\u03ae \u03ba\u03b1\u03b9 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03bf\u03bc\u03ad\u03bd\u03bf\u03c5<\/a>. \u03a3\u03b5 web operations, \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af screenshots \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03ba\u03c4\u03ac \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 \u03b4\u03b9\u03ba\u03b1\u03af\u03c9\u03bc\u03b1 \u03b4\u03b7\u03bc\u03bf\u03c3\u03af\u03b5\u03c5\u03c3\u03b7\u03c2.<\/p>\n<p>\u0397 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7. \u0397 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c4\u03b9\u03bc\u03ae\u03c2, \u03b7 \u03b4\u03b7\u03bc\u03bf\u03c3\u03af\u03b5\u03c5\u03c3\u03b7 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2, \u03b7 \u03b1\u03c0\u03bf\u03c3\u03c4\u03bf\u03bb\u03ae \u03bc\u03b7\u03bd\u03cd\u03bc\u03b1\u03c4\u03bf\u03c2 \u03ae \u03b7 \u03c4\u03c1\u03bf\u03c0\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03ba\u03b1\u03bc\u03c0\u03ac\u03bd\u03b9\u03b1\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 validation, idempotency, audit trail \u03ba\u03b1\u03b9 \u03ad\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bb\u03ac\u03b8\u03bf\u03c5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc. \u0391\u03c5\u03c4\u03cc \u03b9\u03c3\u03c7\u03cd\u03b5\u03b9 \u03b1\u03ba\u03cc\u03bc\u03b7 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c3\u03b5 <a href=\"https:\/\/twodots.gr\/agentic-commerce-seo-ai-agents\/\">agentic commerce workflows<\/a>, \u03cc\u03c0\u03bf\u03c5 \u03bf\u03b9 \u03c0\u03c1\u03ac\u03be\u03b5\u03b9\u03c2 \u03b5\u03bd\u03cc\u03c2 agent \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03c3\u03bf\u03c5\u03bd \u03c4\u03b9\u03bc\u03ad\u03c2, \u03b4\u03b9\u03b1\u03b8\u03b5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03af\u03b1 \u03c0\u03b5\u03bb\u03ac\u03c4\u03b7.<\/p>\n<p>\u03a4\u03bf failure recovery \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03bf\u03b9 retries \u03ad\u03c7\u03bf\u03c5\u03bd \u03cc\u03c1\u03b9\u03b1. \u0391\u03bd \u03ad\u03bd\u03b1 tool \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03ad\u03c8\u03b5\u03b9 error, \u03bf agent \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03b5\u03b9 \u03be\u03b1\u03bd\u03ac \u03ae \u03bd\u03b1 \u03b5\u03c0\u03b9\u03bb\u03ad\u03be\u03b5\u03b9 \u03ac\u03bb\u03bb\u03b7 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ae, \u03b1\u03bb\u03bb\u03ac \u03bf orchestrator \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03b5\u03c2 \u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b5\u03b9\u03b5\u03c2, \u03b5\u03c0\u03b9\u03c4\u03c1\u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ad\u03c2 \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03ba\u03bb\u03b9\u03bc\u03ac\u03ba\u03c9\u03c3\u03b7\u03c2 \u03c3\u03b5 \u03ac\u03bd\u03b8\u03c1\u03c9\u03c0\u03bf.<\/p>\n<h2 id=\"quantization-dflash-taxytita\">Quantization \u03ba\u03b1\u03b9 DFlash: \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b5 \u03c3\u03b1\u03c6\u03ad\u03c2 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/h2>\n<p>\u03a4\u03bf DFlash \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03bb\u03b1\u03c6\u03c1\u03cd block-diffusion draft model \u03b3\u03b9\u03b1 speculative decoding. \u03a0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 blocks \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ce\u03bd tokens \u03ba\u03b1\u03b9 \u03c4\u03bf \u03ba\u03cd\u03c1\u03b9\u03bf Muse Glimmer \u03c4\u03b1 \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03b5\u03b9 \u03c0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03b1. \u0397 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03c4\u03b7\u03c2 Meta \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03ad\u03bd\u03c4\u03b5 draft layers, block size 16 \u03ba\u03b1\u03b9 hidden features \u03b1\u03c0\u03cc \u03c0\u03ad\u03bd\u03c4\u03b5 \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03c4\u03bf\u03c5 \u03ba\u03cd\u03c1\u03b9\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5.<\/p>\n<p>\u03a3\u03c4\u03bf \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03bf test, \u03bf RTX 5090 \u03c0\u03b5\u03c1\u03bd\u03ac \u03b1\u03c0\u03cc 74,9 \u03c3\u03b5 233,4 tokens \u03b1\u03bd\u03ac \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf, \u03bf M4 Max \u03b1\u03c0\u03cc 23,7 \u03c3\u03b5 37,8 \u03ba\u03b1\u03b9 \u03bf M5 Max \u03b1\u03c0\u03cc 26,6 \u03c3\u03b5 50,2. \u03a4\u03b1 \u03bd\u03bf\u03cd\u03bc\u03b5\u03c1\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 \u03c9\u03c2 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03cc\u03c4\u03b9 \u03b7 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2. \u0394\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03b1\u03c0\u03bf\u03cd\u03bd \u03c3\u03b5 \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 prompt, quant \u03ae backend.<\/p>\n<p>\u039f drafter \u03ba\u03b1\u03c4\u03b1\u03bd\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03b7 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03cc\u03c3\u03b1 \u03c0\u03c1\u03bf\u03c4\u03b5\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03b1 tokens \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03ac. \u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae \u03ba\u03c1\u03b1\u03c4\u03ac \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac prompt set, sampling, context, output length \u03ba\u03b1\u03b9 correctness criteria. \u039c\u03b5\u03c4\u03c1\u03ac latency \u03c0\u03c1\u03ce\u03c4\u03bf\u03c5 token, decode speed, \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03c7\u03c1\u03cc\u03bd\u03bf task \u03ba\u03b1\u03b9 \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03b7\u03bc\u03ad\u03bd\u03b7\u03c2 \u03bf\u03bb\u03bf\u03ba\u03bb\u03ae\u03c1\u03c9\u03c3\u03b7\u03c2, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf tokens \u03b1\u03bd\u03ac \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf.<\/p>\n<h2 id=\"benchmarks-ti-apodeiknyoun\">\u03a4\u03b9 \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03bf\u03c5\u03bd \u03c4\u03b1 benchmarks<\/h2>\n<p>\u0397 Meta \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03c4\u03bf Muse Glimmer-30B \u03bc\u03b5 Gemma4-31B Thinking Mode \u03ba\u03b1\u03b9 Qwen3.6-27B Thinking Mode. \u03a3\u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03b7\u03bc\u03bf\u03c3\u03af\u03b5\u03c5\u03c3\u03b5, \u03c4\u03bf Muse Glimmer \u03ad\u03c7\u03b5\u03b9 75,5 \u03c3\u03c4\u03bf MCP Atlas, 74,6 \u03c3\u03c4\u03bf DeepSearch QA, 47,6 \u03c3\u03c4\u03bf WildClawBench \u03ba\u03b1\u03b9 51,2 \u03c3\u03c4\u03bf SWE-Bench Pro. \u03a3\u03c4\u03bf multimodal \u03c3\u03ba\u03ad\u03bb\u03bf\u03c2 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 78,8 \u03c3\u03c4\u03bf Charxiv Reasoning.<\/p>\n<p>\u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03ce\u03c4\u03bf \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd. \u03a4\u03bf Qwen \u03ad\u03c7\u03b5\u03b9 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03c3\u03c4\u03b1 OSWorld-Verified, SWE-Bench Verified, TerminalBench 2.1, ScreenSpot Pro \u03ba\u03b1\u03b9 OmniDocBench. \u03a4\u03bf Gemma \u03ad\u03c7\u03b5\u03b9 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf GPQA Diamond. \u0397 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03b5\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ae \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03c3\u03b5 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac agentic tasks, \u03cc\u03c7\u03b9 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03c5\u03c0\u03b5\u03c1\u03bf\u03c7\u03ae.<\/p>\n<p>\u03a3\u03c4\u03b1 security \u03ba\u03b1\u03b9 privacy benchmarks, \u03cc\u03c0\u03bf\u03c5 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf violation \u03ae attack success rate \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf, \u03c4\u03bf Muse Glimmer \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b4\u03b5\u03bd \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7. \u0397 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03ba\u03ac\u03c1\u03c4\u03b1 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 CI Memories violation 26,4 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 12,1 \u03b3\u03b9\u03b1 Gemma \u03ba\u03b1\u03b9 Siren AgentDojo attack success rate 28,4 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 25,6. \u0391\u03c5\u03c4\u03ac \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03b1\u03bd\u03c4\u03af\u03b2\u03b1\u03c1\u03bf \u03c3\u03c4\u03b7\u03bd \u03b9\u03b4\u03ad\u03b1 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc tool use \u03c3\u03c5\u03bd\u03b5\u03c0\u03ac\u03b3\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1.<\/p>\n<p>\u038c\u03bb\u03bf\u03b9 \u03bf\u03b9 \u03c0\u03b1\u03c1\u03b1\u03c0\u03ac\u03bd\u03c9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03b5\u03af\u03bd\u03b1\u03b9 vendor-reported. \u0393\u03b9\u03b1 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03b1\u03b3\u03bf\u03c1\u03ac\u03c2 hardware \u03ae \u03bc\u03b5\u03c4\u03ac\u03b2\u03b1\u03c3\u03b7 \u03b1\u03c0\u03cc cloud \u03c3\u03b5 local stack, \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc evaluation set \u03bc\u03b5 \u03b5\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, \u03c4\u03b1 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae tools, \u03c4\u03b9\u03c2 \u03b4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03b1\u03c2 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b9\u03c2 \u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b5\u03c2 \u03ba\u03ac\u03b8\u03b5 \u03b1\u03c0\u03bf\u03c4\u03c5\u03c7\u03af\u03b1\u03c2.<\/p>\n<h2 id=\"epiloges-serving-ensomatosis\">\u0395\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 serving \u03ba\u03b1\u03b9 \u03b5\u03bd\u03c3\u03c9\u03bc\u03ac\u03c4\u03c9\u03c3\u03b7\u03c2<\/h2>\n<p>\u03a4\u03bf Muse Glimmer \u03b4\u03b9\u03b1\u03c4\u03af\u03b8\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 BF16 \u03ba\u03b1\u03b9 \u03b4\u03cd\u03bf quantized \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2, \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 DFlash drafter \u03ba\u03b1\u03b9 \u03c4\u03bf\u03bd perception encoder. \u0397 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03c3\u03b5\u03bb\u03af\u03b4\u03b1 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03bc\u03ad\u03c3\u03c9 Transformers, \u03b5\u03bd\u03ce \u03c4\u03bf \u03bf\u03b9\u03ba\u03bf\u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 vLLM, SGLang, llama.cpp, MLX, ExecuTorch \u03ba\u03b1\u03b9 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2 \u03cc\u03c0\u03c9\u03c2 Ollama \u03ba\u03b1\u03b9 LM Studio. \u0397 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03b8\u03b5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c9\u03c1\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03ac\u03b8\u03b5 integration \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03bb\u03ad\u03b3\u03c7\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03b5\u03b3\u03ba\u03b1\u03c4\u03b1\u03c3\u03c4\u03b1\u03b8\u03b5\u03af.<\/p>\n<p>\u0388\u03bd\u03b1 OpenAI-compatible endpoint \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 \u03c4\u03bf integration friction \u03b3\u03b9\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2 \u03c0\u03bf\u03c5 \u03ae\u03b4\u03b7 \u03bc\u03b9\u03bb\u03bf\u03cd\u03bd \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf protocol. \u0394\u03b5\u03bd \u03b5\u03be\u03b9\u03c3\u03ce\u03bd\u03b5\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03c4\u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ce\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd. Tool schemas, multimodal payloads, reasoning controls, token limits \u03ba\u03b1\u03b9 error responses \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 adapter tests \u03ba\u03b1\u03b9 version pinning.<\/p>\n<p>\u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae serving \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03bc\u03b9\u03b1\u03c2 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03b7\u03c2 <a href=\"https:\/\/twodots.gr\/full-stack-ai-integrated-digital-products\/\">full-stack AI \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae\u03c2<\/a>. \u03a4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03ad\u03bd\u03b1 layer. \u0393\u03cd\u03c1\u03c9 \u03c4\u03bf\u03c5 \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03c5\u03bd authentication, queueing, observability, policy engine, vector \u03ae document storage, connectors, \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf review \u03ba\u03b1\u03b9 fallback \u03c0\u03c1\u03bf\u03c2 \u03ac\u03bb\u03bb\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ae \u03c7\u03b5\u03b9\u03c1\u03bf\u03ba\u03af\u03bd\u03b7\u03c4\u03b7 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1.<\/p>\n<div class=\"td-comparison\">\n<p class=\"td-comparison-title\">\u03a4\u03c1\u03b5\u03b9\u03c2 \u03c1\u03b5\u03b1\u03bb\u03b9\u03c3\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ad\u03c2 deployment \u03b3\u03b9\u03b1 \u03c4\u03bf Muse Glimmer<\/p>\n<div class=\"td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid--three\">\n<div class=\"td-platform-card\">\n<h3>Local workstation<\/h3>\n<p>Quantized \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03c3\u03b5 24 GB \u03ae 32 GB VRAM \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1 \u03c3\u03c4\u03b5\u03bd\u03cc workflow, \u03bc\u03b9\u03ba\u03c1\u03cc concurrency \u03ba\u03b1\u03b9 \u03ac\u03bc\u03b5\u03c3\u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c4\u03bf\u03c5 \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd\u03c4\u03bf\u03c2.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u03a7\u03b1\u03bc\u03b7\u03bb\u03ae \u03b4\u03b9\u03ba\u03c4\u03c5\u03b1\u03ba\u03ae \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7<\/span><span class=\"td-badge\">Capacity \u03cc\u03c1\u03b9\u03bf<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<h3>\u0395\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc\u03c2 inference server<\/h3>\n<p>\u039a\u03b5\u03bd\u03c4\u03c1\u03b9\u03ba\u03cc serving \u03bc\u03b5 vLLM \u03ae SGLang, \u03ba\u03bf\u03b9\u03bd\u03cc policy layer, monitoring \u03ba\u03b1\u03b9 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u039a\u03bf\u03b9\u03bd\u03ae \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7<\/span><span class=\"td-badge\">\u03a5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae \u03ba\u03b1\u03b9 SRE<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<h3>Managed \u03ae hybrid<\/h3>\n<p>\u0393\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf pilot \u03ae fallback \u03c3\u03b5 managed provider, \u03bc\u03b5 \u03b1\u03c5\u03c3\u03c4\u03b7\u03c1\u03cc data routing \u03ce\u03c3\u03c4\u03b5 \u03b5\u03c5\u03b1\u03af\u03c3\u03b8\u03b7\u03c4\u03b1 inputs \u03bd\u03b1 \u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03c3\u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03b7 \u03b6\u03ce\u03bd\u03b7.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u0395\u03bb\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1<\/span><span class=\"td-badge\">\u0388\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"idiotikotita-asfaleia-dikaiomata\">\u0399\u03b4\u03b9\u03c9\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1, \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03ba\u03b1\u03b9\u03ce\u03bc\u03b1\u03c4\u03b1 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03c9\u03bd<\/h2>\n<p>\u03a4\u03bf local inference \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c0\u03bf\u03c3\u03c4\u03bf\u03bb\u03ae \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03c3\u03b5 \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03cc provider \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c8\u03b5\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 \u03c7\u03c9\u03c1\u03af\u03c2 \u03c3\u03c5\u03bd\u03b5\u03c7\u03ae \u03c3\u03cd\u03bd\u03b4\u03b5\u03c3\u03b7. \u0397 \u03b9\u03b4\u03b9\u03c9\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03cc\u03bc\u03c9\u03c2 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03b7 \u03c4\u03b7 \u03c1\u03bf\u03ae: logs, prompt traces, caches, backups, telemetry, image stores, connectors \u03ba\u03b1\u03b9 \u03bb\u03bf\u03b3\u03b1\u03c1\u03b9\u03b1\u03c3\u03bc\u03bf\u03cd\u03c2 \u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03b9\u03ce\u03bd.<\/p>\n<p>\u0388\u03bd\u03b1\u03c2 agent \u03bc\u03b5 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7 \u03c3\u03b5 browser, repository, ERP \u03ae e-shop \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03ba\u03b1\u03bb\u03ad\u03c3\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03b1\u03bd \u03c4\u03b1 weights \u03c4\u03c1\u03ad\u03c7\u03bf\u03c5\u03bd \u03c3\u03c4\u03bf \u03b4\u03b9\u03c0\u03bb\u03b1\u03bd\u03cc \u03b4\u03c9\u03bc\u03ac\u03c4\u03b9\u03bf. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 least privilege, \u03c0\u03c1\u03bf\u03c3\u03c9\u03c1\u03b9\u03bd\u03ac credentials, allowlists \u03ba\u03b1\u03b9 <a href=\"https:\/\/twodots.gr\/dynamika-dikaiomata-ai-agents-elachisti-prosvasi\/\">\u03b4\u03c5\u03bd\u03b1\u03bc\u03b9\u03ba\u03ac \u03b4\u03b9\u03ba\u03b1\u03b9\u03ce\u03bc\u03b1\u03c4\u03b1 \u03b5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03b7\u03c2 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7\u03c2 \u03b3\u03b9\u03b1 AI agents<\/a>. \u039f\u03b9 \u03bc\u03b7 \u03b1\u03bd\u03b1\u03c3\u03c4\u03c1\u03ad\u03c8\u03b9\u03bc\u03b5\u03c2 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b5\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b5\u03c1\u03bd\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc confirmation \u03ae \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03cc execution service.<\/p>\n<p>\u0397 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03ba\u03ac\u03c1\u03c4\u03b1 \u03b1\u03bd\u03b1\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03b1\u03bd\u03b1\u03ba\u03c1\u03b9\u03b2\u03b5\u03af\u03c2, \u03bc\u03b5\u03c1\u03bf\u03bb\u03b7\u03c0\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ae \u03c0\u03c1\u03bf\u03b2\u03bb\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03bb\u03ac\u03b8\u03b7 \u03c3\u03b5 novel multi-step scenarios. \u03a3\u03c5\u03bd\u03b9\u03c3\u03c4\u03ac \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03b1 guardrails \u03ba\u03b1\u03b9 human-in-the-loop confirmation \u03cc\u03c4\u03b1\u03bd \u03bf agent \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03bd\u03b5\u03c1\u03b3\u03ae\u03c3\u03b5\u03b9 \u03c3\u03c4\u03bf\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03bc\u03bf. \u0391\u03c5\u03c4\u03ae \u03b7 \u03c3\u03cd\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03b1\u03c0\u03b5\u03af \u03c3\u03b5 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae, \u03cc\u03c7\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03af\u03bd\u03b5\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03b1\u03c1\u03c7\u03ae.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-label\">\u03a0\u03cc\u03c4\u03b5 \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03b5\u03c1\u03b8\u03b5\u03af \u03ad\u03bd\u03b1 multimodal agent workflow \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac;<\/p>\n<p><strong>\u038c\u03c4\u03b1\u03bd \u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, \u03b7 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 \u03c7\u03c9\u03c1\u03af\u03c2 \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03ae \u03b7 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7 \u03ad\u03c7\u03bf\u03c5\u03bd \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b1\u03be\u03af\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03bf workload \u03c0\u03b5\u03c1\u03bd\u03ac \u03c4\u03bf \u03af\u03b4\u03b9\u03bf evaluation set \u03bc\u03b5 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03ae \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1, latency \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2.<\/strong><\/p>\n<p>\u0397 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 local \u03ba\u03b1\u03b9 managed baseline, \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 hardware \u03b1\u03c0\u03cc\u03c3\u03b2\u03b5\u03c3\u03b7, \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1, updates, failure recovery \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf \u03c7\u03c1\u03cc\u03bd\u03bf.<\/p>\n<\/div>\n<h2 id=\"pilot-aksiologisis-epicheirisis\">\u03a0\u03ce\u03c2 \u03c3\u03c4\u03ae\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc pilot<\/h2>\n<p>\u03a4\u03bf \u03c3\u03c9\u03c3\u03c4\u03cc pilot \u03b4\u03b5\u03bd \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03bc\u03b5 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03b5\u03bd\u03c4\u03bf\u03bb\u03ae \u00ab\u03c6\u03c4\u03b9\u03ac\u03be\u03c4\u03b5 \u03ad\u03bd\u03b1\u03bd \u03b1\u03c5\u03c4\u03cc\u03bd\u03bf\u03bc\u03bf agent\u00bb. \u0395\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 \u03bc\u03af\u03b1 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03bc\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 inputs, tools, \u03b9\u03b4\u03b9\u03bf\u03ba\u03c4\u03ae\u03c4\u03b7 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1. \u0393\u03b9\u03b1 \u03c4\u03bf Muse Glimmer, \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf multimodal \u03ae local \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1, \u03b1\u03bb\u03bb\u03b9\u03ce\u03c2 \u03ad\u03bd\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf text-only \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b2\u03ac\u03c3\u03b7.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u03a0\u03ad\u03bd\u03c4\u03b5 \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1 pilot Muse Glimmer \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03b1<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 1<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1 \u03c3\u03c4\u03b5\u03bd\u03cc multimodal workload<\/strong>\n<p>\u0395\u03c0\u03b9\u03bb\u03ad\u03be\u03c4\u03b5, \u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03c9\u03bd \u03ae \u03b5\u03be\u03b1\u03b3\u03c9\u03b3\u03ae \u03b1\u03c0\u03cc screenshots \u03bc\u03b5 read-only output \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf \u03b9\u03b4\u03b9\u03bf\u03ba\u03c4\u03ae\u03c4\u03b7.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 2<\/span><strong>\u03a7\u03c4\u03af\u03c3\u03c4\u03b5 \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc evaluation set<\/strong>\n<p>\u03a3\u03c5\u03bb\u03bb\u03ad\u03be\u03c4\u03b5 \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03c5\u03c4\u03b9\u03ba\u03ac \u03b5\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03ac inputs, \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03b5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2, prompt injections \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b1\u03bc\u03b5\u03bd\u03cc\u03bc\u03b5\u03bd\u03b1 structured outputs \u03c0\u03c1\u03b9\u03bd \u03c3\u03c5\u03bd\u03b4\u03b5\u03b8\u03b5\u03af \u03bf\u03c0\u03bf\u03b9\u03bf\u03b4\u03ae\u03c0\u03bf\u03c4\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03cc tool.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 3<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 quant \u03ba\u03b1\u03b9 hardware \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf<\/strong>\n<p>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 24 GB \u03ba\u03b1\u03b9 32 GB \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 \u03c9\u03c2 \u03c0\u03c1\u03bf\u03c2 task success, first-token latency, \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03c7\u03c1\u03cc\u03bd\u03bf, \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03b5\u03bd\u03b5\u03c1\u03b3\u03b5\u03b9\u03b1\u03ba\u03cc \u03ae \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 4<\/span><strong>\u03a0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03c3\u03c4\u03b5 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03b1 \u03ba\u03b1\u03b9 retries<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 allowlisted schemas, least-privilege credentials, \u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c0\u03c1\u03bf\u03c3\u03c0\u03b1\u03b8\u03b5\u03b9\u03ce\u03bd, validation \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03ad\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03ba\u03ac\u03b8\u03b5 \u03bc\u03b7 \u03b1\u03bd\u03b1\u03c3\u03c4\u03c1\u03ad\u03c8\u03b9\u03bc\u03b7 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 5<\/span><strong>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 \u03bc\u03b5 managed \u03ba\u03b1\u03b9 manual baseline<\/strong>\n<p>\u0397 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 go\/no-go \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03af\u03b4\u03b9\u03b1 cases, \u03af\u03b4\u03b9\u03bf quality threshold, \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ad\u03c2 rollback \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, quant \u03ae backend.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u03a4\u03bf pilot \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03af\u03b5\u03c2 \u03b1\u03bb\u03bb\u03ac \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 \u03b1\u03c0\u03bf\u03c4\u03c5\u03c7\u03af\u03b1\u03c2: \u03bb\u03ac\u03b8\u03bf\u03c2 \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2, incorrect tool arguments, failure loops, \u03c5\u03c0\u03ad\u03c1\u03b2\u03b1\u03c3\u03b7 context, \u03b1\u03c3\u03c4\u03b1\u03b8\u03ad\u03c2 structured output \u03ba\u03b1\u03b9 human overrides. \u0391\u03c5\u03c4\u03ae \u03b7 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b1\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03ad\u03bf prompt, \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf schema, \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc quant \u03ae \u03ac\u03bb\u03bb\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<h2 id=\"symperasma\">\u0417\u0430\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435<\/h2>\n<p>\u03a4\u03bf Muse Glimmer \u03c6\u03ad\u03c1\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b1\u03c3\u03c5\u03bd\u03ae\u03b8\u03b9\u03c3\u03c4\u03b1 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 \u03c0\u03b1\u03ba\u03ad\u03c4\u03bf \u03c3\u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b1 \u03c4\u03c9\u03bd \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 30B open-weight \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd: \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf context, perception encoder, agentic training, tool use, quantized weights \u03ba\u03b1\u03b9 DFlash. \u0397 \u03ac\u03b4\u03b5\u03b9\u03b1 Apache 2.0 \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 serving \u03bc\u03b5\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03c3\u03c4\u03b1\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b1\u03c6\u03b1\u03b9\u03c1\u03bf\u03cd\u03bd \u03c4\u03b9\u03c2 \u03b1\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae\u03c2 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1\u03c2.<\/p>\n<p>\u0397 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u00ab\u03c4\u03c1\u03ad\u03c7\u03b5\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac\u00bb, \u03b1\u03bb\u03bb\u03ac \u03b1\u03bd \u03b5\u03ba\u03c4\u03b5\u03bb\u03b5\u03af \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf workload \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03ae \u03c0\u03b9\u03bf \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03ba\u03c4\u03b9\u03ba\u03ad\u03c2. \u0391\u03c5\u03c4\u03cc \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03b1, \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1, \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 rollback.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-eyebrow\">\u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af \u0395\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd &amp; AI \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd TWO DOTS<\/p>\n<p class=\"td-service-cta-title\">\u0391\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1\u03bd local multimodal agent \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 workflow.<\/p>\n<p>\u0397 TWO DOTS \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03b5\u03b9 \u03c4\u03bf pilot, \u03c4\u03b1 read-only tools, \u03c4\u03bf evaluation set, \u03c4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03b4\u03b9\u03ba\u03b1\u03b9\u03c9\u03bc\u03ac\u03c4\u03c9\u03bd \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 local \u03bc\u03b5 managed \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae. \u0397 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1, \u03cc\u03c7\u03b9 \u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03bf \u03c0\u03af\u03bd\u03b1\u03ba\u03b1 benchmarks.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u0394\u03b5\u03af\u03c4\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd<\/a><\/div>\n<\/div>\n<\/section>\n<section id=\"sychnes-erotiseis\" class=\"td-faq-section\">\n<div class=\"td-faq\">\n<p class=\"td-faq-heading\">\u0427\u0435\u0441\u0442\u043e \u0437\u0430\u0434\u0430\u0432\u0430\u043d\u0438 \u0432\u044a\u043f\u0440\u043e\u0441\u0438<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf Muse Glimmer;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf Muse Glimmer \u03b5\u03af\u03bd\u03b1\u03b9 open-weight dense \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 30B \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd \u03c4\u03b7\u03c2 Meta, \u03b1\u03c0\u03bf\u03c3\u03c4\u03b1\u03b3\u03bc\u03ad\u03bd\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf Muse Spark \u03ba\u03b1\u03b9 \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03ad\u03bd\u03bf \u03b3\u03b9\u03b1 local agentic \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2. \u0394\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf \u03ba\u03b1\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03b5 \u03c0\u03bf\u03b9\u03b1 \u03ac\u03b4\u03b5\u03b9\u03b1 \u03b4\u03b9\u03b1\u03c4\u03af\u03b8\u03b5\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b1 weights;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 Meta \u03b4\u03b9\u03b1\u03b8\u03ad\u03c4\u03b5\u03b9 \u03c4\u03b1 weights \u03c4\u03bf\u03c5 Muse Glimmer \u03bc\u03b5 \u03ac\u03b4\u03b5\u03b9\u03b1 Apache 2.0. \u0397 \u03ac\u03b4\u03b5\u03b9\u03b1 \u03b4\u03b9\u03b5\u03c5\u03ba\u03bf\u03bb\u03cd\u03bd\u03b5\u03b9 \u03b5\u03bc\u03c0\u03bf\u03c1\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7, \u03b1\u03bb\u03bb\u03ac \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c4\u03c9\u03bd \u03cc\u03c1\u03c9\u03bd, \u03c4\u03c9\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, \u03c4\u03b7\u03c2 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03c2 \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae\u03c2 \u03c3\u03c5\u03bc\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 hardware \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 \u03c0\u03bb\u03ae\u03c1\u03b7\u03c2 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc 55 GB \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2. \u0397 Meta \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 4-bit \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c5\u03c2 24 GB \u03ba\u03b1\u03b9 32 GB VRAM, \u03cc\u03bc\u03c9\u03c2 \u03b7 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03c0\u03b1\u03af\u03c4\u03b7\u03c3\u03b7 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc context, KV cache, \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, concurrency \u03ba\u03b1\u03b9 drafter.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, \u03b2\u03af\u03bd\u03c4\u03b5\u03bf \u03ba\u03b1\u03b9 \u03ae\u03c7\u03bf;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03ba\u03ac\u03c1\u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03b5\u03af\u03c3\u03bf\u03b4\u03bf \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03ad\u03be\u03bf\u03b4\u03bf \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5. \u03a4\u03bf \u03b2\u03af\u03bd\u03c4\u03b5\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03c4\u03b5\u03af \u03c9\u03c2 \u03b4\u03b9\u03b1\u03b4\u03bf\u03c7\u03b9\u03ba\u03ac frames, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ac \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03bf \u03b3\u03b9\u03b1 video \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 audio input \u03ae output.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf DFlash \u03c3\u03c4\u03bf Muse Glimmer;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf DFlash \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03bf\u03b1\u03b9\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc\u03c2 speculative-decoding drafter \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 blocks tokens \u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03b7 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf \u03ba\u03cd\u03c1\u03b9\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u0397 Meta \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf decode speed \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c4\u03b7\u03c2 \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf, \u03bc\u03b5 \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03ac\u03bb\u03c9\u03c3\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03b1\u03c3\u03c4\u03ae\u03c3\u03b5\u03b9 \u03bc\u03b9\u03b1 cloud \u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03af\u03b1 AI;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bc\u03ac\u03c4\u03c9\u03c2. \u03a4\u03bf local deployment \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03ba\u03b1\u03b9 \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1, \u03b1\u03bb\u03bb\u03ac \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 hardware, \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03c3\u03b5\u03b9\u03c2, monitoring, backups, capacity planning \u03ba\u03b1\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03c5\u03c0\u03bf\u03c3\u03c4\u03ae\u03c1\u03b9\u03be\u03b7. \u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf workload.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03ce\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03b1\u03c3\u03c4\u03bf\u03cd\u03bd \u03c4\u03b1 benchmarks;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03b1 benchmarks \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03b7\u03bc\u03bf\u03c3\u03af\u03b5\u03c5\u03c3\u03b1\u03bd \u03b7 Meta \u03ba\u03b1\u03b9 \u03b7 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03ba\u03ac\u03c1\u03c4\u03b1 \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5. \u0394\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03b9\u03c3\u03c7\u03c5\u03c1\u03ad\u03c2 \u03b5\u03c0\u03b9\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b5 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ad\u03c2 agentic \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2, \u03b1\u03bb\u03bb\u03ac \u03cc\u03c7\u03b9 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03c5\u03c0\u03b5\u03c1\u03bf\u03c7\u03ae \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 workflow.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0395\u03af\u03bd\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03c1\u03ad\u03c7\u03b5\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0397 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7 \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03b1\u03c0\u03cc \u03bc\u03cc\u03bd\u03b7 \u03c4\u03b7\u03c2 \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b1 tool calls, prompt injection, \u03c5\u03c0\u03b5\u03c1\u03b2\u03bf\u03bb\u03b9\u03ba\u03ac \u03b4\u03b9\u03ba\u03b1\u03b9\u03ce\u03bc\u03b1\u03c4\u03b1, \u03b4\u03b9\u03b1\u03c1\u03c1\u03bf\u03ad\u03c2 \u03bc\u03ad\u03c3\u03c9 logs \u03ae \u03bc\u03b7 \u03b1\u03bd\u03b1\u03c3\u03c4\u03c1\u03ad\u03c8\u03b9\u03bc\u03b5\u03c2 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b5\u03c2. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 least privilege, validation, \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03ad\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">\u0418\u0437\u0442\u043e\u0447\u043d\u0438\u0446\u0438<\/p>\n<ul>\n<li><a href=\"https:\/\/research.meta.ai\/blog\/introducing-muse-glimmer-open-agentic-model\" target=\"_blank\" rel=\"noopener\">Meta AI Research \u2014 Introducing Muse Glimmer: An Open Agentic Model That Runs on Your Device<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/meta-models\/Muse-Glimmer-30B\" target=\"_blank\" rel=\"noopener\">Meta Models \u2014 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03ba\u03ac\u03c1\u03c4\u03b1 Muse Glimmer-30B \u03c3\u03c4\u03bf Hugging Face<\/a><\/li>\n<li><a href=\"https:\/\/dev.meta.ai\/docs\/muse-glimmer\" target=\"_blank\" rel=\"noopener\">Meta AI Developer Center \u2014 \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03af\u03c9\u03c3\u03b7 Muse Glimmer<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2602.06036\" target=\"_blank\" rel=\"noopener\">DFlash: Block Diffusion for Flash Speculative Decoding<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2504.13181\" target=\"_blank\" rel=\"noopener\">Perception Encoder: The Best Visual Embeddings Are Not at the Output of the Network<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf Muse Glimmer \u03b5\u03af\u03bd\u03b1\u03b9 open-weight multimodal \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf 30B \u03c4\u03b7\u03c2 Meta \u03b3\u03b9\u03b1 local AI agents, \u03bc\u03b5 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, tool use, DFlash \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 \u03c4\u03bf\u03c0\u03b9\u03ba\u03bf\u03cd deployment.<\/p>","protected":false},"author":1,"featured_media":88615,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[17366],"tags":[6506,19217,18467,19216,19218],"class_list":["post-88575","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-marketing","tag-ai-agents","tag-local-ai","tag-multimodal-ai","tag-muse-glimmer","tag-open-source-ai-2"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":7}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/88575","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/comments?post=88575"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/88575\/revisions"}],"predecessor-version":[{"id":88616,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/88575\/revisions\/88616"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media\/88615"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media?parent=88575"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/categories?post=88575"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/tags?post=88575"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}