{"id":97894,"date":"2026-09-26T16:49:29","date_gmt":"2026-09-26T13:49:29","guid":{"rendered":"https:\/\/twodots.gr\/?p=97894"},"modified":"2026-09-26T16:49:30","modified_gmt":"2026-09-26T13:49:30","slug":"saem-stadia-syllogismou-kostos-moe-montelon","status":"publish","type":"post","link":"https:\/\/twodots.gr\/bg\/saem-stadia-syllogismou-kostos-moe-montelon\/","title":{"rendered":"SAEM: \u03c0\u03ce\u03c2 \u03c4\u03b1 \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03c3\u03c5\u03bb\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03bf\u03cd \u03bc\u03b5\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03c9\u03bd MoE \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u0391\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ce\u03c4\u03b1:<\/strong> \u03c4\u03bf SAEM \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03bf\u03c5 MoE inference \u03cc\u03c7\u03b9 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03b1\u03bb\u03bb\u03ac \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b4\u03b9\u03b1\u03c7\u03b5\u03b9\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 experts, GPU cache \u03ba\u03b1\u03b9 CPU\u2013GPU \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b1 \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03c4\u03bf\u03c5 reasoning. \u03a3\u03c4\u03b9\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03ba\u03b1\u03c4\u03ac \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf 1,33\u00d7 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf throughput \u03b1\u03c0\u03cc \u03c4\u03bf \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf baseline \u03ba\u03b1\u03b9 1,54\u00d7 \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf calibration dataset \u03c4\u03b1\u03af\u03c1\u03b9\u03b1\u03b6\u03b5 \u03bc\u03b5 \u03c4\u03bf workload.<\/p>\n<p>\u03a4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03b3\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03c4\u03c1\u03ad\u03c7\u03bf\u03c5\u03bd \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 reasoning \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bc\u03b5 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b7 GPU \u03bc\u03bd\u03ae\u03bc\u03b7, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 cloud bill. \u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03b4\u03cd\u03bf MoE \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03c4\u03c1\u03af\u03b1 benchmarks \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03ba\u03cc\u03bc\u03b2\u03bf A100\/Xeon\u00b7 production \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03c4\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac traces \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03bf\u03cd\u03bd \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1 stage-level \u03c3\u03c5\u03bd\u03bf\u03c7\u03ae.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">\u0421\u044a\u0434\u044a\u0440\u0436\u0430\u043d\u0438\u0435<\/div>\n<ul>\n<li><a href=\"#provlima-moe-inference\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf reasoning \u03c0\u03b9\u03ad\u03b6\u03b5\u03b9 \u03c4\u03bf MoE inference<\/a><\/li>\n<li><a href=\"#stage-level-synoxi\">\u0397 \u03c0\u03b1\u03c1\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7 \u03c0\u03af\u03c3\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf SAEM<\/a><\/li>\n<li><a href=\"#entopismos-orion-stadiou\">\u03a0\u03ce\u03c2 \u03b5\u03bd\u03c4\u03bf\u03c0\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03c9\u03bd \u03c3\u03c4\u03b1\u03b4\u03af\u03c9\u03bd<\/a><\/li>\n<li><a href=\"#stage-aware-cache-prefetch\">Stage-aware cache \u03ba\u03b1\u03b9 prefetch<\/a><\/li>\n<li><a href=\"#cpu-energos-rolos\">\u0397 CPU \u03c9\u03c2 \u03b5\u03bd\u03b5\u03c1\u03b3\u03cc \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 inference<\/a><\/li>\n<li><a href=\"#token-repacking\">Token repacking \u03ba\u03b1\u03b9 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 fragmented kernels<\/a><\/li>\n<li><a href=\"#apotelesmata-saem\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03bf\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2<\/a><\/li>\n<li><a href=\"#concurrency-ablation-oracle\">Concurrency, ablation \u03ba\u03b1\u03b9 oracle<\/a><\/li>\n<li><a href=\"#ai-proionta-kostos\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 AI \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/a><\/li>\n<li><a href=\"#production-elegxoi\">\u03a4\u03b9 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03b7\u03b8\u03b5\u03af \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc production<\/a><\/li>\n<li><a href=\"#epta-vimata-pilot\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 pilot<\/a><\/li>\n<li><a href=\"#oria-symperasma\">\u038c\u03c1\u03b9\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"provlima-moe-inference\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf reasoning \u03c0\u03b9\u03ad\u03b6\u03b5\u03b9 \u03c4\u03bf MoE inference<\/h2>\n<p>\u038c\u03c3\u03bf \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1 reasoning, \u03c4\u03cc\u03c3\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 \u03b7 \u03b1\u03c0\u03b1\u03af\u03c4\u03b7\u03c3\u03b7 \u03c3\u03b5 \u03c7\u03c1\u03cc\u03bd\u03bf, \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03b1\u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd. \u0397 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b1\u03c5\u03c4\u03bf\u03c0\u03b1\u03bb\u03af\u03bd\u03b4\u03c1\u03bf\u03bc\u03b7: \u03ba\u03ac\u03b8\u03b5 \u03bd\u03ad\u03bf token \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03cc\u03c3\u03b1 \u03ad\u03c7\u03bf\u03c5\u03bd \u03ae\u03b4\u03b7 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b7\u03b8\u03b5\u03af. \u03a3\u03b5 traces \u03b5\u03ba\u03b1\u03c4\u03bf\u03bd\u03c4\u03ac\u03b4\u03c9\u03bd \u03ae \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03c9\u03bd tokens, \u03b7 \u03ba\u03b1\u03b8\u03c5\u03c3\u03c4\u03ad\u03c1\u03b7\u03c3\u03b7 \u03b1\u03c0\u03bf\u03ba\u03c9\u03b4\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03b7 \u03c0\u03af\u03b5\u03c3\u03b7 \u03c3\u03c4\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ba\u03b5\u03bd\u03c4\u03c1\u03b9\u03ba\u03cc \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03bf\u03cd.<\/p>\n<p>\u03a3\u03c4\u03b1 Mixture-of-Experts \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03ad\u03bd\u03b1\u03c2 router \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03ad\u03bd\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc \u03c5\u03c0\u03bf\u03c3\u03cd\u03bd\u03bf\u03bb\u03bf experts \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 token. \u0397 \u03b1\u03c1\u03b1\u03b9\u03ae \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf\u03bd \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc \u03b1\u03bd\u03ac token, \u03b1\u03bb\u03bb\u03ac \u03c4\u03b1 \u03c0\u03bb\u03ae\u03c1\u03b7 expert weights \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03be\u03b5\u03c0\u03b5\u03c1\u03bd\u03bf\u03cd\u03bd \u03c4\u03b7 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b7 GPU \u03bc\u03bd\u03ae\u03bc\u03b7. \u03a4\u03cc\u03c4\u03b5 \u03c4\u03bf runtime \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b2\u03ac\u03c1\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b7 CPU \u03ae \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b5\u03bb\u03b5\u03af \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03b5\u03ba\u03b5\u03af.<\/p>\n<p>\u03a4\u03bf Qwen3-30B-A3B \u03c0\u03bf\u03c5 \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03b9 \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c4\u03b5\u03b9 128 routed experts \u03ba\u03b1\u03b9 \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03bf\u03ba\u03c4\u03ce \u03b1\u03bd\u03ac token. \u03a4\u03bf ERNIE-4.5-21B-A3B-Thinking \u03ad\u03c7\u03b5\u03b9 64 routed \u03ba\u03b1\u03b9 \u03b4\u03cd\u03bf shared experts, \u03bc\u03b5 top-6 routing. \u0397 \u03af\u03b4\u03b9\u03b1 \u03ad\u03bd\u03c4\u03b1\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03b1\u03c1\u03b1\u03b9\u03ae \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7, cache \u03ba\u03b1\u03b9 bandwidth \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03c5\u03bc\u03b5 \u03b3\u03b9\u03b1\u03c4\u03af \u03c3\u03c4\u03b1 <a href=\"https:\/\/twodots.gr\/moe-modela-stin-akri-cache-bandwidth-bottleneck\/\">MoE \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b7 cache \u03b4\u03b5\u03bd \u03bb\u03cd\u03bd\u03b5\u03b9 \u03bc\u03cc\u03bd\u03b7 \u03c4\u03b7\u03c2 \u03c4\u03bf bandwidth bottleneck<\/a>.<\/p>\n<p>\u039f\u03b9 \u03c3\u03c5\u03bd\u03ae\u03b8\u03b5\u03b9\u03c2 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ad\u03c2 caching \u03b1\u03bd\u03c4\u03b9\u03b4\u03c1\u03bf\u03cd\u03bd \u03c3\u03c4\u03b7 \u03c3\u03c5\u03c7\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ae \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03c3\u03c6\u03b1\u03c4\u03b7 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03c3\u03b5 \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03bf token. \u03a4\u03bf SAEM \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b1 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7\u03c2: \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03bf stream, \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1 \u03c9\u03c2 \u03b4\u03b9\u03b1\u03ba\u03c1\u03b9\u03c4\u03ac \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2, \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae\u03c2 \u03c0\u03bf\u03c1\u03b5\u03af\u03b1\u03c2, \u03b4\u03b9\u03cc\u03c1\u03b8\u03c9\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7\u03c2. \u0397 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03ad\u03c7\u03b5\u03b9 \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ac \u03c3\u03b5 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03cc\u03c0\u03bf\u03c5 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/overthinking-ai-montela-enischymeni-skepsi-krymmeni-gnosi\/\">overthinking \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf \u03bc\u03ae\u03ba\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03bd\u03b9\u03c3\u03c7\u03c5\u03bc\u03ad\u03bd\u03b7\u03c2 \u03c3\u03ba\u03ad\u03c8\u03b7\u03c2<\/a> \u03c7\u03c9\u03c1\u03af\u03c2 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd token \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03af\u03c3\u03b7 \u03b1\u03be\u03af\u03b1.<\/p>\n<h2 id=\"stage-level-synoxi\">\u0397 \u03c0\u03b1\u03c1\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7 \u03c0\u03af\u03c3\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf SAEM<\/h2>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03bc\u03b5\u03c4\u03c1\u03bf\u03cd\u03bd \u03c4\u03b7 \u03c3\u03c5\u03bd\u03bf\u03c7\u03ae \u03c4\u03c9\u03bd \u03bc\u03bf\u03c4\u03af\u03b2\u03c9\u03bd \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03b4\u03bf\u03c7\u03b9\u03ba\u03ac \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03bc\u03b5 \u03bc\u03ad\u03c3\u03b7 layer-wise cosine similarity. \u03a3\u03c4\u03b1 MATH-500, AIME 2024 \u03ba\u03b1\u03b9 GPQA-Diamond, \u03bf \u03bc\u03ad\u03c3\u03bf\u03c2 \u03b4\u03b5\u03af\u03ba\u03c4\u03b7\u03c2 temporal coherence \u03b5\u03af\u03bd\u03b1\u03b9 89,30%. \u039f\u03b9 \u03ad\u03be\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b1\u03bd\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b1\u03b9 dataset \u03ba\u03b9\u03bd\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc 85,27% \u03ad\u03c9\u03c2 92,01%.<\/p>\n<p>\u0397 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03af\u03b4\u03b9\u03bf\u03c5\u03c2 experts \u03bf\u03cd\u03c4\u03b5 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf \u03c3\u03c4\u03ac\u03b4\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03bd\u03c9\u03c3\u03c4\u03cc \u03b5\u03ba \u03c4\u03c9\u03bd \u03c0\u03c1\u03bf\u03c4\u03ad\u03c1\u03c9\u03bd. \u0394\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03b9\u03c3\u03c7\u03c5\u03c1\u03ae \u03ce\u03c3\u03c4\u03b5 \u03b7 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03c3\u03c4\u03b1\u03b4\u03af\u03bf\u03c5 \u03c0\u03bf\u03c5 \u03bc\u03cc\u03bb\u03b9\u03c2 \u03bf\u03bb\u03bf\u03ba\u03bb\u03b7\u03c1\u03ce\u03b8\u03b7\u03ba\u03b5 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03c3\u03ae\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03c4\u03bf\u03c0\u03bf\u03b8\u03ad\u03c4\u03b7\u03c3\u03b7.<\/p>\n<p>\u03a0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03b1, \u03c4\u03b1 traces \u03b4\u03b5\u03bd \u03ad\u03c7\u03bf\u03c5\u03bd \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03b7 \u03b4\u03bf\u03bc\u03ae. \u03a3\u03c4\u03b1 \u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 Qwen3 \u03c4\u03bf\u03c5 MATH-500, \u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2 \u03c3\u03c4\u03b1\u03b4\u03af\u03c9\u03bd \u03ba\u03c5\u03bc\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc 1 \u03ad\u03c9\u03c2 49, \u03bc\u03b5 \u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03bf 5 \u03ba\u03b1\u03b9 \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf 8. \u03a4\u03bf \u03bc\u03ae\u03ba\u03bf\u03c2 \u03c3\u03c4\u03b1\u03b4\u03af\u03bf\u03c5 \u03ba\u03c5\u03bc\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc 55 \u03ad\u03c9\u03c2 7.129 tokens, \u03bc\u03b5 \u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03bf 271 \u03ba\u03b1\u03b9 \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf 484. \u03a4\u03bf SAEM \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03b3\u03b5\u03b9\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2, \u03cc\u03c7\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c5\u03c0\u03cc\u03b8\u03b5\u03c3\u03b7 \u03cc\u03c4\u03b9 \u03cc\u03bb\u03b1 \u03c4\u03b1 \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03cd\u03bd\u03c4\u03bf\u03bc\u03b1.<\/p>\n<aside class=\"td-article-note\"><strong>\u03a3\u03c5\u03bd\u03bf\u03c7\u03ae \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1:<\/strong> \u03c4\u03bf 89,30% \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c3\u03bf\u03c2 \u03b4\u03b5\u03af\u03ba\u03c4\u03b7\u03c2 cosine similarity \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03b4\u03bf\u03c7\u03b9\u03ba\u03ac stage-level activation profiles. \u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 cache hit rate, \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc \u03af\u03b4\u03b9\u03b1\u03c2 \u03bb\u03af\u03c3\u03c4\u03b1\u03c2 experts \u03ae \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03ac\u03bb\u03bb\u03bf workload.<\/aside>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-kicker\">\u0391\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c4\u03bf\u03c5 preprint<\/p>\n<p class=\"td-chart-title\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03c0\u03bf\u03c5 \u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf SAEM<\/p>\n<p class=\"td-chart-intro\">\u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03b4\u03cd\u03bf MoE \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03c4\u03c1\u03af\u03b1 reasoning benchmarks \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf A100\/Xeon \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2.<\/p>\n<\/div>\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">89,30%<\/span><span class=\"td-metric-label\">\u03bc\u03ad\u03c3\u03b7 temporal coherence \u03b4\u03b9\u03b1\u03b4\u03bf\u03c7\u03b9\u03ba\u03ce\u03bd reasoning stages<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">1,33\u00d7<\/span><span class=\"td-metric-label\">\u03bc\u03ad\u03c3\u03bf throughput gain \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03bf\u03c5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 baseline<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">1,54\u00d7<\/span><span class=\"td-metric-label\">\u03bc\u03ad\u03c3\u03bf gain \u03cc\u03c4\u03b1\u03bd calibration \u03ba\u03b1\u03b9 workload \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">54,2% \u2192 40,0%<\/span><span class=\"td-metric-label\">\u03bc\u03b5\u03c1\u03af\u03b4\u03b9\u03bf bookkeeping overhead \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf token repacking<\/span><\/div>\n<\/div>\n<p class=\"td-chart-source\">\u03a0\u03b7\u03b3\u03ae: arXiv 2608.21614 v1, \u03b5\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 III \u03ba\u03b1\u03b9 V.<\/p>\n<\/div>\n<h2 id=\"entopismos-orion-stadiou\">\u03a0\u03ce\u03c2 \u03b5\u03bd\u03c4\u03bf\u03c0\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03c9\u03bd \u03c3\u03c4\u03b1\u03b4\u03af\u03c9\u03bd<\/h2>\n<p>\u03a4\u03bf SAEM \u03b1\u03c0\u03bf\u03c6\u03b5\u03cd\u03b3\u03b5\u03b9 \u03ad\u03bd\u03b1\u03bd \u03bd\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03cc classifier \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03bc\u03bf\u03bd\u03bf\u03c0\u03ac\u03c4\u03b9 \u03c4\u03b7\u03c2 \u03b1\u03c0\u03bf\u03ba\u03c9\u03b4\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2. \u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03b5\u03bb\u03b1\u03c6\u03c1\u03cd pattern matching \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03bb\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac \u03c3\u03ae\u03bc\u03b1\u03c4\u03b1 \u03bc\u03b5\u03c4\u03ac\u03b2\u03b1\u03c3\u03b7\u03c2, \u03cc\u03c0\u03c9\u03c2 \u03c6\u03c1\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03b4\u03b7\u03bb\u03ce\u03bd\u03bf\u03c5\u03bd \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae, \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c0\u03bf\u03c1\u03b5\u03af\u03b1\u03c2 \u03ae \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c3\u03ba\u03ad\u03c8\u03b7. \u0388\u03bd\u03b1 sliding window \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03c4\u03b1 \u03c0\u03c1\u03cc\u03c3\u03c6\u03b1\u03c4\u03b1 tokens \u03ba\u03b1\u03b9 \u03bc\u03b9\u03b1 finite-state machine \u03b1\u03bd\u03b1\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 cues \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b5\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 subword tokens.<\/p>\n<p>\u03a4\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c3\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03af\u03b4\u03b9\u03bf \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 checkpoint. \u039f\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03be\u03b5\u03ba\u03b9\u03bd\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc \u03bb\u03b5\u03be\u03b9\u03bb\u03cc\u03b3\u03b9\u03bf \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd offline \u03bc\u03b5 calibration traces. \u0391\u03c6\u03b1\u03b9\u03c1\u03bf\u03cd\u03bd markers \u03c0\u03bf\u03c5 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c0\u03ac\u03bd\u03b9\u03b1, \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03bd \u03b8\u03bf\u03c1\u03c5\u03b2\u03ce\u03b4\u03b7 \u03c4\u03bc\u03b7\u03bc\u03b1\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03ae \u03b4\u03b5\u03bd \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03c4\u03b7 \u03c3\u03c5\u03bd\u03bf\u03c7\u03ae \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03b4\u03bf\u03c7\u03b9\u03ba\u03ac \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1.<\/p>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b5\u03c3\u03c4\u03b9\u03ac\u03b6\u03b5\u03b9 \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03c3\u03b5 \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03ba\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ad\u03c2 reasoning. \u0394\u03b5\u03bd \u03b9\u03c3\u03c7\u03c5\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b1\u03bd\u03b1\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03af\u03b4\u03bf\u03c2 \u03c3\u03b7\u03bc\u03b1\u03c3\u03b9\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae\u03c2 \u03bc\u03b5\u03c4\u03ac\u03b2\u03b1\u03c3\u03b7\u03c2. \u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b1 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ba\u03c1\u03c5\u03c6\u03ac \u03ae \u03b1\u03c3\u03c4\u03b1\u03b8\u03ae traces, \u03cc\u03c0\u03c9\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c1\u03af\u03c3\u03ba\u03bf \u03c4\u03bf\u03c5 <a href=\"https:\/\/twodots.gr\/otan-to-ai-peithei-ton-elegkti-tou-chain-of-thought-monitoring\/\">chain-of-thought monitoring<\/a>.<\/p>\n<p>\u038c\u03c4\u03b1\u03bd \u03b5\u03bd\u03c4\u03bf\u03c0\u03b9\u03c3\u03c4\u03b5\u03af \u03cc\u03c1\u03b9\u03bf, \u03c4\u03bf runtime \u03c3\u03c5\u03bd\u03bf\u03c8\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03ba\u03ac\u03b8\u03b5 expert \u03ba\u03b1\u03b9 \u03ba\u03ac\u03b8\u03b5 layer \u03c3\u03c4\u03bf \u03c3\u03c4\u03ac\u03b4\u03b9\u03bf \u03c0\u03bf\u03c5 \u03bf\u03bb\u03bf\u03ba\u03bb\u03b7\u03c1\u03ce\u03b8\u03b7\u03ba\u03b5. \u0397 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03b1\u03c5\u03c4\u03ae \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 predictive proxy \u03b3\u03b9\u03b1 \u03c4\u03bf \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf \u03c3\u03c4\u03ac\u03b4\u03b9\u03bf. \u039f \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03ad\u03bb\u03b5\u03b9\u03b1 \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03b1\u03bd\u03cc\u03b7\u03c3\u03b7, \u03b1\u03bb\u03bb\u03ac \u03ad\u03bd\u03b1 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf trigger \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03b7 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae residency.<\/p>\n<h2 id=\"stage-aware-cache-prefetch\">Stage-aware cache \u03ba\u03b1\u03b9 prefetch<\/h2>\n<p>\u039a\u03b1\u03c4\u03ac \u03c4\u03b7\u03bd \u03b5\u03ba\u03ba\u03af\u03bd\u03b7\u03c3\u03b7, \u03c4\u03b1 \u03bc\u03b7-MoE \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1, \u03cc\u03c0\u03c9\u03c2 attention \u03ba\u03b1\u03b9 normalization, \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c4\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 GPU. \u0393\u03b9\u03b1 \u03c4\u03bf\u03c5\u03c2 experts \u03b4\u03b5\u03c3\u03bc\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2 \u03b1\u03bd\u03ac layer, \u03bc\u03b5 \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03ba\u03b1\u03b8\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf Expert Cache Ratio. \u0397 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b9\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf\u03bd \u03ba\u03b1\u03c4\u03b1\u03ba\u03b5\u03c1\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03cc.<\/p>\n<p>\u03a4\u03bf SAEM \u03ba\u03c1\u03b1\u03c4\u03ac activation log \u03b3\u03b9\u03b1 \u03c4\u03bf \u03c4\u03c1\u03ad\u03c7\u03bf\u03bd \u03c3\u03c4\u03ac\u03b4\u03b9\u03bf \u03ba\u03b1\u03b9 residency map \u03b3\u03b9\u03b1 \u03c4\u03bf\u03c5\u03c2 experts \u03c0\u03bf\u03c5 \u03b2\u03c1\u03af\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 GPU. \u039c\u03cc\u03bd\u03bf \u03c3\u03c4\u03b1 \u03b1\u03bd\u03b9\u03c7\u03bd\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 stage boundaries \u03ba\u03b1\u03c4\u03b1\u03c4\u03ac\u03c3\u03c3\u03b5\u03b9 \u03c4\u03bf\u03c5\u03c2 experts \u03ba\u03b1\u03b9 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03c4\u03bf\u03c5\u03c2 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf\u03c5\u03c2. \u0397 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf token-level LRU, \u03c4\u03bf \u03bf\u03c0\u03bf\u03af\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af cache churn \u03cc\u03c4\u03b1\u03bd \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b5\u03c2 \u03b6\u03b7\u03c4\u03bf\u03cd\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03c3\u03cd\u03bd\u03bf\u03bb\u03b1 experts.<\/p>\n<p>\u039f\u03b9 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03b1\u03c3\u03cd\u03b3\u03c7\u03c1\u03bf\u03bd\u03bf DMA \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03c4\u03bf\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b5 GPU events, \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf prefetch \u03bd\u03b1 \u03b5\u03c0\u03b9\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03b5\u03bd\u03b5\u03c1\u03b3\u03cc \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc. \u0397 \u03bc\u03b5\u03c4\u03b1\u03ba\u03af\u03bd\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03be\u03b1\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9\u00b7 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03cc\u03c0\u03bf\u03c5 \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf stage-level \u03c3\u03ae\u03bc\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bd\u03b1 \u03ba\u03c1\u03cd\u03c8\u03b5\u03b9 \u03c4\u03bf latency.<\/p>\n<p>\u0397 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b1\u03c5\u03c4\u03ae \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9, \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac, \u03ac\u03bb\u03bb\u03b5\u03c2 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae\u03c2. \u0397 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c4\u03bf\u03c5 <a href=\"https:\/\/twodots.gr\/moonep-exisorropisi-ai-experts-moe\/\">MoonEP \u03b3\u03b9\u03b1 \u03b5\u03be\u03b9\u03c3\u03bf\u03c1\u03c1\u03cc\u03c0\u03b7\u03c3\u03b7 experts<\/a> \u03b1\u03c6\u03bf\u03c1\u03ac \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc bottleneck, \u03b1\u03bb\u03bb\u03ac \u03c5\u03c0\u03b5\u03bd\u03b8\u03c5\u03bc\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9 routing, placement \u03ba\u03b1\u03b9 communication \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03b5\u03bd\u03b9\u03b1\u03af\u03b1 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae.<\/p>\n<div class=\"td-comparison td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid-three\">\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">Token-level LRU<\/p>\n<p>\u0391\u03bd\u03b1\u03bd\u03b5\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7 GPU cache \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03c3\u03c6\u03b1\u03c4\u03b7 \u03c7\u03c1\u03ae\u03c3\u03b7. \u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b5\u03c4\u03c5\u03c7\u03b1\u03af\u03bd\u03b5\u03b9 \u03c5\u03c8\u03b7\u03bb\u03cc hit rate \u03c3\u03b5 \u03bc\u03b9\u03ba\u03c1\u03cc batch, \u03b1\u03bb\u03bb\u03ac \u03c0\u03c1\u03bf\u03ba\u03b1\u03bb\u03b5\u03af \u03c3\u03c5\u03c7\u03bd\u03ad\u03c2 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 \u03cc\u03c4\u03b1\u03bd \u03bf\u03b9 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b5\u03c2 \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b8\u03ad\u03c3\u03b5\u03b9\u03c2.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Fine-grained<\/span><span class=\"td-badge\">Cache churn<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">Stage-aware cache<\/p>\n<p>\u0395\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7 residency \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae \u03c3\u03c4\u03b1 \u03cc\u03c1\u03b9\u03b1 reasoning \u03ba\u03b1\u03b9 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf activation profile \u03c4\u03bf\u03c5 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf\u03c5 \u03c3\u03c4\u03b1\u03b4\u03af\u03bf\u03c5 \u03c9\u03c2 proxy \u03b3\u03b9\u03b1 \u03c4\u03bf \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Stage signal<\/span><span class=\"td-badge\">Predictive placement<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<p class=\"td-comparison-title\">\u03a0\u03bb\u03ae\u03c1\u03b5\u03c2 SAEM<\/p>\n<p>\u03a3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 stage-aware cache, asynchronous prefetch, selective CPU execution \u03ba\u03b1\u03b9 token repacking. \u03a4\u03bf ablation \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03b4\u03b5\u03bd \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03b5\u03b9 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc \u03bc\u03cc\u03bd\u03bf.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">CPU\u2013GPU<\/span><span class=\"td-badge\">Coordinated runtime<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"cpu-energos-rolos\">\u0397 CPU \u03c9\u03c2 \u03b5\u03bd\u03b5\u03c1\u03b3\u03cc \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 inference<\/h2>\n<p>\u038c\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1\u03c2 \u03b6\u03b7\u03c4\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf\u03c2 expert \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 resident \u03c3\u03c4\u03b7 GPU, \u03c4\u03bf SAEM \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03b4\u03cd\u03bf \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ad\u03c2: \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf\u03c5 expert \u03c3\u03c4\u03b7 GPU \u03ae \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03ae \u03c4\u03bf\u03c5 \u03b1\u03c0\u03b5\u03c5\u03b8\u03b5\u03af\u03b1\u03c2 \u03c3\u03c4\u03b7 CPU. \u0393\u03b9\u03b1 \u03bb\u03af\u03b3\u03b1 tokens, \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03b7\u03c2 PCIe \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03c4\u03b7\u03c2 GPU. \u038c\u03c4\u03b1\u03bd \u03c4\u03b1 tokens \u03b1\u03c5\u03be\u03ac\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9, \u03b7 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b1\u03c0\u03bf\u03c3\u03b2\u03ad\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03b7 GPU \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b5\u03bb\u03ba\u03c5\u03c3\u03c4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b7.<\/p>\n<p>\u0397 CPU-side \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ae \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af optimized GEMM backends \u03cc\u03c0\u03c9\u03c2 Intel MKL \u03ae OpenBLAS \u03ba\u03b1\u03b9 \u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03c5\u03c0\u03cc\u03c8\u03b7 \u03c4\u03b7 NUMA \u03c4\u03bf\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03b1. Threads, expert weights \u03ba\u03b1\u03b9 activation buffers \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c4\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf socket, \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c4\u03bf remote memory traffic. \u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 CPU \u03ba\u03b1\u03b9 GPU \u03c3\u03c5\u03b3\u03c7\u03c9\u03bd\u03b5\u03cd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf layer.<\/p>\n<p>\u0397 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03b1\u03be\u03af\u03b1: \u03b7 \u03b5\u03c4\u03b5\u03c1\u03bf\u03b3\u03b5\u03bd\u03ae\u03c2 \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03b7. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae \u03c0\u03bf\u03c5 \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03b6\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03bc\u03ad\u03bd\u03b5\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac, \u03c0\u03bf\u03b9\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03bf\u03b9 experts \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd \u03c4\u03b7 \u03c3\u03c0\u03ac\u03bd\u03b9\u03b1 GPU \u03bc\u03bd\u03ae\u03bc\u03b7. \u0397 \u03af\u03b4\u03b9\u03b1 \u03b1\u03bd\u03ac\u03b3\u03ba\u03b7 \u03b3\u03b9\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload validation \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 <a href=\"https:\/\/twodots.gr\/llm4llm-gpu-kernel-dokimi-pragmatiko-montelo\/\">GPU kernel \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf<\/a> \u03b1\u03bd\u03c4\u03af \u03c3\u03b5 \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf microbenchmark.<\/p>\n<h2 id=\"token-repacking\">Token repacking \u03ba\u03b1\u03b9 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 fragmented kernels<\/h2>\n<p>\u0397 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03b7 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03c0\u03b7\u03b3\u03ae \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b4\u03b9\u03ac\u03c3\u03c0\u03b1\u03c1\u03c4\u03b7 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7 \u03c3\u03c4\u03b1 tokens. \u038c\u03c4\u03b1\u03bd \u03ba\u03ac\u03b8\u03b5 expert \u03c3\u03c5\u03bb\u03bb\u03ad\u03b3\u03b5\u03b9 \u03bc\u03b9\u03ba\u03c1\u03ac, \u03bc\u03b7 \u03c3\u03c5\u03bd\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1 \u03ba\u03bf\u03bc\u03bc\u03ac\u03c4\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, \u03b1\u03c5\u03be\u03ac\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bf\u03b9 \u03bc\u03b5\u03c4\u03b1\u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af layout \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03bc\u03b9\u03ba\u03c1\u03ad\u03c2 kernel launches. \u03a3\u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2, kernel launches, routing metadata \u03ba\u03b1\u03b9 layout transformations \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf\u03cd\u03bd \u03ba\u03b1\u03c4\u03ac \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf \u03c3\u03c4\u03bf 54,2% \u03c4\u03bf\u03c5 \u03b5\u03be\u03b5\u03c4\u03b1\u03b6\u03cc\u03bc\u03b5\u03bd\u03bf\u03c5 \u03c7\u03c1\u03cc\u03bd\u03bf\u03c5 \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7\u03c2.<\/p>\n<p>\u03a4\u03bf SAEM \u03bf\u03bc\u03b1\u03b4\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03b9\u03c2 \u03b1\u03bd\u03b1\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 token\u2013expert \u03c3\u03b5 \u03c3\u03c5\u03bd\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1 buffers. \u039a\u03ac\u03b8\u03b5 expert \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ad\u03c4\u03c3\u03b9 \u03c0\u03c5\u03ba\u03bd\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c0\u03b1\u03c1\u03c4\u03af\u03b4\u03b1 \u03bc\u03b5 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03ba\u03bb\u03ae\u03c3\u03b5\u03b9\u03c2 kernel. \u039c\u03b5 \u03c4\u03bf token repacking, \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf overhead \u03c0\u03ad\u03c6\u03c4\u03b5\u03b9 \u03c3\u03c4\u03bf 40,0%. \u0397 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03b1\u03c0\u03bf\u03b4\u03af\u03b4\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 4,7 \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03b9\u03b1\u03af\u03b5\u03c2 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b5\u03c2 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf token access\/layout transformation \u03ba\u03b1\u03b9 6,7 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b5\u03c2 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf kernel-launch overhead.<\/p>\n<p>\u039f \u03c0\u03c1\u03bf\u03c3\u03c9\u03c1\u03b9\u03bd\u03cc\u03c2 \u03c7\u03ce\u03c1\u03bf\u03c2 \u03b4\u03b5\u03c3\u03bc\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03af\u03b1 \u03c6\u03bf\u03c1\u03ac \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b1\u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03cc\u03bb\u03b1 \u03c4\u03b1 MoE layers. \u03a3\u03b5 \u03b1\u03bd\u03c4\u03af\u03b8\u03b5\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03b7 stage-aware cache, \u03c4\u03bf repacking \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 layer \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bc\u03ad\u03bd\u03b5\u03b9 \u03cc\u03c1\u03b9\u03bf reasoning. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03b7 \u03c0\u03c1\u03cc\u03c4\u03b1\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03bd\u03c4\u03bf\u03bd\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf runtime \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03af\u03b1 \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b5\u03c5\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae.<\/p>\n<h2 id=\"apotelesmata-saem\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03bf\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2<\/h2>\n<p>\u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03ad\u03b3\u03b9\u03bd\u03b5 \u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd NVIDIA A100 \u03bc\u03b5 80 GB HBM2e, \u03ad\u03bd\u03b1\u03bd Intel Xeon Gold 6326 \u03bc\u03b5 16 cores \u03ba\u03b1\u03b9 512 GB DDR4. \u0397 \u03c3\u03cd\u03bd\u03b4\u03b5\u03c3\u03b7 CPU\u2013GPU \u03ae\u03c4\u03b1\u03bd PCIe 4.0 \u00d716. \u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd Qwen3-30B-A3B \u03ba\u03b1\u03b9 ERNIE-4.5-21B-A3B-Thinking, batch sizes \u03b1\u03c0\u03cc 1 \u03ad\u03c9\u03c2 8 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac Expert Cache Ratios. \u0397 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03bc\u03b5\u03c4\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c9\u03c2 end-to-end tokens \u03b1\u03bd\u03ac \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf.<\/p>\n<p>\u03a3\u03b5 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03b5 MoE-OnDemand, Mixtral-Offloading, Fiddler \u03ba\u03b1\u03b9 DAOP, \u03c4\u03bf SAEM \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03bc\u03ad\u03c3\u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc throughput gain 1,33\u00d7. \u03a3\u03c4\u03bf MATH-500 \u03bf\u03b9 \u03bc\u03ad\u03c3\u03b5\u03c2 \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03c3\u03b5\u03b9\u03c2 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03bf\u03c5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 baseline \u03ae\u03c4\u03b1\u03bd 1,60\u00d7 \u03b3\u03b9\u03b1 \u03c4\u03bf Qwen3 \u03ba\u03b1\u03b9 1,47\u00d7 \u03b3\u03b9\u03b1 \u03c4\u03bf ERNIE-4.5. \u03a3\u03c4\u03bf AIME 2024 \u03ae\u03c4\u03b1\u03bd 1,20\u00d7 \u03ba\u03b1\u03b9 1,21\u00d7, \u03b5\u03bd\u03ce \u03c3\u03c4\u03bf GPQA-Diamond 1,14\u00d7 \u03ba\u03b1\u03b9 1,34\u00d7.<\/p>\n<p>\u0397 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac calibration \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c5\u03c3\u03b9\u03ce\u03b4\u03b7\u03c2. \u03a4\u03bf MATH-500 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c4\u03cc\u03c3\u03bf \u03b3\u03b9\u03b1 \u03c4\u03bf \u03b1\u03c1\u03c7\u03b9\u03ba\u03cc hot set \u03cc\u03c3\u03bf \u03ba\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c4\u03bf model-specific transition vocabulary. \u03a4\u03b1 AIME 2024 \u03ba\u03b1\u03b9 GPQA-Diamond \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03b9\u03bd\u03b1\u03bd held out \u03ba\u03b1\u03b9 \u03ad\u03b4\u03c9\u03c3\u03b1\u03bd \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 \u03b1\u03bb\u03bb\u03ac \u03b8\u03b5\u03c4\u03b9\u03ba\u03ac gains. \u0397 \u03bc\u03ad\u03c3\u03b7 \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 1,54\u00d7 \u03c3\u03b5 matched \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7.<\/p>\n<aside class=\"td-article-note\"><strong>\u03a4\u03bf benchmark \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf deployment:<\/strong> \u03bf\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03bc\u03b5\u03c4\u03c1\u03bf\u03cd\u03bd throughput \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf hardware \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 reasoning datasets. \u0394\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03c5\u03bd \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc cloud TCO, \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1, \u03bf\u03c5\u03c1\u03ad\u03c2 \u03b1\u03b9\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd, SLA tail latency, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 engineering \u03ae \u03bc\u03b5\u03c4\u03b1\u03b2\u03bf\u03bb\u03ad\u03c2 \u03c3\u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae\u03c2 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae\u03c2.<\/aside>\n<p>\u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae dataset, cache ratio, batch size \u03ba\u03b1\u03b9 baseline \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd. \u038c\u03c0\u03c9\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03c0\u03ce\u03c2 <a href=\"https:\/\/twodots.gr\/ai-benchmark-harness-allazei-nikiti\/\">\u03c4\u03bf benchmark harness \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf\u03bd \u03c6\u03b1\u03b9\u03bd\u03bf\u03bc\u03b5\u03bd\u03b9\u03ba\u03cc \u03bd\u03b9\u03ba\u03b7\u03c4\u03ae<\/a>, \u03ad\u03bd\u03b1 headline speedup \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf.<\/p>\n<h2 id=\"concurrency-ablation-oracle\">Concurrency, ablation \u03ba\u03b1\u03b9 oracle<\/h2>\n<p>\u039c\u03b5 batch size 1 \u03ba\u03b1\u03b9 50% ECR \u03c3\u03c4\u03bf Qwen3, \u03c4\u03bf SAEM \u03b5\u03af\u03c7\u03b5 90,08% cache hit rate \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 94,53% \u03c4\u03bf\u03c5 Mixtral-Offloading, \u03b1\u03bb\u03bb\u03ac \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 1,62\u00d7 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf throughput. \u03a4\u03bf hit rate \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03c4\u03b7\u03bd \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03bf token repacking \u03ba\u03b1\u03b9 \u03b7 \u03b5\u03c0\u03b9\u03bb\u03b5\u03ba\u03c4\u03b9\u03ba\u03ae CPU execution \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03bf\u03c5\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1 \u03c4\u03bf\u03c5 latency.<\/p>\n<p>\u03a3\u03b5 batch size 8 \u03ba\u03b1\u03b9 3,125% ECR, \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 187,95% \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ae \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 cache hit ratio \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03bf\u03c5 Mixtral-Offloading \u03ba\u03b1\u03b9 2,10\u00d7 throughput gain. \u0397 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c3\u03c5\u03c7\u03bd\u03ae LRU migration \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 \u03cc\u03c4\u03b1\u03bd \u03b5\u03be\u03c5\u03c0\u03b7\u03c1\u03b5\u03c4\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b5\u03c2, \u03b5\u03bd\u03ce \u03b7 stage-aware \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae \u03ba\u03c1\u03b1\u03c4\u03ac experts \u03c0\u03bf\u03c5 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf \u03bd\u03b1 \u03b5\u03c0\u03b1\u03bd\u03b1\u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03bf\u03cd\u03bd.<\/p>\n<p>\u03a4\u03bf ablation \u03c3\u03c4\u03bf Qwen3 \u03bc\u03b5 12,5% ECR \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1. \u0393\u03b9\u03b1 batch 1, \u03b7 stage-aware cache \u03bc\u03cc\u03bd\u03b7 \u03c4\u03b7\u03c2 \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 1,39\u00d7, \u03c4\u03bf token repacker 1,46\u00d7 \u03ba\u03b1\u03b9 \u03b7 in-situ CPU execution \u03bc\u03cc\u03bd\u03b7 \u03c4\u03b7\u03c2 0,79\u00d7. \u038c\u03bb\u03b1 \u03bc\u03b1\u03b6\u03af \u03ad\u03c6\u03c4\u03b1\u03c3\u03b1\u03bd 1,77\u00d7. \u0393\u03b9\u03b1 batch 8, \u03bf\u03b9 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03ae\u03c4\u03b1\u03bd 1,23\u00d7, 1,07\u00d7, 1,00\u00d7 \u03ba\u03b1\u03b9 1,62\u00d7.<\/p>\n<p>\u03a3\u03b5 replay \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03b5 oracle \u03c0\u03bf\u03c5 \u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc activation profile \u03c4\u03bf\u03c5 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf\u03c5 \u03c3\u03c4\u03b1\u03b4\u03af\u03bf\u03c5, \u03b7 throughput efficiency \u03c4\u03bf\u03c5 SAEM \u03ba\u03c5\u03bc\u03ac\u03bd\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 87,87% \u03ad\u03c9\u03c2 92,80% \u03b3\u03b9\u03b1 batch 1 \u03ba\u03b1\u03b9 \u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc 95% \u03b3\u03b9\u03b1 batch 8. \u0397 \u03c4\u03b9\u03bc\u03ae 100,78% \u03c3\u03b5 \u03bc\u03af\u03b1 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf\u03c5\u03c2 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c9\u03c2 \u03b9\u03c3\u03bf\u03b4\u03c5\u03bd\u03b1\u03bc\u03af\u03b1 \u03bb\u03cc\u03b3\u03c9 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7\u03c2 \u03c4\u03bf\u03c5 1%, \u03cc\u03c7\u03b9 \u03c9\u03c2 \u03c5\u03c0\u03b5\u03c1\u03bf\u03c7\u03ae \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03ad\u03bb\u03b5\u03b9\u03b1\u03c2 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7\u03c2.<\/p>\n<h2 id=\"ai-proionta-kostos\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 AI \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/h2>\n<p>\u0393\u03b9\u03b1 \u03ad\u03bd\u03b1 e-commerce, support \u03ae marketing \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c3\u03cd\u03bd\u03b8\u03b5\u03c4\u03bf reasoning, \u03c4\u03bf SAEM \u03b4\u03b5\u03bd \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03b9 \u03b1\u03c0\u03cc \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03c5 \u03c4\u03bf \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03c4\u03b7\u03c2 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2. \u0391\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf serving layer: \u03c0\u03cc\u03c3\u03b5\u03c2 reasoning \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03bf\u03bb\u03bf\u03ba\u03bb\u03b7\u03c1\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bd\u03ac \u03bc\u03bf\u03bd\u03ac\u03b4\u03b1 \u03c7\u03c1\u03cc\u03bd\u03bf\u03c5 \u03cc\u03c4\u03b1\u03bd \u03b7 GPU \u03bc\u03bd\u03ae\u03bc\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b7. \u0391\u03c5\u03c4\u03cc \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03c3\u03b5\u03b9 capacity planning, latency budgets \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03cc\u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03ad\u03b1 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae.<\/p>\n<p>\u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 traces \u03c0\u03bf\u03c5 \u03bc\u03bf\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd \u03bc\u03b5 \u03c4\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac prompts. \u0391\u03bd \u03c4\u03bf workload \u03b1\u03c6\u03bf\u03c1\u03ac product research, \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bb\u03cc\u03b3\u03bf\u03c5 \u03ae \u03c3\u03cd\u03bd\u03b8\u03b5\u03c4\u03b7 \u03b5\u03be\u03c5\u03c0\u03b7\u03c1\u03ad\u03c4\u03b7\u03c3\u03b7, \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03b7 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03ac\u03c4\u03c9\u03bd \u03b1\u03c0\u03cc \u03bc\u03b1\u03b8\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac benchmarks. \u03a0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03b7\u03b8\u03bf\u03cd\u03bd transition cues, expert-activation coherence, batch concurrency, PCIe traffic \u03ba\u03b1\u03b9 p95 latency \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf hardware.<\/p>\n<p>\u0397 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 hardware \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c4\u03b9\u03bc\u03ae \u03b1\u03bd\u03ac GPU \u03ce\u03c1\u03b1. \u03a3\u03c4\u03b1 <a href=\"https:\/\/twodots.gr\/gpu-neoclouds-2026-ti-agorazeis-pera-apo-timi\/\">GPU neoclouds<\/a>, topology, \u03b4\u03b9\u03b1\u03b8\u03b5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1, networking \u03ba\u03b1\u03b9 observability \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\u03cc\u03c3\u03c4\u03bf\u03c2. \u0391\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1, \u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae runtime \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ae \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae checkpoint.<\/p>\n<p>\u03a4\u03bf SAEM \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b9\u03b4\u03ad\u03b1: \u03ad\u03bd\u03b1 AI runtime \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c7\u03b1\u03bc\u03b7\u03bb\u03bf\u03cd \u03b5\u03c0\u03b9\u03c0\u03ad\u03b4\u03bf\u03c5 recency counters. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03ce\u03c1\u03b9\u03bc\u03b5\u03c2 serving \u03c0\u03bb\u03b1\u03c4\u03c6\u03cc\u03c1\u03bc\u03b5\u03c2 \u03cc\u03c0\u03c9\u03c2 \u03b7 \u03b5\u03bd\u03c3\u03c9\u03bc\u03ac\u03c4\u03c9\u03c3\u03b7 <a href=\"https:\/\/twodots.gr\/vllm-transformers-native-speed-ai-inference\/\">vLLM \u03ba\u03b1\u03b9 Transformers \u03b3\u03b9\u03b1 native-speed inference<\/a>\u00b7 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03b3\u03b9\u03b1 semantics-aware resource management.<\/p>\n<h2 id=\"production-elegxoi\">\u03a4\u03b9 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03b7\u03b8\u03b5\u03af \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc production<\/h2>\n<p>\u0388\u03bd\u03b1 pilot \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9 \u03bc\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc baseline \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf hardware, \u03af\u03b4\u03b9\u03b1 prompts, \u03af\u03b4\u03b9\u03bf decoding budget \u03ba\u03b1\u03b9 \u03af\u03b4\u03b9\u03b5\u03c2 \u03b1\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 throughput, p50 \u03ba\u03b1\u03b9 p95 latency, GPU HBM, CPU RAM, PCIe traffic, cache hit rate, CPU utilization, tokens \u03b1\u03bd\u03ac expert \u03ba\u03b1\u03b9 stage-detection overhead.<\/p>\n<p>\u03a4\u03bf calibration set \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03b1\u03c0\u03cc \u03c4\u03bf \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc test set \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03bf\u03c0\u03c4\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03bc\u03b5\u03af\u03b3\u03bc\u03b1 \u03b5\u03c1\u03b3\u03b1\u03c3\u03b9\u03ce\u03bd. \u03a7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b1\u03bd\u03ac workload class, \u03bc\u03ae\u03ba\u03bf\u03c2 trace, batch concurrency \u03ba\u03b1\u03b9 cue density. \u0388\u03bd\u03b1\u03c2 \u03b5\u03bd\u03b9\u03b1\u03af\u03bf\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2 \u03cc\u03c1\u03bf\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03c1\u03cd\u03c8\u03b5\u03b9 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 \u03cc\u03c0\u03bf\u03c5 \u03c4\u03bf stage detector \u03b1\u03c0\u03bf\u03c4\u03c5\u03b3\u03c7\u03ac\u03bd\u03b5\u03b9 \u03ae \u03cc\u03c0\u03bf\u03c5 \u03b7 CPU \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ae \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 bottleneck.<\/p>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae\u03c2, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf tokens\/s. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af runtime throughput \u03ba\u03b1\u03b9 \u03ba\u03c1\u03b1\u03c4\u03ac \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03c4\u03b9\u03c2 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b5\u03c2 \u03cc\u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9, \u03b1\u03bb\u03bb\u03ac \u03ad\u03bd\u03b1 production \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03b9\u03ce\u03c3\u03b5\u03b9 answer quality, tool correctness, failure recovery \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03c3\u03b5 model version \u03ae prompting style.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-kicker\">\u0391\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc pilot<\/p>\n<p class=\"td-decision-title\">\u039c\u03b7\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03c4\u03b5 infrastructure \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03bf 1,33\u00d7<\/p>\n<p>\u03a0\u03c1\u03bf\u03c7\u03c9\u03c1\u03ae\u03c3\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 held-out workload \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03b9 stage-level coherence, \u03c4\u03bf gain \u03b5\u03c0\u03b9\u03b2\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac batch patterns \u03ba\u03b1\u03b9 \u03b7 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ae \u03b5\u03be\u03bf\u03b9\u03ba\u03bf\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf calibration, \u03c4\u03bf CPU load, \u03c4\u03bf engineering overhead \u03ba\u03b1\u03b9 \u03c4\u03b1 quality gates.<\/p>\n<\/div>\n<h2 id=\"epta-vimata-pilot\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 pilot<\/h2>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0391\u03c0\u03cc \u03c4\u03b1 reasoning traces \u03c3\u03b5 \u03b5\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03bf stage-aware inference<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 1<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03bf production workload<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 use cases, prompt classes, concurrency, \u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03bf output \u03ba\u03b1\u03b9 \u03b1\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u039c\u03b7\u03bd \u03b2\u03b1\u03b8\u03bc\u03bf\u03bd\u03bf\u03bc\u03b5\u03af\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03b5\u03cd\u03ba\u03bf\u03bb\u03bf \u03ae \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ac \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03bf dataset.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 2<\/span><strong>\u039a\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc baseline<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03c1\u03b9\u03bd\u03cc caching \u03ba\u03b1\u03b9 offloading stack \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf model, hardware \u03ba\u03b1\u03b9 decoding setup \u03c0\u03c1\u03b9\u03bd \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c3\u03b5\u03c4\u03b5 stage-aware \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 3<\/span><strong>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03c4\u03b1 stage signals<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 cue frequency, false boundaries, \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03c7\u03c9\u03c1\u03af\u03c2 \u03c1\u03b7\u03c4\u03ac markers \u03ba\u03b1\u03b9 temporal coherence \u03b1\u03bd\u03ac workload class. \u0391\u03bd \u03c4\u03bf \u03c3\u03ae\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9, \u03c4\u03bf placement proxy \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03bf.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 4<\/span><strong>\u0394\u03b9\u03b1\u03c7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 calibration \u03ba\u03b1\u03b9 test<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03c5\u03c4\u03b9\u03ba\u03ac traces \u03b3\u03b9\u03b1 \u03c4\u03bf hot set \u03ba\u03b1\u03b9 \u03c4\u03b1 transition patterns, \u03b1\u03bb\u03bb\u03ac \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03bf held-out \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 5<\/span><strong>\u0391\u03c0\u03bf\u03bc\u03bf\u03bd\u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2<\/strong>\n<p>\u03a4\u03c1\u03ad\u03be\u03c4\u03b5 cache-only, repacking-only, CPU-only \u03ba\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 SAEM-style configuration. \u0388\u03c4\u03c3\u03b9 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c0\u03bf\u03b9\u03bf bottleneck \u03bc\u03b5\u03c4\u03b1\u03ba\u03b9\u03bd\u03b5\u03af\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03b1\u03bd \u03b7 \u03c3\u03cd\u03bd\u03b8\u03b5\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 6<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1<\/strong>\n<p>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 tokens\/s, tail latency, transfers, \u03bc\u03bd\u03ae\u03bc\u03b7, \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1 \u03ba\u03b1\u03b9 answer quality. \u0388\u03bd\u03b1 \u03b3\u03c1\u03b7\u03b3\u03bf\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf runtime \u03c0\u03bf\u03c5 \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 failures \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 7<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 release gate \u03ba\u03b1\u03b9 fallback<\/strong>\n<p>\u0398\u03ad\u03c3\u03c4\u03b5 \u03b5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf gain, \u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03b7 \u03c5\u03c0\u03bf\u03b2\u03ac\u03b8\u03bc\u03b9\u03c3\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ba\u03b1\u03b9 trigger \u03b5\u03c0\u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac\u03c2. \u039a\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf inference path \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 checkpoint \u03ae workload mix.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<h2 id=\"oria-symperasma\">\u038c\u03c1\u03b9\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1<\/h2>\n<p>\u03a4\u03bf SAEM \u03b1\u03bd\u03b9\u03c7\u03bd\u03b5\u03cd\u03b5\u03b9 stage boundaries \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03b1\u03c0\u03cc \u03c1\u03b7\u03c4\u03ac \u03bb\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac cues. Traces \u03c7\u03c9\u03c1\u03af\u03c2 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ad\u03c2 \u03bc\u03b5\u03c4\u03b1\u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ae \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03cd\u03c6\u03bf\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03ce\u03c3\u03bf\u03c5\u03bd \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae \u03c4\u03bc\u03b7\u03bc\u03b1\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03c9\u03c2 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03b5\u03cd\u03b8\u03c5\u03bd\u03c3\u03b7 \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b5\u03bd\u03c4\u03c1\u03bf\u03c0\u03af\u03b1 \u03c4\u03c9\u03bd expert activations, \u03b1\u03bb\u03bb\u03ac \u03b1\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c5\u03bb\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03c0\u03b1\u03c1\u03bf\u03cd\u03c3\u03b1\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2.<\/p>\n<p>\u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b4\u03cd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03c4\u03c1\u03af\u03b1 reasoning benchmarks, batch sizes \u03ad\u03c9\u03c2 8 \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03ba\u03cc\u03bc\u03b2\u03bf A100\/Xeon. \u03a4\u03bf MATH-500 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b3\u03b9\u03b1 calibration, \u03b5\u03bd\u03ce \u03b7 \u03ba\u03bb\u03b9\u03bc\u03ac\u03ba\u03c9\u03c3\u03b7 \u03c3\u03b5 \u03c0\u03bf\u03bb\u03cd \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 contexts \u03bc\u03ad\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ae \u03ad\u03c1\u03b5\u03c5\u03bd\u03b1. \u0395\u03c0\u03af\u03c3\u03b7\u03c2, \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b5\u03bd \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03cd\u03b5\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 cloud TCO, \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1\u03c2 \u03ae SLA \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac\u03c2.<\/p>\n<p>\u03a4\u03bf \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 \u03b1\u03bb\u03bb\u03ac \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf: \u03cc\u03c4\u03b1\u03bd \u03c4\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac traces \u03ad\u03c7\u03bf\u03c5\u03bd stage-level locality, \u03bf \u03c3\u03c5\u03bd\u03c4\u03bf\u03bd\u03b9\u03c3\u03bc\u03cc\u03c2 cache, prefetch, CPU execution \u03ba\u03b1\u03b9 token layout \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03ce\u03c3\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03b8\u03b5\u03c4\u03b9\u03ba\u03ae \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 \u03bc\u03cc\u03bd\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf\u03c5. \u0393\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 MLOps, \u03c4\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03b5\u03c1\u03ce\u03c4\u03b7\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u00ab\u03c0\u03bf\u03b9\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c7\u03c9\u03c1\u03ac \u03c3\u03c4\u03b7 GPU;\u00bb, \u03b1\u03bb\u03bb\u03ac \u00ab\u03c0\u03bf\u03b9\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03ba\u03b1\u03bb\u03ac \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c4\u03b7\u03b8\u03b5\u03af \u03c0\u03c1\u03b9\u03bd \u03b6\u03b7\u03c4\u03b7\u03b8\u03b5\u03af;\u00bb.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">AI workflows \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac production \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1<\/p>\n<p class=\"td-service-cta-title\">\u039c\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c8\u03c4\u03b5 \u03ad\u03bd\u03b1 inference pilot \u03c3\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7<\/p>\n<p>\u0397 TWO DOTS \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03b5\u03b9 AI \u03ba\u03b1\u03b9 automation \u03c1\u03bf\u03ad\u03c2 \u03bc\u03b5 \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03c5\u03c4\u03b9\u03ba\u03ac tests, observability, quality gates \u03ba\u03b1\u03b9 fallback, \u03ce\u03c3\u03c4\u03b5 latency, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b1\u03b6\u03af \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae.<\/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 \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03ce\u03bd \u03ba\u03b1\u03b9 AI<\/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 SAEM;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc runtime \u03b3\u03b9\u03b1 inference \u03c3\u03b5 Mixture-of-Experts \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03b1 \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03c4\u03bf\u03c5 reasoning \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03ba\u03b1\u03b8\u03bf\u03b4\u03b7\u03b3\u03b5\u03af expert caching, prefetching \u03ba\u03b1\u03b9 \u03c5\u03b2\u03c1\u03b9\u03b4\u03b9\u03ba\u03ae CPU\u2013GPU \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c0\u03b1\u03b8\u03b5\u03af \u03bd\u03b1 \u03bb\u03cd\u03c3\u03b5\u03b9;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03bf\u03c7\u03b5\u03cd\u03b5\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03ac\u03c3\u03ba\u03bf\u03c0\u03b5\u03c2 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 expert weights, \u03c3\u03c4\u03bf cache churn \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 fragmented kernels \u03cc\u03c4\u03b1\u03bd \u03cc\u03bb\u03b1 \u03c4\u03b1 \u03b2\u03ac\u03c1\u03b7 \u03b5\u03bd\u03cc\u03c2 MoE \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c7\u03c9\u03c1\u03bf\u03cd\u03bd \u03c3\u03c4\u03b7 GPU.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03ce\u03c2 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03b9 \u03c4\u03b1 reasoning stages;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af model-specific \u03bb\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac transition patterns \u03c3\u03b5 sliding window. \u03a4\u03b1 patterns \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03bf\u03bd\u03c4\u03b1\u03b9 offline \u03bc\u03b5 calibration traces \u03ba\u03b1\u03b9 \u03b5\u03bd\u03c4\u03bf\u03c0\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 online \u03bc\u03b5 \u03b5\u03bb\u03b1\u03c6\u03c1\u03cd finite-state matcher.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03cc\u03c3\u03bf \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf \u03ae\u03c4\u03b1\u03bd \u03c3\u03c4\u03b9\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03bc\u03ad\u03c3\u03bf throughput gain 1,33\u00d7 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03bf\u03c5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 baseline \u03c3\u03c4\u03b9\u03c2 \u03b5\u03be\u03b5\u03c4\u03b1\u03b6\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 1,54\u00d7 \u03cc\u03c4\u03b1\u03bd calibration dataset \u03ba\u03b1\u03b9 workload \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03b5\u03ba\u03c4\u03b5\u03bb\u03b5\u03af experts \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7 CPU;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0393\u03b9\u03b1 \u03bb\u03af\u03b3\u03b1 tokens, \u03b7 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b5\u03bd\u03cc\u03c2 \u03bc\u03b7 resident expert \u03bc\u03ad\u03c3\u03c9 PCIe \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03bf\u03c3\u03c4\u03af\u03b6\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae CPU \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7. \u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b4\u03c5\u03bd\u03b1\u03bc\u03b9\u03ba\u03ac \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03af\u03b4\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 \u03c3\u03c9\u03c3\u03c4\u03cc placement \u03ba\u03b1\u03b9 repacking.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf cache hit rate;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u03a3\u03b5 \u03bc\u03af\u03b1 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae \u03c4\u03bf SAEM \u03b5\u03af\u03c7\u03b5 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf hit rate \u03b1\u03c0\u03cc \u03c4\u03bf token-level LRU \u03b1\u03bb\u03bb\u03ac \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf throughput, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae repacking, transfer avoidance \u03ba\u03b1\u03b9 CPU\u2013GPU orchestration \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03bf\u03c5\u03bd \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03c4\u03bf end-to-end latency.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0395\u03af\u03bd\u03b1\u03b9 \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b5\u03bd\u03b9\u03ba\u03b5\u03cd\u03c3\u03b9\u03bc\u03b1 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 AI \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03ad\u03bf benchmark. \u039f\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd \u03b4\u03cd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03c4\u03c1\u03af\u03b1 reasoning datasets \u03ba\u03b1\u03b9 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf hardware. \u039a\u03ac\u03b8\u03b5 production workload \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b9\u03ba\u03cc \u03c4\u03bf\u03c5 calibration \u03ba\u03b1\u03b9 held-out test.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf \u03b2\u03ae\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039d\u03b1 \u03c4\u03c1\u03ad\u03be\u03b5\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc offline pilot \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac traces, \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc baseline, \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03bf test set, \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 throughput \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ad\u03c2 fallback \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03bf\u03c0\u03bf\u03b9\u03b1\u03b4\u03ae\u03c0\u03bf\u03c4\u03b5 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae\u03c2.<\/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:\/\/arxiv.org\/abs\/2608.21614\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 SAEM: Stage-Aware Expert Management for Memory-Efficient MoE Inference in Chain-of-Thought Reasoning<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/QwenLM\/Qwen3\" target=\"_blank\" rel=\"noopener\">QwenLM \u2014 \u0395\u03c0\u03af\u03c3\u03b7\u03bc\u03bf repository \u03ba\u03b1\u03b9 \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03af\u03c9\u03c3\u03b7 Qwen3<\/a><\/li>\n<li><a href=\"https:\/\/ernie.baidu.com\/blog\/publication\/ERNIE_Technical_Report.pdf\" target=\"_blank\" rel=\"noopener\">Baidu ERNIE \u2014 ERNIE 4.5 Technical Report<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/docs\/transformers\/model_doc\/qwen3\" target=\"_blank\" rel=\"noopener\">Hugging Face Transformers \u2014 \u03a4\u03b5\u03ba\u03bc\u03b7\u03c1\u03af\u03c9\u03c3\u03b7 Qwen3<\/a><\/li>\n<li><a href=\"https:\/\/airc.nist.gov\/airmf-resources\/airmf\/5-sec-core\/\" target=\"_blank\" rel=\"noopener\">NIST AIRC \u2014 AI Risk Management Framework Core<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf SAEM \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf MoE inference \u03b1\u03bd\u03ac reasoning stage \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2, cache churn \u03ba\u03b1\u03b9 fragmented kernels. \u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03ba\u03b1\u03b9 \u03c0\u03ce\u03c2 \u03b5\u03bb\u03ad\u03b3\u03c7\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 production.<\/p>","protected":false},"author":1,"featured_media":105069,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[8988,18338,20646,19427,20714],"class_list":["post-97894","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ai-infrastructure","tag-chain-of-thought","tag-gpu-optimization","tag-llm-inference","tag-mixture-of-experts-2"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/97894","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=97894"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/97894\/revisions"}],"predecessor-version":[{"id":105070,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/97894\/revisions\/105070"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media\/105069"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media?parent=97894"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/categories?post=97894"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/tags?post=97894"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}