{"id":97828,"date":"2026-09-20T14:02:34","date_gmt":"2026-09-20T11:02:34","guid":{"rendered":"https:\/\/twodots.gr\/?p=97828"},"modified":"2026-09-20T14:02:36","modified_gmt":"2026-09-20T11:02:36","slug":"llm4llm-gpu-kernel-dokimi-pragmatiko-montelo","status":"publish","type":"post","link":"https:\/\/twodots.gr\/bg\/llm4llm-gpu-kernel-dokimi-pragmatiko-montelo\/","title":{"rendered":"LLM4LLM: \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf GPU kernel \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \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"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u0391\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ce\u03c4\u03b1:<\/strong> \u03ad\u03bd\u03b1 GPU kernel \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03ba\u03ad\u03c1\u03b4\u03b9\u03c3\u03b5 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf benchmark. \u03a0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03b5\u03af\u03be\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c3\u03c9\u03c3\u03c4\u03cc, \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf \u03cc\u03c4\u03b1\u03bd \u03b5\u03bd\u03c3\u03c9\u03bc\u03b1\u03c4\u03c9\u03b8\u03b5\u03af \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc model instance, \u03bc\u03b5 \u03c4\u03b1 \u03b1\u03bb\u03b7\u03b8\u03b9\u03bd\u03ac shapes, \u03c4\u03bf cache state, \u03c4\u03b1 guards \u03ba\u03b1\u03b9 \u03c4\u03bf dispatch overhead \u03c4\u03bf\u03c5 workload.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b5\u03af \u03c4\u03bf LLM4LLM: \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03b1\u03c0\u03cc inference script, \u03b5\u03bd\u03c4\u03bf\u03c0\u03af\u03b6\u03b5\u03b9 \u03c4\u03b1 hotspots \u03c4\u03bf\u03c5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03bf\u03cd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5, \u03b1\u03bd\u03b1\u03b6\u03b7\u03c4\u03ac \u03c5\u03c0\u03bf\u03c8\u03ae\u03c6\u03b9\u03b1 Triton kernels \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 patch \u03bc\u03cc\u03bd\u03bf \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc in-model validation. \u03a3\u03c4\u03b1 \u03b4\u03ad\u03ba\u03b1 workloads \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2, \u03b7 \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03c4\u03bf end-to-end latency \u03c3\u03b5 A100 \u03ba\u03b1\u03b9 H100, \u03b1\u03bb\u03bb\u03ac \u03bf\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 single-GPU \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03ba\u03ac\u03b8\u03b5 production stack.<\/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=\"#benchmark-deployment-gap\">\u03a4\u03bf benchmark-to-deployment gap \u03c0\u03bf\u03c5 \u03ba\u03c1\u03cd\u03b2\u03b5\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/a><\/li>\n<li><a href=\"#treis-apotychies\">\u03a4\u03c1\u03b5\u03b9\u03c2 \u03c4\u03c1\u03cc\u03c0\u03bf\u03b9 \u03bc\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03bf\u03c0\u03bf\u03af\u03bf\u03c5\u03c2 \u03ad\u03bd\u03b1 \u00ab\u03c3\u03c9\u03c3\u03c4\u03cc\u00bb kernel \u03b1\u03c0\u03bf\u03c4\u03c5\u03b3\u03c7\u03ac\u03bd\u03b5\u03b9<\/a><\/li>\n<li><a href=\"#tessera-epipeda-elegchou\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03b1 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03b1\u03bd\u03c4\u03af \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1 score<\/a><\/li>\n<li><a href=\"#inference-script-prodiagrafi\">\u03a4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc inference script \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03c0\u03c1\u03bf\u03b4\u03b9\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae<\/a><\/li>\n<li><a href=\"#episodic-search\">Episodic search: \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ac\u03c2 \u03c4\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1, \u03cc\u03c7\u03b9 \u03cc\u03bb\u03bf \u03c4\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc<\/a><\/li>\n<li><a href=\"#in-model-acceptance\">\u0397 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf model instance<\/a><\/li>\n<li><a href=\"#deka-workloads\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 \u03b4\u03ad\u03ba\u03b1 workloads \u03c3\u03b5 A100 \u03ba\u03b1\u03b9 H100<\/a><\/li>\n<li><a href=\"#expert-kernels\">\u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03b5 expert kernels \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03af\u03b4\u03b9\u03bf scope<\/a><\/li>\n<li><a href=\"#kernelbench\">\u03a4\u03bf KernelBench \u03b5\u03af\u03bd\u03b1\u03b9 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03b9\u03ba\u03c4\u03b9\u03ba\u03cc evidence, \u03cc\u03c7\u03b9 \u03bf \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2<\/a><\/li>\n<li><a href=\"#restart-ablation\">\u03a4\u03bf ablation \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf restart \u03ad\u03c7\u03b5\u03b9 \u03b1\u03be\u03af\u03b1<\/a><\/li>\n<li><a href=\"#omades-ai-saas-ecommerce\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 AI, SaaS \u03ba\u03b1\u03b9 e-commerce<\/a><\/li>\n<li><a href=\"#oria-meletis\">\u039f\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03bf\u03c1\u03b1\u03c4\u03bf\u03af<\/a><\/li>\n<li><a href=\"#symperasma\">\u0397 \u03bf\u03c5\u03c3\u03af\u03b1: \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"benchmark-deployment-gap\">\u03a4\u03bf benchmark-to-deployment gap \u03c0\u03bf\u03c5 \u03ba\u03c1\u03cd\u03b2\u03b5\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/h2>\n<p>\u03a4\u03b1 \u03c3\u03c5\u03bd\u03ae\u03b8\u03b7 kernel benchmarks \u03b5\u03ba\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03ad\u03bd\u03b1\u03bd \u03c5\u03c0\u03bf\u03c8\u03ae\u03c6\u03b9\u03bf \u03c3\u03b5 \u03c3\u03c5\u03bd\u03b8\u03b5\u03c4\u03b9\u03ba\u03ac inputs, \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5\u03bd \u03b1\u03bd \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03b5\u03af \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ac \u03bc\u03b5 \u03bc\u03b9\u03b1 reference \u03c5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03bf\u03cd\u03bd \u03c4\u03b7\u03bd \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03b7 \u03ba\u03b1\u03b8\u03c5\u03c3\u03c4\u03ad\u03c1\u03b7\u03c3\u03b7. \u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03c9\u03c2 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf \u03c6\u03af\u03bb\u03c4\u03c1\u03bf, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b5\u03c4\u03c1\u03ac \u03ad\u03bd\u03b1\u03bd \u03b5\u03bd\u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03bf \u03b4\u03b5\u03af\u03ba\u03c4\u03b7. \u03a4\u03bf deployment \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf\u03c5 patched \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c3\u03c4\u03bf\u03c7\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf workload: cache state, memory residency, dispatch overhead, allocator state, shape guards \u03ba\u03b1\u03b9 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 \u03bc\u03b5 \u03ae\u03b4\u03b7 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03b1 kernels.<\/p>\n<p>\u03a3\u03c4\u03bf \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2, \u03ad\u03bd\u03b1 convolution kernel \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03c5 \u03bc\u03b5 freshly allocated inputs, \u03b1\u03bb\u03bb\u03ac \u03c7\u03ac\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c4\u03b5\u03af\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u039f \u03bb\u03cc\u03b3\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 baseline \u03c5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03b1\u03c1\u03b1\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 activations \u03c0\u03bf\u03c5 \u03bc\u03cc\u03bb\u03b9\u03c2 \u03c0\u03b1\u03c1\u03ae\u03b3\u03b1\u03b3\u03b5 \u03c4\u03bf \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf layer \u03ba\u03b1\u03b9 \u03b5\u03c0\u03c9\u03c6\u03b5\u03bb\u03b5\u03af\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc warm-cache locality. \u0397 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03c0\u03bf\u03c5 \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd cold-cache \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c4\u03bf \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03ac \u03c4\u03b7\u03c2 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc execution context.<\/p>\n<p>\u0397 \u03af\u03b4\u03b9\u03b1 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 AI agents: \u03ad\u03bd\u03b1 \u03ba\u03b1\u03bb\u03cc score \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03cc\u03c4\u03b1\u03bd \u03b4\u03b5\u03bd \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03bf \u03c3\u03c9\u03c3\u03c4\u03cc artifact, \u03c4\u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ae \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03c3\u03b9\u03bc\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1. \u0397 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c4\u03bf\u03c5 <a href=\"https:\/\/twodots.gr\/k-bench-ai-agents-epistimoniki-akriveia-artifacts\/\">K-Bench \u03b3\u03b9\u03b1 accuracy, communication \u03ba\u03b1\u03b9 artifacts<\/a> \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03b7 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b5 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c0\u03b1\u03c1\u03b1\u03b4\u03bf\u03c4\u03ad\u03bf.<\/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\">Isolated benchmark<\/p>\n<p>\u039c\u03b5\u03c4\u03c1\u03ac \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03bf\u03c1\u03b8\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 latency \u03bc\u03b5 \u03c3\u03c5\u03bd\u03b8\u03b5\u03c4\u03b9\u03ba\u03ac inputs. \u0395\u03af\u03bd\u03b1\u03b9 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf \u03c6\u03af\u03bb\u03c4\u03c1\u03bf, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae\u03c2 \u03b1\u03be\u03af\u03b1\u03c2.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Proxy<\/span><span class=\"td-badge\">Cold context<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<p class=\"td-comparison-title\">In-model acceptance<\/p>\n<p>\u0395\u03bb\u03ad\u03b3\u03c7\u03b5\u03b9 \u03c4\u03bf patch \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf model instance, \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac shapes, phase predicates, guards, cache state \u03ba\u03b1\u03b9 fallbacks.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Deployable<\/span><span class=\"td-badge\">Workload-aware<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">End-to-end \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1<\/p>\n<p>\u039c\u03b5\u03c4\u03c1\u03ac \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf inference script \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 dispatch overhead \u03ba\u03b1\u03b9 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03b9\u03b4\u03c1\u03ac\u03c3\u03b5\u03b9\u03c2 \u03bc\u03b5 \u03c4\u03bf\u03bd \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03bf \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Business metric<\/span><span class=\"td-badge\">Final gate<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"treis-apotychies\">\u03a4\u03c1\u03b5\u03b9\u03c2 \u03c4\u03c1\u03cc\u03c0\u03bf\u03b9 \u03bc\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03bf\u03c0\u03bf\u03af\u03bf\u03c5\u03c2 \u03ad\u03bd\u03b1 \u00ab\u03c3\u03c9\u03c3\u03c4\u03cc\u00bb kernel \u03b1\u03c0\u03bf\u03c4\u03c5\u03b3\u03c7\u03ac\u03bd\u03b5\u03b9<\/h2>\n<p>\u03a4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03b5\u03af\u03b4\u03bf\u03c2 \u03b1\u03c3\u03c4\u03bf\u03c7\u03af\u03b1\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03c1\u03bf\u03c6\u03ae \u03c4\u03b7\u03c2 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2: \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c2 \u03c7\u03c1\u03cc\u03bd\u03bf\u03c2 \u03c3\u03c4\u03bf harness, \u03c7\u03b5\u03b9\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c2 \u03c7\u03c1\u03cc\u03bd\u03bf\u03c2 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf integration. \u03a4\u03bf \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 runtime \u03b5\u03b3\u03ba\u03c5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1. \u0388\u03bd\u03b1 out-of-bounds read \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b7 \u03c1\u03af\u03be\u03b5\u03b9 \u03c4\u03bf standalone test \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b7 \u03b4\u03b9\u03c0\u03bb\u03b1\u03bd\u03ae \u03bc\u03bd\u03ae\u03bc\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 mapped, \u03b1\u03bb\u03bb\u03ac \u03bd\u03b1 \u03c0\u03c1\u03bf\u03ba\u03b1\u03bb\u03ad\u03c3\u03b5\u03b9 CUDA error \u03cc\u03c4\u03b1\u03bd \u03bf allocator \u03c4\u03bf\u03c5 \u03c0\u03bb\u03ae\u03c1\u03bf\u03c5\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c4\u03ae\u03c3\u03b5\u03b9 \u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac.<\/p>\n<p>\u03a4\u03bf \u03c4\u03c1\u03af\u03c4\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b7 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b1\u03bd\u03ac \u03c6\u03ac\u03c3\u03b7. \u03a3\u03c4\u03bf autoregressive inference, \u03c4\u03bf prefill \u03ba\u03b1\u03b9 \u03c4\u03bf decode \u03ad\u03c7\u03bf\u03c5\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac shapes, \u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 KV cache, patterns \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7\u03c2 \u03c3\u03c4\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03b5\u03c5\u03b1\u03b9\u03c3\u03b8\u03b7\u03c3\u03af\u03b1 \u03c3\u03c4\u03bf latency. \u0388\u03bd\u03b1 attention kernel \u03c0\u03bf\u03c5 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf prefill \u03b3\u03b9\u03b1 <em>q_len<\/em> \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc 1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b7\u03bd \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b8\u03cc\u03bb\u03bf\u03c5 \u03c3\u03c4\u03bf decode, \u03cc\u03c0\u03bf\u03c5 <em>q_len<\/em> \u03b9\u03c3\u03bf\u03cd\u03c4\u03b1\u03b9 \u03bc\u03b5 1. \u0386\u03c1\u03b1 \u03b7 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03af\u03b1 \u03b4\u03b5\u03bd \u03b9\u03c3\u03bf\u03b4\u03c5\u03bd\u03b1\u03bc\u03b5\u03af \u03bf\u03cd\u03c4\u03b5 \u03bc\u03b5 \u03c0\u03bb\u03ae\u03c1\u03b7 \u03ba\u03ac\u03bb\u03c5\u03c8\u03b7 \u03bf\u03cd\u03c4\u03b5 \u03bc\u03b5 end-to-end \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2.<\/p>\n<aside class=\"td-article-note\"><strong>\u039a\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7:<\/strong> correctness \u03c3\u03b5 \u03ad\u03bd\u03b1 extracted task \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 runtime safety \u03ae phase coverage \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf integration. \u039a\u03ac\u03b8\u03b5 candidate \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03ad\u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c3\u03c4\u03bf model instance \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03c4\u03bf\u03bd \u03b5\u03ba\u03c4\u03b5\u03bb\u03ad\u03c3\u03b5\u03b9.<\/aside>\n<h2 id=\"tessera-epipeda-elegchou\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03b1 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03b1\u03bd\u03c4\u03af \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1 score<\/h2>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1. \u0397 isolated qualification \u03b5\u03bb\u03ad\u03b3\u03c7\u03b5\u03b9 standalone \u03bf\u03c1\u03b8\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 latency. \u0397 search-time validation \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac inputs, safety gates \u03ae timing \u03c3\u03b5 \u03c0\u03b9\u03bf \u03c0\u03b9\u03c3\u03c4\u03cc context. \u0397 deployment-time acceptance \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c4\u03b5\u03af \u03c4\u03bf\u03bd \u03c5\u03c0\u03bf\u03c8\u03ae\u03c6\u03b9\u03bf \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf model instance. \u03a4\u03ad\u03bb\u03bf\u03c2, \u03b7 end-to-end \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03bc\u03b5\u03c4\u03c1\u03ac \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf patched workload.<\/p>\n<p>\u0397 \u03bf\u03c5\u03c3\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf benchmark \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03b3\u03b9\u03b1 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03bb\u03bf\u03b3\u03ae \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd candidates, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03c0\u03b1\u03af\u03c1\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7. \u03a4\u03bf LLM4LLM \u03b4\u03ad\u03bd\u03b5\u03b9 generation, verification, acceptance \u03ba\u03b1\u03b9 deployment \u03c3\u03b5 loop \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u03c3\u03c4\u03cc\u03c7\u03bf \u03c0\u03bf\u03c5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9. \u0391\u03c5\u03c4\u03cc \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03bf performance engineering \u03b1\u03c0\u03cc offline \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b1\u03c0\u03bf\u03c3\u03c0\u03b1\u03c3\u03bc\u03ad\u03bd\u03c9\u03bd kernels \u03c3\u03b5 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03cc\u03c0\u03bf\u03c5 \u03ba\u03ac\u03b8\u03b5 patch \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03b5\u03af\u03be\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03be\u03af\u03b1 \u03c4\u03bf\u03c5 \u03c3\u03c4\u03bf \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03ae\u03c3\u03b5\u03b9.<\/p>\n<h2 id=\"inference-script-prodiagrafi\">\u03a4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc inference script \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03c0\u03c1\u03bf\u03b4\u03b9\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae<\/h2>\n<p>\u03a4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03b1\u03c0\u03cc inference script \u03c0\u03bf\u03c5 \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 \u03bf \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7\u03c2. \u03a4\u03bf script \u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf workload, \u03c4\u03b1 inputs, \u03c4\u03bf runtime path \u03ba\u03b1\u03b9 \u03c4\u03bf\u03bd \u03c3\u03c4\u03cc\u03c7\u03bf \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2. \u03a4\u03bf LLM4LLM \u03ba\u03ac\u03bd\u03b5\u03b9 \u03b9\u03b5\u03c1\u03b1\u03c1\u03c7\u03b9\u03ba\u03cc profiling \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 semantic module instances \u03c4\u03c9\u03bd \u03bf\u03c0\u03bf\u03af\u03c9\u03bd \u03b7 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03c0\u03c1\u03ac\u03b3\u03bc\u03b1\u03c4\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03c3\u03b5\u03b9 \u03c4\u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc latency. \u0388\u03bd\u03b1 module \u03b8\u03b5\u03c9\u03c1\u03b5\u03af\u03c4\u03b1\u03b9 deployable \u03cc\u03c4\u03b1\u03bd \u03ad\u03c7\u03b5\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc call boundary, \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03b3\u03ce\u03b3\u03b9\u03bc\u03b1 tensors \u03b5\u03b9\u03c3\u03cc\u03b4\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03b5\u03be\u03cc\u03b4\u03bf\u03c5 \u03ba\u03b1\u03b9 fallback \u03b3\u03b9\u03b1 regimes \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9.<\/p>\n<p>\u0393\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 hotspot \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03bf\u03bd\u03c4\u03b1\u03b9 representative inputs, reference outputs, shapes, module family, hardware scope \u03ba\u03b1\u03b9 phase tags. \u038c\u03c4\u03b1\u03bd prefill \u03ba\u03b1\u03b9 decode \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd, \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ad\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03b5\u03c4\u03b1\u03b9 dispatch template \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03b5\u03c0\u03b1\u03bd\u03b5\u03bd\u03c9\u03b8\u03bf\u03cd\u03bd \u03bf\u03b9 phase-specialized candidates. \u0388\u03bd\u03b1\u03c2 workload-weighted hotspot score \u03c3\u03c5\u03bd\u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc \u03c7\u03c1\u03cc\u03bd\u03bf\u03c5 \u03ba\u03ac\u03b8\u03b5 module \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03c6\u03ac\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03c3\u03c5\u03c7\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ae \u03b2\u03b1\u03c1\u03cd\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03c6\u03ac\u03c3\u03b7\u03c2 \u03c3\u03c4\u03bf workload.<\/p>\n<p>\u0397 \u03b1\u03c1\u03c7\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03b3\u03b3\u03b5\u03bd\u03ae\u03c2 \u03bc\u03b5 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/multimodal-speculative-decoding-parallili-ai\/\">multimodal speculative decoding<\/a>: \u03b7 \u03b8\u03b5\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03ae \u03c0\u03b1\u03c1\u03b1\u03bb\u03bb\u03b7\u03bb\u03af\u03b1 \u03ae \u03b7 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03b5\u03bd\u03cc\u03c2 \u03b5\u03c0\u03b9\u03bc\u03ad\u03c1\u03bf\u03c5\u03c2 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03cd \u03b1\u03c0\u03bf\u03ba\u03c4\u03ac \u03b1\u03be\u03af\u03b1 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf latency \u03c3\u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 serving path.<\/p>\n<h2 id=\"episodic-search\">Episodic search: \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ac\u03c2 \u03c4\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1, \u03cc\u03c7\u03b9 \u03cc\u03bb\u03bf \u03c4\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc<\/h2>\n<p>\u039f optimizer \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03c3\u03b5 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b1 generate\u2013verify\u2013decide episodes. \u039f agent \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 Triton kernels, \u03c4\u03b1 \u03ba\u03ac\u03bd\u03b5\u03b9 compile, \u03b5\u03bb\u03ad\u03b3\u03c7\u03b5\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03bf\u03c1\u03b8\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 runtime safety, \u03bc\u03b5\u03c4\u03c1\u03ac latency \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03b5\u03b9 compiler diagnostics, numerical errors \u03ba\u03b1\u03b9 failures. \u039f\u03b9 \u03c5\u03c0\u03bf\u03c8\u03ae\u03c6\u03b9\u03bf\u03b9 \u03ba\u03b1\u03c4\u03b1\u03c4\u03ac\u03c3\u03c3\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b5 utility \u03c0\u03bf\u03c5 \u03bc\u03b7\u03b4\u03b5\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03bd \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03ac\u03c3\u03bf\u03c5\u03bd \u03c4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c3\u03c6\u03ac\u03bb\u03bc\u03b1\u03c4\u03bf\u03c2 \u03ae \u03c4\u03bf\u03c5\u03c2 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5\u03c2 \u03b1\u03c3\u03c6\u03b1\u03bb\u03b5\u03af\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03b1\u03bd\u03c4\u03b1\u03bd\u03b1\u03ba\u03bb\u03ac \u03c4\u03bf speedup \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03b7\u03c2 reference \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2.<\/p>\n<p>\u03a3\u03c4\u03bf \u03c4\u03ad\u03bb\u03bf\u03c2 \u03ba\u03ac\u03b8\u03b5 episode, \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c3\u03c5\u03bc\u03c0\u03c5\u03ba\u03bd\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf evidence: \u03ad\u03b3\u03ba\u03c5\u03c1\u03b1 tiling choices, boundary masks, phase predicates, numerical constraints, failure signatures \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2. \u03a4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 transcript \u03b1\u03c1\u03c7\u03b5\u03b9\u03bf\u03b8\u03b5\u03c4\u03b5\u03af\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03b7 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b5\u03b9\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03b4\u03b9\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae \u03c3\u03c5\u03bd \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7 \u03c3\u03cd\u03bd\u03c4\u03bf\u03bc\u03b7 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03af\u03b1. \u039f \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03bc\u03b7 \u03c6\u03c5\u03bb\u03b1\u03ba\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b1\u03bd\u03b1\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7 \u03c3\u03b5 \u03bc\u03b9\u03b1 \u03bc\u03b1\u03ba\u03c1\u03ac \u03b1\u03bb\u03c5\u03c3\u03af\u03b4\u03b1 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ce\u03bd \u03b4\u03b9\u03bf\u03c1\u03b8\u03ce\u03c3\u03b5\u03c9\u03bd \u03b5\u03bd\u03cc\u03c2 candidate \u03c0\u03bf\u03c5 \u03af\u03c3\u03c9\u03c2 \u03b4\u03b5\u03bd \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c9\u03b8\u03b5\u03af.<\/p>\n<p>\u0397 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03ba\u03c1\u03b1\u03c4\u03ac \u03c4\u03b5\u03ba\u03bc\u03ae\u03c1\u03b9\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2, \u03cc\u03c7\u03b9 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03b1\u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c4\u03b1 \u03cc\u03bb\u03bf \u03c4\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc. \u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03c3\u03c5\u03bd\u03b1\u03bd\u03c4\u03ac\u03bc\u03b5 \u03c3\u03c4\u03b7 \u03c3\u03c5\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7 \u03b3\u03b9\u03b1 <a href=\"https:\/\/twodots.gr\/echo-mnimi-ai-agent-apodeixeis-provenance\/\">\u03bc\u03bd\u03ae\u03bc\u03b7 AI agents \u03bc\u03b5 provenance \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03af\u03be\u03b5\u03b9\u03c2<\/a>.<\/p>\n<h2 id=\"in-model-acceptance\">\u0397 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf model instance<\/h2>\n<p>\u0388\u03bd\u03b1\u03c2 candidate \u03c0\u03bf\u03c5 \u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c4\u03bf extracted task \u03b4\u03b5\u03bd \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bc\u03ac\u03c4\u03c9\u03c2 patch. \u039a\u03b1\u03c4\u03b1\u03c4\u03ac\u03c3\u03c3\u03b5\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03c0\u03c1\u03bf\u03c2 module compatibility, shape coverage, phase compatibility \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b1\u03bc\u03b5\u03bd\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7, \u03b5\u03bd\u03c3\u03c9\u03bc\u03b1\u03c4\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b1\u03b9 \u03be\u03b1\u03bd\u03b1\u03bc\u03b5\u03c4\u03c1\u03b9\u03ad\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 correctness \u03ba\u03b1\u03b9 latency. \u0393\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b5\u03ba\u03c4\u03cc\u03c2 \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03c0\u03b5\u03c1\u03ac\u03c3\u03b5\u03b9 \u03c4\u03bf\u03c5\u03c2 in-model \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03be\u03b5\u03c0\u03b5\u03c1\u03ac\u03c3\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf \u03cc\u03c1\u03b9\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7\u03c2.<\/p>\n<p>\u03a4\u03b1 runtime guards \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03bf\u03cd\u03bd \u03c4\u03b7 \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03c9\u03bd shapes \u03ba\u03b1\u03b9 \u03c4\u03b1 fallback paths \u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03c5\u03bd \u03c4\u03b1 \u03bc\u03b7 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03b1 regimes. \u0388\u03c4\u03c3\u03b9 \u03c4\u03bf \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc artifact \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1\u03c2 \u03c0\u03bf\u03c5 \u03ad\u03b3\u03c1\u03b1\u03c8\u03b5 \u03ad\u03bd\u03b1\u03c2 LLM agent. \u0395\u03af\u03bd\u03b1\u03b9 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1\u03c2 \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 dispatch, guards \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b4\u03b5\u03b9\u03b3\u03bc\u03ad\u03bd\u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf workload. \u0393\u03b9\u03b1 production \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2, \u03b1\u03c5\u03c4\u03ae \u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae: \u03c4\u03bf acceptance criterion \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b5 \u03c4\u03bf\u03bd \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc \u03c3\u03c4\u03cc\u03c7\u03bf.<\/p>\n<p>\u038c\u03c0\u03c9\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 <a href=\"https:\/\/twodots.gr\/lacl-gui-gui-agents-poiotita-diadromis\/\">GUI agents \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ae\u03c2<\/a>, \u03b7 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03af\u03b1 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03c1\u03cd\u03b2\u03b5\u03b9 \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b1 \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1, \u03c0\u03b5\u03c1\u03b9\u03c4\u03c4\u03ad\u03c2 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b5\u03c2 \u03ae brittle fallbacks.<\/p>\n<h2 id=\"deka-workloads\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 \u03b4\u03ad\u03ba\u03b1 workloads \u03c3\u03b5 A100 \u03ba\u03b1\u03b9 H100<\/h2>\n<p>\u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 transformer models, Mamba state-space models \u03ba\u03b1\u03b9 RecurrentGemma. \u03a3\u03c4\u03bf\u03c5\u03c2 transformers, \u03c4\u03bf Qwen3-4B \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 speedup 3,03\u00d7 \u03c3\u03c4\u03b7\u03bd A100 \u03ba\u03b1\u03b9 3,78\u00d7 \u03c3\u03c4\u03b7\u03bd H100, \u03b5\u03bd\u03ce \u03c4\u03bf Qwen3-32B 2,19\u00d7 \u03ba\u03b1\u03b9 2,55\u00d7 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1. \u03a4\u03b1 Llama-2-7B \u03ba\u03b1\u03b9 13B \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1\u03c3\u03b1\u03bd \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b1\u03bb\u03bb\u03ac \u03b8\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03c3\u03b5\u03b9\u03c2, \u03b1\u03c0\u03cc 1,79\u00d7 \u03ad\u03c9\u03c2 2,22\u00d7.<\/p>\n<p>\u03a3\u03c4\u03b9\u03c2 \u03bf\u03b9\u03ba\u03bf\u03b3\u03ad\u03bd\u03b5\u03b9\u03b5\u03c2 state-space \u03bf\u03b9 \u03b1\u03c5\u03be\u03ae\u03c3\u03b5\u03b9\u03c2 \u03ae\u03c4\u03b1\u03bd \u03c0\u03bf\u03bb\u03cd \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b5\u03c2 \u03c3\u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7. \u03a4\u03bf Mamba 130M \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b8\u03b7\u03ba\u03b5 \u03bc\u03b5 22,37\u00d7 \u03c3\u03c4\u03b7\u03bd A100 \u03ba\u03b1\u03b9 32,79\u00d7 \u03c3\u03c4\u03b7\u03bd H100, \u03b5\u03bd\u03ce \u03c4\u03bf Mamba2 2.7B \u03bc\u03b5 15,23\u00d7 \u03ba\u03b1\u03b9 14,04\u00d7. \u03a4\u03bf RecurrentGemma-9B \u03b5\u03af\u03c7\u03b5 1,24\u00d7 \u03c3\u03c4\u03b7\u03bd A100 \u03b1\u03bb\u03bb\u03ac 7,55\u00d7 \u03c3\u03c4\u03b7\u03bd H100. \u0397 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03bc\u03af\u03b1 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae attention-first \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae: \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bf\u03b9\u03ba\u03bf\u03b3\u03ad\u03bd\u03b5\u03b9\u03b5\u03c2 \u03ba\u03b1\u03b9 GPUs \u03b5\u03ba\u03b8\u03ad\u03c4\u03bf\u03c5\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac hotspots.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-kicker\">\u03a4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2<\/p>\n<p class=\"td-chart-title\">\u03a4\u03bf end-to-end \u03ba\u03b1\u03b9 \u03c4\u03bf kernel-level evidence \u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac<\/p>\n<p class=\"td-chart-intro\">\u039f\u03b9 \u03b4\u03cd\u03bf \u03c0\u03c1\u03ce\u03c4\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03c4\u03b1 \u03b4\u03ad\u03ba\u03b1 model-integrated workloads. \u039f\u03b9 \u03b4\u03cd\u03bf \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03b5\u03c2 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd KernelBench Level 2 \u03ba\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03bd \u03c9\u03c2 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03b9\u03ba\u03c4\u03b9\u03ba\u03ae, \u03cc\u03c7\u03b9 \u03b9\u03c3\u03bf\u03b4\u03cd\u03bd\u03b1\u03bc\u03b7, \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7.<\/p>\n<\/div>\n<div class=\"td-chart-body\">\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">3,91\u00d7<\/span><span class=\"td-metric-label\">\u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03cc \u03bc\u03ad\u03c3\u03bf end-to-end speedup \u03c3\u03b5 A100<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">6,98\u00d7<\/span><span class=\"td-metric-label\">\u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03cc \u03bc\u03ad\u03c3\u03bf end-to-end speedup \u03c3\u03b5 H100<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">2,745\u00d7<\/span><span class=\"td-metric-label\">KernelBench Level 2 GeoMean \u03c3\u03b5 A100<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">2,628\u00d7<\/span><span class=\"td-metric-label\">KernelBench Level 2 GeoMean \u03c3\u03b5 H100<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<aside class=\"td-article-note\"><strong>\u039c\u03b7\u03bd \u03c5\u03c0\u03b5\u03c1\u03b3\u03b5\u03bd\u03b9\u03ba\u03b5\u03cd\u03b5\u03c4\u03b5 \u03c4\u03b1 speedups:<\/strong> \u03bf\u03b9 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03be\u03b5\u03ba\u03b9\u03bd\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc eager PyTorch \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03bf\u03cd\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, scripts \u03ba\u03b1\u03b9 GPUs. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03bf\u03c5\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03ba\u03ad\u03c1\u03b4\u03bf\u03c2 \u03b1\u03c0\u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03bf serving engine \u03ae traffic mix.<\/aside>\n<h2 id=\"expert-kernels\">\u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03b5 expert kernels \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03af\u03b4\u03b9\u03bf scope<\/h2>\n<p>\u0393\u03b9\u03b1 attention-only workloads, \u03c4\u03bf paper \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03bc\u03b5 FlashAttention\u00b7 \u03b3\u03b9\u03b1 state-space mixers, \u03bc\u03b5 \u03c4\u03b1 hand-optimized paths <em>mamba_ssm<\/em> \u0438 <em>causal-conv1d<\/em>. \u03a3\u03c4\u03bf attention, \u03c4\u03bf LLM4LLM \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03b5 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 prompt\/decode regimes, \u03b1\u03bb\u03bb\u03ac \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c5\u03bb\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9\u03c2 \u03ba\u03c1\u03b1\u03c4\u03bf\u03cd\u03bd \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03b1 shapes. \u0391\u03c5\u03c4\u03cc \u03b1\u03c0\u03bf\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03c5\u03c0\u03b5\u03c1\u03b1\u03c0\u03bb\u03bf\u03c5\u03c3\u03c4\u03b5\u03c5\u03bc\u03ad\u03bd\u03b7 \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03cc\u03c4\u03b9 \u03bf agent \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd expert kernels.<\/p>\n<p>\u03a3\u03c4\u03b1 mixers, \u03bf\u03b9 generated replacements \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c1\u03c1\u03bf\u03c6\u03ae\u03c3\u03bf\u03c5\u03bd reshapes, projections \u03ba\u03b1\u03b9 elementwise updates \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03c3\u03c4\u03b5\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf library kernel. \u0397 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03b7 deployable boundary \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03b3\u03b9\u03b1\u03c4\u03af \u03c3\u03b5 \u03bc\u03b5\u03c1\u03b9\u03ba\u03ac regimes \u03c0\u03bb\u03b7\u03c3\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd \u03ae \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03c4\u03b9\u03c2 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b5\u03c2 fast paths. \u03a0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 scope-matched \u03c0\u03b1\u03c1\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 paper, \u03cc\u03c7\u03b9 \u03b3\u03b9\u03b1 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03c5\u03c0\u03b5\u03c1\u03bf\u03c7\u03ae \u03b1\u03c0\u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03b2\u03b9\u03b2\u03bb\u03b9\u03bf\u03b8\u03ae\u03ba\u03b7 \u03ae production stack.<\/p>\n<h2 id=\"kernelbench\">\u03a4\u03bf KernelBench \u03b5\u03af\u03bd\u03b1\u03b9 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03b9\u03ba\u03c4\u03b9\u03ba\u03cc evidence, \u03cc\u03c7\u03b9 \u03bf \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2<\/h2>\n<p>\u03a3\u03c4\u03bf KernelBench Level 2, \u03c4\u03bf LLM4LLM \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 pass rate 100% \u03c3\u03c4\u03b7\u03bd A100 \u03ba\u03b1\u03b9 99% \u03c3\u03c4\u03b7\u03bd H100, \u03bc\u03b5 GeoMean speedup 2,745\u00d7 \u03ba\u03b1\u03b9 2,628\u00d7 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 eager PyTorch. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ac \u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac arithmetic mean \u03ba\u03b1\u03b9 geometric mean, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 outliers \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b4\u03b9\u03bf\u03b3\u03ba\u03ce\u03c3\u03bf\u03c5\u03bd \u03c4\u03bf\u03bd \u03c0\u03c1\u03ce\u03c4\u03bf. \u03a3\u03c4\u03b7\u03bd A100, \u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03bf arithmetic mean \u03b5\u03af\u03bd\u03b1\u03b9 22,157\u00d7 \u03b5\u03bd\u03ce \u03bf GeoMean 2,745\u00d7.<\/p>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03cc\u03c4\u03b9 unconstrained \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b5\u03ba\u03bc\u03b5\u03c4\u03b1\u03bb\u03bb\u03b5\u03c5\u03c4\u03bf\u03cd\u03bd benchmark \u03b9\u03b4\u03b9\u03b1\u03b9\u03c4\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2, \u03cc\u03c0\u03c9\u03c2 hard-coded outputs \u03ae \u03c0\u03b1\u03c1\u03b1\u03ba\u03ac\u03bc\u03c8\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5 intended computation. \u03a3\u03c4\u03b9\u03c2 \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2, \u03b7 \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7 \u03c4\u03ad\u03c4\u03bf\u03b9\u03c9\u03bd exploits \u03b1\u03bd\u03b1\u03c4\u03af\u03b8\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf harness. \u0391\u03c5\u03c4\u03cc\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ad\u03bd\u03b1\u03c2 \u03bb\u03cc\u03b3\u03bf\u03c2 \u03c0\u03bf\u03c5 \u03b7 \u03ba\u03cd\u03c1\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03bf\u03bb\u03bf\u03b3\u03af\u03b1 \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b1 deployment experiments \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd model-integrated correctness.<\/p>\n<h2 id=\"restart-ablation\">\u03a4\u03bf ablation \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf restart \u03ad\u03c7\u03b5\u03b9 \u03b1\u03be\u03af\u03b1<\/h2>\n<p>\u03a4\u03b1 experiments \u03c3\u03b5 GPT-5.4, Claude Sonnet 4.6 \u03ba\u03b1\u03b9 GLM-5 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd 100 KernelBench Level-2 tasks \u03b1\u03bd\u03ac \u03c0\u03bb\u03ae\u03c1\u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7. \u03a4\u03bf Sample-10 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ac \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf Sample-1, \u03ac\u03c1\u03b1 \u03b7 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b7 \u03c0\u03bf\u03b9\u03ba\u03b9\u03bb\u03af\u03b1 candidates \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03c3\u03bf\u03b2\u03b1\u03c1\u03cc baseline. \u03a4\u03bf Iter-10 \u03c6\u03c4\u03ac\u03bd\u03b5\u03b9 100% pass rate \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 \u03c4\u03c1\u03af\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03b1\u03bb\u03bb\u03ac \u03c3\u03c5\u03c7\u03bd\u03ac \u03be\u03bf\u03b4\u03b5\u03cd\u03b5\u03b9 \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b5\u03b9\u03b5\u03c2 \u03b5\u03c0\u03b9\u03c3\u03ba\u03b5\u03c5\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03bc\u03af\u03b1 \u03c4\u03c1\u03bf\u03c7\u03b9\u03ac \u03ba\u03b1\u03b9 \u03c5\u03c3\u03c4\u03b5\u03c1\u03b5\u03af \u03c3\u03b5 GeoMean \u03ae Fast2 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 sampling.<\/p>\n<p>\u039c\u03b5 episodic restart \u03ba\u03b1\u03b9 budget 15 trials, \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03b7\u03ba\u03b5 \u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c2 GeoMean \u03ba\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c4\u03b1 \u03c4\u03c1\u03af\u03b1 backbones: 2,153 \u03b3\u03b9\u03b1 GPT-5.4, 2,546 \u03b3\u03b9\u03b1 Claude Sonnet 4.6 \u03ba\u03b1\u03b9 2,745 \u03b3\u03b9\u03b1 GLM-5 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 eager. \u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03b9\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03c7\u03c9\u03c1\u03af\u03c2 restart \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c3\u03c5\u03bc\u03c0\u03c5\u03ba\u03bd\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03af\u03b1 \u03ba\u03b1\u03b9 \u03b7 \u03b5\u03c0\u03b1\u03bd\u03b5\u03ba\u03ba\u03af\u03bd\u03b7\u03c3\u03b7 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03bf\u03c5\u03bd \u03b1\u03be\u03af\u03b1 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03b4\u03bf\u03ba\u03b9\u03bc\u03ce\u03bd.<\/p>\n<p>\u0397 \u03b4\u03c5\u03bd\u03b1\u03bc\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae\u03c2 \u03b2\u03ac\u03c3\u03b5\u03b9 evidence \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/llm-smartsolve-dynamic-algorithm-dispatch\/\">SmartSolve \u03ba\u03b1\u03b9 \u03c4\u03bf dynamic algorithm dispatch<\/a>: \u03bf \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03bb\u03b1\u03c4\u03c1\u03b5\u03cd\u03bf\u03c5\u03bc\u03b5 \u03ad\u03bd\u03b1\u03bd solver \u03ae \u03ad\u03bd\u03b1\u03bd candidate, \u03b1\u03bb\u03bb\u03ac \u03bd\u03b1 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03bf\u03c5\u03bc\u03b5 \u03b1\u03c5\u03c4\u03cc\u03bd \u03c0\u03bf\u03c5 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03bf\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1.<\/p>\n<h2 id=\"omades-ai-saas-ecommerce\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 AI, SaaS \u03ba\u03b1\u03b9 e-commerce<\/h2>\n<p>\u0397 \u03ac\u03bc\u03b5\u03c3\u03b7 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03b1\u03c6\u03bf\u03c1\u03ac inference infrastructure, \u03cc\u03c7\u03b9 \u03c4\u03b7 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03bc\u03b9\u03b1\u03c2 \u03ba\u03b1\u03bc\u03c0\u03ac\u03bd\u03b9\u03b1\u03c2 \u03ae \u03b5\u03bd\u03cc\u03c2 e-shop. \u03a9\u03c3\u03c4\u03cc\u03c3\u03bf, \u03b7 \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b1\u03c1\u03c7\u03ae \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c3\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac: proxy metrics \u03ba\u03b1\u03b9 sandbox tests \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03af\u03c1\u03bd\u03bf\u03c5\u03bd \u03bc\u03cc\u03bd\u03b1 \u03c4\u03bf\u03c5\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2. \u0388\u03bd\u03b1 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf microbenchmark \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf user-perceived latency, \u03cc\u03c0\u03c9\u03c2 \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf offline score \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03af\u03b1 \u03c0\u03b5\u03bb\u03ac\u03c4\u03b7.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 AI \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b5\u03c2, \u03c4\u03bf \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf \u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03cc loop \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac request distributions, quality and safety gates, end-to-end latency, fallback coverage \u03ba\u03b1\u03b9 rollback-ready patches. \u0397 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b1\u03c5\u03c4\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b5 \u03c4\u03bf paper \u03c3\u03b5 \u03b5\u03bc\u03c0\u03bf\u03c1\u03b9\u03ba\u03ac \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1. \u03a4\u03bf \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf deployment context \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03c4\u03c9\u03bd \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ce\u03bd \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ce\u03bd.<\/p>\n<p>\u0397 \u03b1\u03be\u03b9\u03bf\u03c0\u03b9\u03c3\u03c4\u03af\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ae \u03c0\u03b1\u03c1\u03b1\u03c4\u03b7\u03c1\u03b7\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1. \u0397 \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7 \u03c4\u03bf\u03c5 <a href=\"https:\/\/twodots.gr\/core-dump-epidemiology-openai-axiopistia-epicheiriseon\/\">core dump epidemiology \u03b3\u03b9\u03b1 software reliability<\/a> \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03b1 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b1 failure signatures \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 tickets \u03c0\u03bf\u03c5 \u03ba\u03bb\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03b1.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-kicker\">\u039a\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7\u03c2 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc rollout<\/p>\n<p class=\"td-decision-title\">\u039c\u03b7\u03bd \u03b5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 optimization \u03c0\u03bf\u03c5 \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c3\u03c4\u03bf harness<\/p>\n<p>Go \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 representative workload, \u03c3\u03c9\u03c3\u03c4\u03ac phase tags, correctness \u03ba\u03b1\u03b9 runtime-safety gates, shape coverage, explicit fallback, in-model latency \u03ba\u03b1\u03b9 rollback path. No-go \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b5\u03bd\u03c4\u03c5\u03c0\u03c9\u03c3\u03b9\u03b1\u03ba\u03cc isolated speedup \u03c7\u03c9\u03c1\u03af\u03c2 end-to-end evidence \u03ae \u03c7\u03c9\u03c1\u03af\u03c2 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c3\u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 serving.<\/p>\n<\/div>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0395\u03c0\u03c4\u03ac \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03b9 \u03b3\u03b9\u03b1 deployment-aware \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 1<\/span><strong>\u039a\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 request distribution, batch regime, sequence lengths, hardware, runtime \u03ba\u03b1\u03b9 \u03c4\u03bf end-to-end metric \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c0\u03c1\u03ac\u03b3\u03bc\u03b1\u03c4\u03b9 \u03bd\u03b1 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c9\u03b8\u03b5\u03af.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 2<\/span><strong>\u039a\u03ac\u03bd\u03c4\u03b5 profiling \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf search<\/strong>\n<p>\u0395\u03bd\u03c4\u03bf\u03c0\u03af\u03c3\u03c4\u03b5 \u03c0\u03bf\u03b9\u03b1 modules \u03ba\u03b1\u03c4\u03b1\u03bd\u03b1\u03bb\u03ce\u03bd\u03bf\u03c5\u03bd \u03c7\u03c1\u03cc\u03bd\u03bf \u03c3\u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c6\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03b7\u03bd \u03b4\u03b9\u03b1\u03b8\u03ad\u03c4\u03b5\u03c4\u03b5 budget \u03c3\u03b5 kernels \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc latency.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 3<\/span><strong>\u03a7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 prefill \u03ba\u03b1\u03b9 decode \u03cc\u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9<\/strong>\n<p>\u0391\u03bd shapes, KV-cache state \u03ae execution path \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd, \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03ae\u03c3\u03c4\u03b5 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ad\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae dispatch predicates.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 4<\/span><strong>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 correctness \u03ba\u03b1\u03b9 runtime safety<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 representative tensors, \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ac thresholds, allocator jitter \u03ba\u03b1\u03b9 blocking launches \u03c0\u03c1\u03b9\u03bd \u03b8\u03b5\u03c9\u03c1\u03ae\u03c3\u03b5\u03c4\u03b5 \u03ad\u03bd\u03b1\u03bd candidate \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03bf \u03b3\u03b9\u03b1 integration.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 5<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf model instance<\/strong>\n<p>\u0395\u03c0\u03b1\u03bd\u03b1\u03bb\u03ac\u03b2\u03b5\u03c4\u03b5 correctness \u03ba\u03b1\u03b9 latency \u03bc\u03b5 \u03c4\u03bf patch, \u03c4\u03b1 guards, \u03c4\u03b1 fallbacks \u03ba\u03b1\u03b9 \u03c4\u03bf\u03bd \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd\u03c4\u03b1 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1 \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03c4\u03c1\u03ad\u03be\u03b5\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 6<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03b5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03ad\u03c1\u03b4\u03bf\u03c2<\/strong>\n<p>\u0391\u03c0\u03bf\u03b4\u03b5\u03c7\u03b8\u03b5\u03af\u03c4\u03b5 patch \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03be\u03b5\u03c0\u03b5\u03c1\u03bd\u03ac \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf end-to-end threshold \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 coverage, \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1 \u03ae \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b4\u03b9\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 7<\/span><strong>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 drift \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ae\u03c3\u03c4\u03b5 rollback<\/strong>\n<p>\u039d\u03ad\u03b1 shapes, scheduler, batching \u03ae hardware \u03b1\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03bd \u03bd\u03ad\u03b1 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7. \u039a\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 versioned artifacts, logs \u03ba\u03b1\u03b9 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b7 \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03bf\u03c6\u03ae \u03c3\u03c4\u03b7 reference \u03c5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<h2 id=\"oria-meletis\">\u039f\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03bf\u03c1\u03b1\u03c4\u03bf\u03af<\/h2>\n<p>\u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 workloads, \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, A100\/H100 GPUs \u03ba\u03b1\u03b9 eager PyTorch baselines. \u0394\u03b5\u03bd \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03af\u03b4\u03b9\u03bf speedup \u03b1\u03c0\u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03bf serving stack, \u03bf\u03cd\u03c4\u03b5 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03b1 chips, batch regimes \u03ae real-world traffic mixes. \u039f\u03b9 scope-matched \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03bc\u03b5 expert kernels \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03ae\u03b4\u03b7 \u03cc\u03c4\u03b9 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c5\u03bb\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b5\u03c2 \u03c3\u03b5 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 attention shapes.<\/p>\n<p>\u03a4\u03bf paper \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b4\u03b5\u03bd \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03b7 kernel generation \u03c3\u03b5 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03c7\u03c9\u03c1\u03af\u03c2 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2. \u0391\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 profiling, compilation, \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 candidates, \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc hardware \u03ba\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c3\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac correctness thresholds. \u03a4\u03bf restart \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03ac\u03c7\u03c1\u03b7\u03c3\u03c4\u03bf context, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03b1\u03bd\u03b1\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 compute-intensive. \u039a\u03b1\u03b9 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b7 model-integrated acceptance \u03b5\u03af\u03bd\u03b1\u03b9 workload-specific, \u03ad\u03bd\u03b1 patch \u03c0\u03bf\u03c5 \u03ad\u03b3\u03b9\u03bd\u03b5 \u03b4\u03b5\u03ba\u03c4\u03cc \u03b3\u03b9\u03b1 \u03bc\u03af\u03b1 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03ad\u03b1 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7 \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03c4\u03bf workload \u03ae \u03c4\u03bf runtime.<\/p>\n<p>\u0397 \u03c4\u03c1\u03ad\u03c7\u03bf\u03c5\u03c3\u03b1 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c3\u03c4\u03bf\u03c7\u03b5\u03cd\u03b5\u03b9 single-GPU inference \u03ba\u03b1\u03b9 \u03c0\u03c1\u03bf\u03cb\u03c0\u03bf\u03b8\u03ad\u03c4\u03b5\u03b9 \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03c5\u03c4\u03b9\u03ba\u03cc inference script. Tensor parallelism, pipeline parallelism, continuous batching \u03ba\u03b1\u03b9 multi-tenant serving \u03b5\u03b9\u03c3\u03ac\u03b3\u03bf\u03c5\u03bd \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03b1 \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03b1: communication cost, scheduler interaction, batch-level interference \u03ba\u03b1\u03b9 resource contention. \u0391\u03c5\u03c4\u03ad\u03c2 \u03bf\u03b9 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b1 \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1.<\/p>\n<h2 id=\"symperasma\">\u0397 \u03bf\u03c5\u03c3\u03af\u03b1: \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1<\/h2>\n<p>\u03a4\u03bf LLM4LLM \u03b4\u03b5\u03bd \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03ad\u03bd\u03b1 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 Triton. \u0397 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03c4\u03bf\u03c5 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 profiling \u03c4\u03bf\u03c5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03bf\u03cd workload, phase-aware decomposition, compact experiential memory, runtime safety \u03ba\u03b1\u03b9 end-to-end acceptance. \u0388\u03c4\u03c3\u03b9 \u03bf\u03b9 candidates \u03ba\u03c1\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b1\u03bd \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b1\u03bd \u03b5\u03bd\u03c4\u03c5\u03c0\u03c9\u03c3\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf scoreboard.<\/p>\n<p>\u0393\u03b9\u03b1 engineering leaders, \u03b7 \u03c0\u03b9\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03bd\u03bf\u03bf\u03c4\u03c1\u03bf\u03c0\u03af\u03b1\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf search \u03c4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 deployable success: \u03c0\u03bf\u03b9\u03b5\u03c2 \u03c6\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03bd\u03c4\u03b1\u03b9, \u03c0\u03bf\u03b9\u03b1 shapes \u03ad\u03c7\u03bf\u03c5\u03bd fallback, \u03c0\u03bf\u03b9\u03b1 correctness \u03cc\u03c1\u03b9\u03b1 \u03b9\u03c3\u03c7\u03cd\u03bf\u03c5\u03bd \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03bf end-to-end metric \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03b6\u03b5\u03b9. \u0397 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03ad\u03bd\u03b1 \u03c3\u03c4\u03ac\u03b4\u03b9\u03bf. \u0397 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03bf\u03bb\u03bf\u03ba\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf patch \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03c7\u03b8\u03b5\u03af \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf workload \u03c0\u03bf\u03c5 \u03bc\u03b5\u03c4\u03c1\u03ac.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">\u0391\u03c0\u03cc \u03c4\u03bf benchmark \u03c3\u03b5 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 production AI<\/p>\n<p class=\"td-service-cta-title\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 AI workflows \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac acceptance \u03ba\u03b1\u03b9 rollback gates<\/p>\n<p>\u0397 TWO DOTS \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af workloads, quality checks, latency \u03c3\u03c4\u03cc\u03c7\u03bf\u03c5\u03c2, observability, fallbacks \u03ba\u03b1\u03b9 human handoffs \u03ce\u03c3\u03c4\u03b5 \u03ba\u03ac\u03b8\u03b5 AI \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03a3\u03c5\u03b6\u03b7\u03c4\u03ae\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1\u03bd \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03bf AI \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03cc<\/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 LLM4LLM;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 framework \u03b3\u03b9\u03b1 deployment-aware \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 GPU kernels. \u039e\u03b5\u03ba\u03b9\u03bd\u03ac \u03b1\u03c0\u03cc \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc inference script, \u03b5\u03bd\u03c4\u03bf\u03c0\u03af\u03b6\u03b5\u03b9 hotspots, \u03b1\u03bd\u03b1\u03b6\u03b7\u03c4\u03ac candidates \u03bc\u03b5 LLM agent \u03ba\u03b1\u03b9 \u03b4\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 patches \u03bc\u03cc\u03bd\u03bf \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc in-model validation.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 benchmark-to-deployment gap;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b1\u03c0\u03cc\u03ba\u03bb\u03b9\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b7\u03bd \u03bf\u03c1\u03b8\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ae \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03b5\u03bd\u03cc\u03c2 kernel \u03c3\u03b5 standalone harness \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf\u03c5 \u03bc\u03b5\u03c4\u03ac \u03c4\u03b7\u03bd \u03b5\u03bd\u03c3\u03c9\u03bc\u03ac\u03c4\u03c9\u03c3\u03b7 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc model workload.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03c7\u03c9\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 prefill \u03ba\u03b1\u03b9 decode;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0388\u03c7\u03bf\u03c5\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac sequence shapes, KV-cache state, memory patterns \u03ba\u03b1\u03b9 \u03b5\u03c5\u03b1\u03b9\u03c3\u03b8\u03b7\u03c3\u03af\u03b1 \u03c3\u03c4\u03bf latency. \u0388\u03bd\u03b1 kernel \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c9\u03c6\u03b5\u03bb\u03b5\u03af \u03c4\u03b7 \u03bc\u03af\u03b1 \u03c6\u03ac\u03c3\u03b7 \u03b1\u03bb\u03bb\u03ac \u03bd\u03b1 \u03bc\u03b7\u03bd \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03ac\u03bb\u03bb\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 speedup \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03b1 \u03b4\u03ad\u03ba\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b7\u03bc\u03ad\u03bd\u03b1 inference workloads \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03cc \u03bc\u03ad\u03c3\u03bf 3,91\u00d7 \u03c3\u03c4\u03b7\u03bd A100 \u03ba\u03b1\u03b9 6,98\u00d7 \u03c3\u03c4\u03b7\u03bd H100 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 eager PyTorch. \u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03ac\u03bb\u03bb\u03b5\u03c2 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0391\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03c4\u03bf LLM4LLM \u03c4\u03bf FlashAttention;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ac. \u03a3\u03c4\u03b9\u03c2 scope-matched \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03b5 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 regimes, \u03b5\u03bd\u03ce \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 attention kernels \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03bf\u03cd\u03bd \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03b1 shapes.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af episodic restart;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0393\u03b9\u03b1 \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ac \u03c3\u03c5\u03bc\u03c0\u03c5\u03ba\u03bd\u03c9\u03bc\u03ad\u03bd\u03b1 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ac \u03bc\u03b1\u03b8\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c0\u03cc \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03b5\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b5\u03b3\u03ba\u03bb\u03c9\u03b2\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03bd\u03b1\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7 \u03c3\u03b5 \u03bc\u03b1\u03ba\u03c1\u03cd \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03c4\u03bf\u03c0\u03b9\u03ba\u03ce\u03bd \u03b4\u03b9\u03bf\u03c1\u03b8\u03ce\u03c3\u03b5\u03c9\u03bd \u03b5\u03bd\u03cc\u03c2 candidate.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf \u03c1\u03cc\u03bb\u03bf\u03c2 \u03c4\u03bf\u03c5 KernelBench;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03b9\u03ba\u03c4\u03b9\u03ba\u03cc evidence \u03b3\u03b9\u03b1 kernel-level \u03b9\u03ba\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 search behavior. \u0397 \u03ba\u03cd\u03c1\u03b9\u03b1 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 deployment transfer \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc model-integrated correctness \u03ba\u03b1\u03b9 end-to-end latency.<\/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 \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc \u03bc\u03ae\u03bd\u03c5\u03bc\u03b1 \u03b3\u03b9\u03b1 production AI;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03b1 proxy benchmarks \u03b2\u03bf\u03b7\u03b8\u03bf\u03cd\u03bd \u03c3\u03c4\u03b7 \u03b4\u03b9\u03b1\u03bb\u03bf\u03b3\u03ae, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 correctness, runtime safety, coverage \u03ba\u03b1\u03b9 latency \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload.<\/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\/2608.21836\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 LLM4LLM: Bridging Kernel Benchmarks and Real Deployment via Closed-Loop Agentic Optimization<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/html\/2608.21836\" target=\"_blank\" rel=\"noopener\">arXiv HTML \u2014 \u03c0\u03bb\u03ae\u03c1\u03b7\u03c2 \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03af\u03b1, \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2, ablations \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03c4\u03bf\u03c5 LLM4LLM<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/hzeng2000\/LLM4LLM\" target=\"_blank\" rel=\"noopener\">\u0395\u03c0\u03af\u03c3\u03b7\u03bc\u03bf repository LLM4LLM \u2014 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1\u03c2, workflow, validation controls \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd \u03b4\u03bf\u03ba\u03b9\u03bc\u03ce\u03bd<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2502.10517\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 KernelBench: Can LLMs Write Efficient GPU Kernels?<\/a><\/li>\n<li><a href=\"https:\/\/triton-lang.org\/main\/getting-started\/tutorials\/01-vector-add.html\" target=\"_blank\" rel=\"noopener\">Triton documentation \u2014 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03bf tutorial \u03b3\u03b9\u03b1 custom GPU kernels<\/a><\/li>\n<li><a href=\"https:\/\/www.nist.gov\/publications\/artificial-intelligence-risk-management-framework-generative-artificial-intelligence\" target=\"_blank\" rel=\"noopener\">NIST \u2014 Generative AI Profile \u03b3\u03b9\u03b1 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7, \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03af\u03c9\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c7\u03b5\u03af\u03c1\u03b9\u03c3\u03b7 \u03ba\u03b9\u03bd\u03b4\u03cd\u03bd\u03bf\u03c5<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf LLM4LLM \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 profiling, kernel search \u03ba\u03b1\u03b9 in-model validation, \u03ce\u03c3\u03c4\u03b5 \u03b7 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc inference workload.<\/p>","protected":false},"author":1,"featured_media":98903,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[6506,20646,20648,19427,20647],"class_list":["post-97828","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ai-agents","tag-gpu-optimization","tag-kernelbench","tag-llm-inference","tag-triton"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/97828","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=97828"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/97828\/revisions"}],"predecessor-version":[{"id":98904,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/97828\/revisions\/98904"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media\/98903"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media?parent=97828"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/categories?post=97828"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/tags?post=97828"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}