{"id":89629,"date":"2026-08-25T12:37:32","date_gmt":"2026-08-25T09:37:32","guid":{"rendered":"https:\/\/twodots.gr\/?p=89629"},"modified":"2026-08-25T12:37:49","modified_gmt":"2026-08-25T09:37:49","slug":"lfm25-dspark-taxytero-ai-inference","status":"publish","type":"post","link":"https:\/\/twodots.gr\/bg\/lfm25-dspark-taxytero-ai-inference\/","title":{"rendered":"LFM2.5-DSpark: \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf AI inference \u03b4\u03b5\u03bd \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u03a4\u03bf LFM2.5-DSpark \u03b5\u03c0\u03b9\u03c4\u03b1\u03c7\u03cd\u03bd\u03b5\u03b9 \u03c4\u03bf AI inference \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03c4\u03bf target model: \u03ad\u03bd\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc draft model \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 tokens \u03ba\u03b1\u03b9 \u03c4\u03bf \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03cc LFM2.5 \u03c4\u03b9\u03c2 \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03b5\u03b9.<\/strong> \u03a3\u03c4\u03b1 benchmarks \u03c4\u03b7\u03c2 Liquid AI \u03b7 \u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03b7 \u03b5\u03c0\u03b9\u03c4\u03ac\u03c7\u03c5\u03bd\u03c3\u03b7 \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 3,18\u00d7 \u03c3\u03b5 NVIDIA H100 \u03ba\u03b1\u03b9 2,87\u00d7 \u03c3\u03b5 MacBook Pro M4 Max, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf \u03ba\u03ad\u03c1\u03b4\u03bf\u03c2 \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03ad\u03bd\u03c4\u03bf\u03bd\u03b1 \u03b1\u03bd\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, dataset, hardware \u03ba\u03b1\u03b9 runtime. \u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b2\u03b3\u03b1\u03af\u03bd\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf headline\u00b7 \u03b2\u03b3\u03b1\u03af\u03bd\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 baseline \u03ba\u03b1\u03b9 DSpark \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">\u03a0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1<\/div>\n<ul>\n<li><a href=\"#ti-kykloforise-liquid-ai\">\u03a4\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03ba\u03c5\u03ba\u03bb\u03bf\u03c6\u03cc\u03c1\u03b7\u03c3\u03b5 \u03b7 Liquid AI<\/a><\/li>\n<li><a href=\"#decode-simeio-symforisis\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf decode \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03c3\u03c5\u03bc\u03c6\u03cc\u03c1\u03b7\u03c3\u03b7\u03c2<\/a><\/li>\n<li><a href=\"#tria-systatika-dspark\">\u03a4\u03b1 \u03c4\u03c1\u03af\u03b1 \u03c3\u03c5\u03c3\u03c4\u03b1\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf\u03c5 DSpark<\/a><\/li>\n<li><a href=\"#ekpaidefsi-draft-models\">\u03a0\u03ce\u03c2 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b1\u03bd \u03c4\u03b1 draft models<\/a><\/li>\n<li><a href=\"#idio-apotelesma-greedy-decoding\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u00ab\u03af\u03b4\u03b9\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1\u00bb \u03c3\u03c4\u03bf greedy decoding<\/a><\/li>\n<li><a href=\"#benchmarks-h100-m4-max\">\u03a4\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c3\u03b5 H100 \u03ba\u03b1\u03b9 M4 Max<\/a><\/li>\n<li><a href=\"#moe-m4-max\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf MoE \u03ba\u03ad\u03c1\u03b4\u03b9\u03c3\u03b5 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c3\u03c4\u03b7 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae<\/a><\/li>\n<li><a href=\"#function-calling-agents\">Function calling \u03ba\u03b1\u03b9 agentic workflows<\/a><\/li>\n<li><a href=\"#kostos-inference\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03c4\u03b1 \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/a><\/li>\n<li><a href=\"#plano-axiologisis\">\u03a5\u03c0\u03b5\u03cd\u03b8\u03c5\u03bd\u03bf \u03c0\u03bb\u03ac\u03bd\u03bf \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2<\/a><\/li>\n<li><a href=\"#ousia-eos-3-18x\">\u0397 \u03bf\u03c5\u03c3\u03af\u03b1 \u03c0\u03af\u03c3\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf \u00ab\u03ad\u03c9\u03c2 3,18\u00d7\u00bb<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"ti-kykloforise-liquid-ai\">\u03a4\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03ba\u03c5\u03ba\u03bb\u03bf\u03c6\u03cc\u03c1\u03b7\u03c3\u03b5 \u03b7 Liquid AI<\/h2>\n<p>\u0397 Liquid AI \u03ba\u03c5\u03ba\u03bb\u03bf\u03c6\u03cc\u03c1\u03b7\u03c3\u03b5 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac DSpark draft checkpoints \u03b3\u03b9\u03b1 \u03c4\u03b1 LFM2.5-1.2B-Instruct, LFM2.5-2.6B \u03ba\u03b1\u03b9 LFM2.5-8B-A1B. \u0394\u03b5\u03bd \u03c0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03bd\u03ad\u03b1 target models \u03bf\u03cd\u03c4\u03b5 \u03b3\u03b9\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c4\u03c9\u03bd \u03b2\u03b1\u03c3\u03b9\u03ba\u03ce\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd. \u039a\u03ac\u03b8\u03b5 drafter \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1 \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c5\u03c0\u03bf\u03c8\u03ae\u03c6\u03b9\u03c9\u03bd tokens \u03ba\u03b1\u03b9 \u03c4\u03bf \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf LFM2.5 \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03b6\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03ac.<\/p>\n<p>\u03a4\u03b1 checkpoints \u03b4\u03b9\u03b1\u03c4\u03af\u03b8\u03b5\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 Safetensors \u03ba\u03b1\u03b9 GGUF, \u03bc\u03b5 \u03c5\u03c0\u03bf\u03c3\u03c4\u03ae\u03c1\u03b9\u03be\u03b7 \u03b3\u03b9\u03b1 SGLang \u03c3\u03b5 GPU serving \u03ba\u03b1\u03b9 llama.cpp \u03c3\u03b5 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ad\u03c2. \u0391\u03c5\u03c4\u03ae \u03b7 \u03b4\u03b9\u03b1\u03b8\u03b5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf DSpark \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ac \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03b9\u03bc\u03bf \u03b1\u03c0\u03cc \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03ae\u03b4\u03b7 \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03c5\u03bd <a href=\"https:\/\/twodots.gr\/vllm-transformers-native-speed-ai-inference\/\">native-speed AI inference \u03bc\u03b5 \u03c3\u03cd\u03b3\u03c7\u03c1\u03bf\u03bd\u03b1 serving runtimes<\/a>, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03bf\u03c5\u03bd \u03b4\u03b9\u03ba\u03cc \u03c4\u03bf\u03c5\u03c2 speculative decoder \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03ae.<\/p>\n<p>\u03a4\u03b1 draft models \u03ad\u03c7\u03bf\u03c5\u03bd \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 295,7 \u03ad\u03c9\u03c2 327,7 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2. \u039f \u03c1\u03cc\u03bb\u03bf\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c4\u03b5\u03bd\u03cc\u03c2: \u03b4\u03b5\u03bd \u03b4\u03af\u03bd\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03b1\u03c5\u03c4\u03cc\u03bd\u03bf\u03bc\u03b7 \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03c4\u03bf\u03c5 target. \u0397 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03ac \u03c4\u03bf\u03c5\u03c2 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ad\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03cc\u03c3\u03bf \u03c3\u03c5\u03c7\u03bd\u03ac \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03bf\u03c5\u03bd tokens \u03c0\u03bf\u03c5 \u03c4\u03bf target \u03b8\u03b1 \u03b4\u03b5\u03c7\u03c4\u03b5\u03af \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b1\u03bd \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03c0\u03c1\u03cc\u03c4\u03b1\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03c2 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u03c7\u03c1\u03cc\u03bd\u03bf \u03c0\u03bf\u03c5 \u03b5\u03be\u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b5\u03af\u03c4\u03b1\u03b9.<\/p>\n<h2 id=\"decode-simeio-symforisis\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf decode \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03c3\u03c5\u03bc\u03c6\u03cc\u03c1\u03b7\u03c3\u03b7\u03c2<\/h2>\n<p>\u039a\u03b1\u03c4\u03ac \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03bc\u03b9\u03b1\u03c2 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2, \u03ad\u03bd\u03b1 language model \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03c3\u03c5\u03bd\u03ae\u03b8\u03c9\u03c2 \u03ad\u03bd\u03b1 token \u03ba\u03ac\u03b8\u03b5 \u03c6\u03bf\u03c1\u03ac. \u0397 \u03c6\u03ac\u03c3\u03b7 decode \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03c7\u03bd\u03ac memory-bound: \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03ba\u03b1\u03b8\u03c5\u03c3\u03c4\u03ad\u03c1\u03b7\u03c3\u03b7\u03c2 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03c9\u03bd \u03b2\u03b1\u03c1\u03ce\u03bd \u03b1\u03c0\u03cc DRAM \u03c0\u03c1\u03bf\u03c2 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc \u03ad\u03bb\u03bb\u03b5\u03b9\u03c8\u03b7 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ae\u03c2 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b9\u03c3\u03c7\u03cd\u03bf\u03c2. \u038c\u03c4\u03b1\u03bd \u03bf \u03af\u03b4\u03b9\u03bf\u03c2 \u03ba\u03cd\u03ba\u03bb\u03bf\u03c2 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 token, \u03c4\u03bf bandwidth \u03ba\u03b1\u03b9 \u03b7 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\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\u03bf\u03af \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af.<\/p>\n<p>\u03a4\u03bf speculative decoding \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03ad\u03bd\u03b1\u03bd \u03b5\u03bb\u03b1\u03c6\u03c1\u03cd\u03c4\u03b5\u03c1\u03bf drafter \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 tokens \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c3\u03c4\u03bf target model \u03bd\u03b1 \u03c4\u03b1 \u03b5\u03bb\u03ad\u03b3\u03be\u03b5\u03b9 \u03bc\u03b1\u03b6\u03af \u03c3\u03b5 \u03ad\u03bd\u03b1 forward pass. \u0391\u03bd \u03b1\u03c1\u03ba\u03b5\u03c4\u03ad\u03c2 \u03c0\u03c1\u03bf\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b3\u03af\u03bd\u03bf\u03c5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03ad\u03c2, \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c6\u03cc\u03c1\u03c4\u03c9\u03c3\u03b7\u03c2 \u03c4\u03c9\u03bd \u03b2\u03b1\u03c1\u03ce\u03bd \u03c3\u03b5 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b3\u03cc\u03bc\u03b5\u03bd\u03b1 tokens. \u0391\u03bd \u03bf\u03b9 \u03c0\u03c1\u03bf\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c0\u03bf\u03c1\u03c1\u03af\u03c0\u03c4\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c5\u03c7\u03bd\u03ac, \u03c4\u03bf \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03bf compute \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03c3\u03b5\u03b9 \u03ae \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03ba\u03c5\u03c1\u03ce\u03c3\u03b5\u03b9 \u03c4\u03bf \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2.<\/p>\n<p>\u0397 \u03af\u03b4\u03b9\u03b1 \u03b1\u03c1\u03c7\u03ae \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bc\u03bf\u03c1\u03c6\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03b5 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u03cc\u03c0\u03c9\u03c2 \u03c4\u03bf constrained decoding. \u0395\u03ba\u03b5\u03af, \u03cc\u03c0\u03c9\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 <a href=\"https:\/\/twodots.gr\/trie-automata-constrained-decoding-ai-systimata\/\">trie automata \u03b3\u03b9\u03b1 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 \u03b5\u03c0\u03b9\u03c4\u03c1\u03b5\u03c0\u03c4\u03ad\u03c2 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2<\/a>, \u03b7 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03c4\u03b7\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf latency \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c5\u03c0\u03bf\u03c7\u03c1\u03b5\u03c9\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<h2 id=\"tria-systatika-dspark\">\u03a4\u03b1 \u03c4\u03c1\u03af\u03b1 \u03c3\u03c5\u03c3\u03c4\u03b1\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf\u03c5 DSpark<\/h2>\n<p>\u03a4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1\u03c2 \u03c0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03bf\u03c2 \u03ba\u03bf\u03c1\u03bc\u03cc\u03c2 \u03c4\u03cd\u03c0\u03bf\u03c5 DFlash, \u03bf \u03bf\u03c0\u03bf\u03af\u03bf\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af context features \u03c4\u03bf\u03c5 target model \u03ba\u03b1\u03b9 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 hidden states \u03b3\u03b9\u03b1 \u03cc\u03bb\u03b1 \u03c4\u03b1 draft tokens \u03c3\u03b5 \u03ad\u03bd\u03b1 forward pass. \u0388\u03c4\u03c3\u03b9 \u03c4\u03bf \u03b2\u03b1\u03c1\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03c0\u03c1\u03cc\u03c4\u03b1\u03c3\u03b7\u03c2 \u03b4\u03b5\u03bd \u03b5\u03ba\u03c4\u03b5\u03bb\u03b5\u03af\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac token \u03c0\u03c1\u03bf\u03c2 token.<\/p>\n<p>\u03a4\u03bf \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03b5\u03bb\u03b1\u03c6\u03c1\u03b9\u03ac \u03b4\u03b9\u03b1\u03b4\u03bf\u03c7\u03b9\u03ba\u03ae \u03ba\u03b5\u03c6\u03b1\u03bb\u03ae, \u03bc\u03bf\u03bd\u03c4\u03b5\u03bb\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03b7 \u03c9\u03c2 \u03b1\u03bb\u03c5\u03c3\u03af\u03b4\u03b1 Markov \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03b3\u03b5\u03b9\u03c4\u03bf\u03bd\u03b9\u03ba\u03ce\u03bd tokens. \u0397 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03b1\u03c5\u03c4\u03ae \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03c4\u03ce\u03c3\u03b7 \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae\u03c2 \u03c0\u03c1\u03bf\u03c2 \u03c4\u03bf \u03c4\u03ad\u03bb\u03bf\u03c2 \u03bc\u03b9\u03b1\u03c2 \u03c0\u03c1\u03bf\u03c4\u03b5\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03b7\u03c2 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1\u03c2, \u03ad\u03bd\u03b1 \u03c3\u03c5\u03bd\u03b7\u03b8\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf \u03bc\u03b5\u03b9\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1 \u03c4\u03c9\u03bd \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 \u03c0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03c9\u03bd drafters.<\/p>\n<p>\u03a4\u03bf \u03c4\u03c1\u03af\u03c4\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf confidence-scheduled verification. \u039c\u03b9\u03b1 confidence head \u03b5\u03ba\u03c4\u03b9\u03bc\u03ac \u03c4\u03b7\u03bd \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03b2\u03b9\u03ce\u03c3\u03b5\u03b9 \u03ba\u03ac\u03b8\u03b5 draft token \u03ba\u03b1\u03b9 \u03bf scheduler \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03cc\u03c8\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03c7\u03b1\u03bc\u03b7\u03bb\u03ae\u03c2 \u03b5\u03bc\u03c0\u03b9\u03c3\u03c4\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 suffix \u03c0\u03c1\u03b9\u03bd \u03ba\u03b1\u03c4\u03b1\u03bd\u03b1\u03bb\u03c9\u03b8\u03b5\u03af \u03ac\u03c3\u03ba\u03bf\u03c0\u03b1 verification compute. \u0397 \u03c0\u03c1\u03c9\u03c4\u03bf\u03b3\u03b5\u03bd\u03ae\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 DSpark \u03c4\u03bf\u03bd\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03bc\u03ae\u03ba\u03bf\u03c2 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c0\u03c1\u03bf\u03c6\u03af\u03bb \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2 \u03c4\u03bf\u03c5 engine.<\/p>\n<div class=\"td-comparison\">\n<p class=\"td-comparison-title\">Fixed-length \u03ba\u03b1\u03b9 confidence-scheduled verification \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03ac\u03be\u03b7<\/p>\n<div class=\"td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid\">\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-kicker\">\u03a3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03bc\u03ae\u03ba\u03bf\u03c2<\/p>\n<h3>\u0395\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03b5\u03b9 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf draft block<\/h3>\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03cc, \u03b1\u03bb\u03bb\u03ac \u03c3\u03b5 \u03c5\u03c8\u03b7\u03bb\u03cc concurrency \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c0\u03b1\u03c4\u03b1\u03bb\u03ac batch capacity \u03c3\u03b5 \u03c7\u03b1\u03bc\u03b7\u03bb\u03ae\u03c2 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 suffix tokens \u03c0\u03bf\u03c5 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ac \u03b8\u03b1 \u03b1\u03c0\u03bf\u03c1\u03c1\u03b9\u03c6\u03b8\u03bf\u03cd\u03bd.<\/p>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<p class=\"td-comparison-kicker\">DSpark scheduling<\/p>\n<h3>\u039a\u03c1\u03b1\u03c4\u03ac \u03c4\u03bf \u03c5\u03c0\u03bf\u03c3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf prefix<\/h3>\n<p>\u03a3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 confidence \u03b1\u03bd\u03ac \u03b8\u03ad\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03bf\u03c5 engine, \u03ce\u03c3\u03c4\u03b5 \u03b7 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7 \u03bd\u03b1 \u03c3\u03c4\u03b1\u03bc\u03b1\u03c4\u03ac \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd token \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03bf\u03c3\u03c4\u03af\u03b6\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03cc\u03c3\u03bf \u03b5\u03be\u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b5\u03af.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"ekpaidefsi-draft-models\">\u03a0\u03ce\u03c2 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b1\u03bd \u03c4\u03b1 draft models<\/h2>\n<p>\u0397 Liquid AI \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03c4\u03b7 \u03c3\u03c5\u03bd\u03c4\u03b1\u03b3\u03ae DSpark \u03bc\u03b5 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 supervised fine-tuning, chat, code \u03ba\u03b1\u03b9 function calling. \u039f\u03b9 \u03c0\u03c1\u03ce\u03c4\u03b5\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 attention-only draft models \u03bc\u03b5 \u03c0\u03ad\u03bd\u03c4\u03b5 layers \u03ba\u03b1\u03b9 block size 9. \u0393\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03ad\u03b3\u03b9\u03bd\u03b1\u03bd 15 epochs \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03bb\u03ad\u03c7\u03b8\u03b7\u03ba\u03b5 \u03c4\u03bf checkpoint \u03bc\u03b5 \u03c4\u03bf \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf acceptance rate, \u03cc\u03c7\u03b9 \u03b5\u03ba\u03b5\u03af\u03bd\u03bf \u03bc\u03b5 \u03c4\u03bf \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf training loss.<\/p>\n<p>\u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b1\u03c5\u03c4\u03ae \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c3\u03c4\u03bf\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c1\u03cc\u03bb\u03bf \u03c4\u03bf\u03c5 drafter. \u0394\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b1\u03c5\u03c4\u03cc\u03bd\u03bf\u03bc\u03bf language model\u00b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c4\u03b7\u03bd \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1 \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03b4\u03b5\u03c7\u03c4\u03b5\u03af \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf target. \u039f decoder stack \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af \u03c3\u03b5 241,2 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03b7 hidden-state projection \u03c3\u03b5 21 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1. \u0397 Markov head \u03b5\u03af\u03bd\u03b1\u03b9 33,6 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03c3\u03c4\u03bf 1.2B-Instruct \u03ba\u03b1\u03b9 65,5 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03c3\u03c4\u03b1 \u03ac\u03bb\u03bb\u03b1 \u03b4\u03cd\u03bf checkpoints.<\/p>\n<div class=\"td-article-note\">\n<p><strong>\u0391\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03cc \u03cc\u03c1\u03b9\u03bf:<\/strong> \u03ad\u03bd\u03b1 DSpark checkpoint \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b5\u03bc\u03ad\u03bd\u03bf \u03bc\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf target model \u03ba\u03b1\u03b9 vocabulary. \u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc\u03c2 \u00ab\u03b5\u03c0\u03b9\u03c4\u03b1\u03c7\u03c5\u03bd\u03c4\u03ae\u03c2 AI\u00bb \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c5\u03b8\u03b1\u03af\u03c1\u03b5\u03c4\u03b1 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc LLM, quantization \u03ae runtime \u03c7\u03c9\u03c1\u03af\u03c2 \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bd\u03ad\u03b1 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7.<\/p>\n<\/div>\n<h2 id=\"idio-apotelesma-greedy-decoding\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u00ab\u03af\u03b4\u03b9\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1\u00bb \u03c3\u03c4\u03bf greedy decoding<\/h2>\n<p>\u03a3\u03c4\u03bf exact speculative setup \u03bc\u03b5 greedy decoding, \u03ad\u03bd\u03b1 draft token \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03b5\u03af \u03bc\u03b5 \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03c4\u03bf\u03c5 target model. \u0391\u03bd \u03b1\u03c0\u03bf\u03c1\u03c1\u03b9\u03c6\u03b8\u03b5\u03af, \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03c4\u03bf token \u03c4\u03bf\u03c5 \u03af\u03b4\u03b9\u03bf\u03c5 \u03c4\u03bf\u03c5 target. \u0397 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03af\u03b4\u03b9\u03b1 \u03bc\u03b5 \u03c4\u03b7 baseline greedy \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1 \u03b5\u03ba \u03ba\u03b1\u03c4\u03b1\u03c3\u03ba\u03b5\u03c5\u03ae\u03c2, \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03ad\u03c2 \u03cc\u03c0\u03c9\u03c2 pass@1 \u03ae exact match \u03b4\u03b5\u03bd \u03b1\u03bb\u03bb\u03ac\u03b6\u03bf\u03c5\u03bd \u03b5\u03be\u03b1\u03b9\u03c4\u03af\u03b1\u03c2 \u03c4\u03bf\u03c5 drafter.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03b1\u03c0\u03b5\u03af \u03c3\u03b5 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 sampling, backend, quantized build \u03ae \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ae \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03c0\u03ac\u03bd\u03c4\u03bf\u03c4\u03b5 byte-for-byte \u03af\u03b4\u03b9\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf. \u03a4\u03b1 benchmarks \u03c4\u03b7\u03c2 Liquid AI \u03ad\u03b3\u03b9\u03bd\u03b1\u03bd \u03bc\u03b5 temperature 0 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 SGLang \u03ba\u03b1\u03b9 llama.cpp. \u0397 \u03b9\u03c3\u03bf\u03b4\u03c5\u03bd\u03b1\u03bc\u03af\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 production configuration.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2, \u03c4\u03bf \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03be\u03b5\u03c4\u03ac\u03c3\u03b5\u03b9 \u03b3\u03c1\u03b7\u03b3\u03bf\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf serving path \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bc\u03ac\u03c4\u03c9\u03c2 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03bf\u03cd target model. \u03a0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03cc\u03bc\u03c9\u03c2 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b1 integration tests, logs, fallback \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03af\u03b4\u03b9\u03bf\u03b9 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03b9 \u03c0\u03bf\u03c5 \u03b1\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c0\u03c1\u03b9\u03bd <a href=\"https:\/\/twodots.gr\/rail-ai-4-erotimata-prin-tin-paragogi\/\">\u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03b5\u03c1\u03ac\u03c3\u03b5\u03b9 \u03b1\u03c0\u03cc \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c3\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae<\/a>.<\/p>\n<h2 id=\"benchmarks-h100-m4-max\">\u03a4\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c3\u03b5 H100 \u03ba\u03b1\u03b9 M4 Max<\/h2>\n<p>\u039f\u03b9 server \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03ad\u03b3\u03b9\u03bd\u03b1\u03bd \u03bc\u03b5 SGLang \u03c3\u03b5 \u03bc\u03af\u03b1 NVIDIA H100 80 GB \u03ba\u03b1\u03b9 BF16. \u039f\u03b9 on-device \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03ad\u03b3\u03b9\u03bd\u03b1\u03bd \u03bc\u03b5 llama.cpp \u03ba\u03b1\u03b9 Metal \u03c3\u03b5 MacBook Pro M4 Max, \u03bc\u03b5 FP16 GGUF weights. \u039a\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03b4\u03cd\u03bf \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd block size 9, batch size 1, temperature 0 \u03ba\u03b1\u03b9 \u03ad\u03c9\u03c2 256 output tokens. \u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03ba\u03ac\u03bb\u03c5\u03c8\u03b5 MATH500, HumanEval, MBPP, GSM8K \u03ba\u03b1\u03b9 MT-Bench.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-title\">\u03a4\u03bf vendor benchmark \u03c3\u03b5 \u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03ad\u03c2<\/p>\n<p class=\"td-chart-subtitle\">\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 Liquid AI \u03bc\u03b5 batch size 1 \u03ba\u03b1\u03b9 temperature 0. \u0395\u03af\u03bd\u03b1\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ac \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b5\u03c2 \u03b5\u03bd\u03b4\u03b5\u03af\u03be\u03b5\u03b9\u03c2 \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf setup, \u03cc\u03c7\u03b9 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b7 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 production \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2.<\/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\">3,18\u00d7<\/span><span class=\"td-metric-label\">\u039c\u03ad\u03b3\u03b9\u03c3\u03c4\u03bf \u03c3\u03b5 H100<\/span><span class=\"td-metric-note\">LFM2.5-8B-A1B \u03c3\u03c4\u03bf MATH500, 428 \u2192 1.362 tok\/s<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">2,87\u00d7<\/span><span class=\"td-metric-label\">\u039c\u03ad\u03b3\u03b9\u03c3\u03c4\u03bf \u03c3\u03b5 M4 Max<\/span><span class=\"td-metric-note\">LFM2.5-1.2B-Instruct \u03c3\u03c4\u03bf HumanEval, 136 \u2192 389 tok\/s<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">2,67\u00d7<\/span><span class=\"td-metric-label\">\u039c\u03ad\u03c3\u03bf\u03c2 \u03cc\u03c1\u03bf\u03c2 2.6B \u03c3\u03b5 H100<\/span><span class=\"td-metric-note\">323 \u2192 864 tok\/s \u03c3\u03c4\u03b1 \u03c0\u03ad\u03bd\u03c4\u03b5 datasets<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">1,18\u00d7<\/span><span class=\"td-metric-label\">\u039c\u03ad\u03c3\u03bf\u03c2 \u03cc\u03c1\u03bf\u03c2 8B-A1B \u03c3\u03b5 M4<\/span><span class=\"td-metric-note\">90 \u2192 106 tok\/s, \u03c0\u03b1\u03c1\u03ac \u03c4\u03bf \u03c5\u03c8\u03b7\u03bb\u03cc acceptance rate<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0393\u03b9\u03b1 \u03c4\u03bf LFM2.5-2.6B, \u03bf \u03bc\u03ad\u03c3\u03bf\u03c2 \u03c1\u03c5\u03b8\u03bc\u03cc\u03c2 \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 323 \u03c3\u03b5 864 tokens \u03b1\u03bd\u03ac \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf \u03c3\u03c4\u03b7\u03bd H100 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc 61 \u03c3\u03b5 139 \u03c3\u03c4\u03bf M4 Max. \u0393\u03b9\u03b1 \u03c4\u03bf LFM2.5-1.2B-Instruct, \u03bf\u03b9 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf\u03b9 \u03bc\u03ad\u03c3\u03bf\u03b9 \u03cc\u03c1\u03bf\u03b9 \u03ae\u03c4\u03b1\u03bd 2,10\u00d7 \u03ba\u03b1\u03b9 2,54\u00d7. \u0397 Liquid AI \u03b5\u03c0\u03b9\u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7 \u03b1\u03bd\u03ac dataset \u03b3\u03b9\u03b1 \u03c4\u03bf 1.2B \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c6\u03c4\u03ac\u03c3\u03b5\u03b9 \u03c4\u03bf 52%, \u03ac\u03c1\u03b1 \u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03ad\u03bd\u03c4\u03bf\u03bd\u03b1 \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-band-kicker\">\u0391\u03c0\u03cc benchmark \u03c3\u03b5 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7<\/p>\n<p><strong>\u03a4\u03bf 3,18\u00d7 \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af \u03ad\u03bd\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf pilot, \u03cc\u03c7\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b7 \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7 \u03c0\u03c1\u03bf\u03c2 \u03c0\u03b5\u03bb\u03ac\u03c4\u03b5\u03c2 \u03ae \u03b9\u03c3\u03cc\u03c0\u03bf\u03c3\u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae\u03c2.<\/strong> \u0397 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf target, \u03c4\u03b1 \u03af\u03b4\u03b9\u03b1 prompts, \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03be\u03cc\u03b4\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 latency, throughput, memory \u03ba\u03b1\u03b9 concurrency \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc runtime.<\/p>\n<\/div>\n<h2 id=\"moe-m4-max\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf MoE \u03ba\u03ad\u03c1\u03b4\u03b9\u03c3\u03b5 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c3\u03c4\u03b7 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae<\/h2>\n<p>\u03a4\u03bf LFM2.5-8B-A1B \u03b5\u03af\u03bd\u03b1\u03b9 Mixture of Experts. \u03a0\u03b1\u03c1\u03cc\u03c4\u03b9 \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b5 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf acceptance rate \u03b1\u03c0\u03cc \u03c4\u03b1 \u03b4\u03cd\u03bf dense models, \u03b7 \u03bc\u03ad\u03c3\u03b7 \u03b5\u03c0\u03b9\u03c4\u03ac\u03c7\u03c5\u03bd\u03c3\u03b7 \u03c3\u03c4\u03bf M4 Max \u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03c3\u03c4\u03bf 18%. \u0397 Liquid AI \u03b1\u03c0\u03bf\u03b4\u03af\u03b4\u03b5\u03b9 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03c3\u03c4\u03b7\u03bd \u03c4\u03c1\u03ad\u03c7\u03bf\u03c5\u03c3\u03b1 MoE \u03c5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 Metal backend \u03c3\u03c4\u03bf llama.cpp \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf \u03cc\u03c4\u03b9 \u03b7 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7 \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd tokens \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5\u03c2 experts, \u03c0\u03c1\u03bf\u03ba\u03b1\u03bb\u03ce\u03bd\u03c4\u03b1\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b7 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b2\u03b1\u03c1\u03ce\u03bd \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03b1\u03c0\u03bb\u03cc decode step.<\/p>\n<p>\u03a4\u03bf \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf acceptance rate \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03c9\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03ae. \u03a4\u03bf end-to-end \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 drafter, verifier, kernels, \u03bc\u03b5\u03c4\u03b1\u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, quantization \u03ba\u03b1\u03b9 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf\u03c5 target. \u03a4\u03bf \u03af\u03b4\u03b9\u03bf pattern \u03c3\u03c5\u03bd\u03b1\u03bd\u03c4\u03ac\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 <a href=\"https:\/\/twodots.gr\/ai-agent-harnesses-elegchos-kostos-self-hosting\/\">agent harnesses, self-hosting \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/a>: \u03ad\u03bd\u03b1 \u03b5\u03c0\u03b9\u03bc\u03ad\u03c1\u03bf\u03c5\u03c2 benchmark \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03c4\u03b7 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03b7\u03c2 \u03c4\u03b7\u03c2 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ae\u03c2.<\/p>\n<h2 id=\"function-calling-agents\">Function calling \u03ba\u03b1\u03b9 agentic workflows<\/h2>\n<p>\u0397 Liquid AI \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03bc\u03ad\u03c3\u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 57% \u03c3\u03c4\u03bf latency \u03c4\u03bf\u03c5 LFM2.5-2.6B \u03c3\u03b5 \u03b4\u03b9\u03ac\u03c6\u03bf\u03c1\u03b1 multi-tool \u03c3\u03b5\u03bd\u03ac\u03c1\u03b9\u03b1. \u03a4\u03bf \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03cc \u03bc\u03b5 agents, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03bc\u03b9\u03b1 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03bf\u03c5, \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 arguments, \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf \u03b2\u03ae\u03bc\u03b1. \u039c\u03b9\u03ba\u03c1\u03ad\u03c2 \u03ba\u03b1\u03b8\u03c5\u03c3\u03c4\u03b5\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03c3\u03c4\u03ac\u03b4\u03b9\u03bf \u03b1\u03b8\u03c1\u03bf\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03af\u03b1 \u03c4\u03bf\u03c5 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7.<\/p>\n<p>\u039f \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 vendor claim \u03b1\u03c0\u03cc \u03c4\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b4\u03bf\u03ba\u03b9\u03bc\u03ce\u03bd \u03c4\u03b7\u03c2 Liquid AI\u00b7 \u03b4\u03b5\u03bd \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b7\u03c2 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03cc agent workflow \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b9\u03c3\u03c7\u03cd\u03b5\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bc\u03ac\u03c4\u03c9\u03c2 \u03b3\u03b9\u03b1 \u03ac\u03bb\u03bb\u03b7 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae. \u0393\u03b9\u03b1 e-commerce, \u03c4\u03bf \u03c3\u03c9\u03c3\u03c4\u03cc test \u03b8\u03b1 \u03ae\u03c4\u03b1\u03bd \u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc\u03c2 \u03c7\u03c1\u03cc\u03bd\u03bf\u03c2 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b1\u03af\u03c4\u03b7\u03bc\u03b1 \u03ad\u03c9\u03c2 \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03bf\u03b8\u03ad\u03bc\u03b1\u03c4\u03bf\u03c2, \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03bf\u03c6\u03ce\u03bd \u03ba\u03b1\u03b9 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae\u03c2 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2. \u0393\u03b9\u03b1 marketing operations, \u03b8\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b1\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, \u03b7 \u03c3\u03cd\u03bd\u03b8\u03b5\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03b7 \u03b4\u03c1\u03bf\u03bc\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03bc\u03b9\u03b1\u03c2 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1\u03c2.<\/p>\n<p>\u0397 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03c5\u03c7\u03af\u03b5\u03c2 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03c9\u03bd, retries \u03ba\u03b1\u03b9 human approval, \u03cc\u03c0\u03c9\u03c2 \u03b5\u03c0\u03b9\u03b2\u03ac\u03bb\u03bb\u03b5\u03b9 \u03b7 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 \u03c4\u03c9\u03bd <a href=\"https:\/\/twodots.gr\/ai-agents-paragogi-leitourgikes-astochies\/\">AI agents \u03c3\u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae<\/a>. \u0388\u03bd\u03b1 \u03b3\u03c1\u03b7\u03b3\u03bf\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf token stream \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03b1\u03b8\u03bc\u03af\u03b6\u03b5\u03b9 \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b1 tool calls \u03ae \u03b1\u03bd\u03b5\u03c0\u03b1\u03c1\u03ba\u03ae \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03cd\u03b8\u03b7\u03c3\u03b7.<\/p>\n<h2 id=\"kostos-inference\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03c4\u03b1 \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/h2>\n<p>\u03a0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 tokens \u03b1\u03bd\u03ac \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b1\u03c5\u03be\u03ae\u03c3\u03bf\u03c5\u03bd \u03c4\u03b7 \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03bd\u03cc\u03c2 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03bf\u03cd hardware budget \u03ae \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03bf\u03c5\u03bd \u03c4\u03bf\u03bd \u03c7\u03c1\u03cc\u03bd\u03bf \u03b1\u03c0\u03b1\u03c3\u03c7\u03cc\u03bb\u03b7\u03c3\u03b7\u03c2 \u03bc\u03b9\u03b1\u03c2 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae\u03c2 \u03b1\u03bd\u03ac \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7. \u0397 \u03b4\u03b7\u03bc\u03bf\u03c3\u03af\u03b5\u03c5\u03c3\u03b7, \u03cc\u03bc\u03c9\u03c2, \u03b4\u03b5\u03bd \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 total cost of ownership, \u03ba\u03b1\u03c4\u03b1\u03bd\u03ac\u03bb\u03c9\u03c3\u03b7 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1\u03c2 \u03ae \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b1\u03bd\u03ac \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1. \u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03bf \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03b1\u03c0\u03b5\u03af \u03c4\u03bf 3,18\u00d7 \u03c3\u03b5 \u03b9\u03c3\u03cc\u03c0\u03bf\u03c3\u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2.<\/p>\n<p>\u039f drafter \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03bf\u03bb\u03c5\u03c0\u03bb\u03bf\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1. \u0397 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03af\u03b1 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b1\u03c0\u03cc utilization, batching, concurrency, \u03bc\u03ae\u03ba\u03bf\u03c2 prompt \u03ba\u03b1\u03b9 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2, acceptance rate, tail latency, retries \u03ba\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc overhead. \u039f\u03b9 \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03c5\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03bc\u03b5 batch size 1 \u03b4\u03b5\u03bd \u03b1\u03c0\u03b1\u03bd\u03c4\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc \u03bc\u03cc\u03bd\u03b5\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03c0\u03ce\u03c2 \u03b8\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03b5\u03c1\u03b8\u03b5\u03af \u03ad\u03bd\u03b1\u03c2 shared production server \u03c3\u03b5 \u03ce\u03c1\u03b5\u03c2 \u03b1\u03b9\u03c7\u03bc\u03ae\u03c2.<\/p>\n<div class=\"td-article-note\">\n<p><strong>\u039c\u03b5\u03c4\u03c1\u03b9\u03ba\u03ae \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7:<\/strong> \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03c4\u03b5 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03b7\u03bc\u03ad\u03bd\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u2014\u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1 \u03b1\u03bd\u03ac \u03c3\u03c9\u03c3\u03c4\u03ac \u03b5\u03c0\u03b9\u03bb\u03c5\u03bc\u03ad\u03bd\u03bf support \u03b1\u03af\u03c4\u03b7\u03bc\u03b1 \u03ae \u03b1\u03bd\u03ac \u03b5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03c0\u03b1\u03c1\u03b1\u03b4\u03bf\u03c4\u03ad\u03bf\u2014 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf peak tokens \u03b1\u03bd\u03ac \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf. \u0397 \u03af\u03b4\u03b9\u03b1 \u03c0\u03b5\u03b9\u03b8\u03b1\u03c1\u03c7\u03af\u03b1 \u03b1\u03c0\u03bf\u03c6\u03b5\u03cd\u03b3\u03b5\u03b9 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/kryfo-kostos-ai-coding-output-rework\/\">\u03ba\u03c1\u03c5\u03c6\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03c0\u03cc AI output \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1<\/a>.<\/p>\n<\/div>\n<h2 id=\"plano-axiologisis\">\u03a5\u03c0\u03b5\u03cd\u03b8\u03c5\u03bd\u03bf \u03c0\u03bb\u03ac\u03bd\u03bf \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2<\/h2>\n<p>\u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03b5\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac target model, prompts, sampling, hardware \u03ba\u03b1\u03b9 runtime. \u0388\u03c0\u03b5\u03b9\u03c4\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 baseline \u03ba\u03b1\u03b9 DSpark \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03bf\u03cd workload, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf. \u0393\u03b9\u03b1 local deployment, \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae \u03c3\u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf quantization \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 pilot LFM2.5-DSpark<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 1<\/span><strong>\u039a\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf baseline<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 target LFM2.5, runtime, quantization, sampling, context length \u03ba\u03b1\u03b9 serving flags \u03ce\u03c3\u03c4\u03b5 \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c8\u03b9\u03bc\u03b7.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 2<\/span><strong>\u03a7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac prompts<\/strong>\n<p>\u0394\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03ae\u03c3\u03c4\u03b5 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 \u03b3\u03b9\u03b1 chat, code, function calling, \u03bc\u03b1\u03ba\u03c1\u03b9\u03ad\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03cd\u03bf\u03c5\u03bd \u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 traffic.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 3<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf latency<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 time to first token, inter-token latency, \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03c7\u03c1\u03cc\u03bd\u03bf, throughput, p95 \u03ba\u03b1\u03b9 p99 \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03b5\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf peak tok\/s.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 4<\/span><strong>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 acceptance \u03ba\u03b1\u03b9 \u03bc\u03bd\u03ae\u03bc\u03b7<\/strong>\n<p>\u03a3\u03c5\u03bd\u03b4\u03ad\u03c3\u03c4\u03b5 acceptance rate \u03ba\u03b1\u03b9 accepted length \u03bc\u03b5 peak memory, drafter overhead \u03ba\u03b1\u03b9 \u03c4\u03b1 workloads \u03cc\u03c0\u03bf\u03c5 \u03c4\u03bf \u03ba\u03ad\u03c1\u03b4\u03bf\u03c2 \u03c5\u03c0\u03bf\u03c7\u03c9\u03c1\u03b5\u03af.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 5<\/span><strong>\u0394\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b5 \u03c4\u03bf production concurrency<\/strong>\n<p>\u0395\u03c0\u03b1\u03bd\u03b1\u03bb\u03ac\u03b2\u03b5\u03c4\u03b5 \u03c4\u03b9\u03c2 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b5 \u03c1\u03b5\u03b1\u03bb\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc batch \u03ba\u03b1\u03b9 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03bf\u03c5\u03c2 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b5\u03c2, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03b1 batch-size-one \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b4\u03b5\u03bd \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03bf\u03c5\u03bd \u03c4\u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c5\u03c0\u03cc \u03c6\u03bf\u03c1\u03c4\u03af\u03bf.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 6<\/span><strong>\u0395\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03c3\u03c4\u03b5 output \u03ba\u03b1\u03b9 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03b1<\/strong>\n<p>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 greedy outputs, function arguments, tool-call errors, retries \u03ba\u03b1\u03b9 \u03bf\u03bb\u03bf\u03ba\u03bb\u03ae\u03c1\u03c9\u03c3\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03c3\u03c4\u03b7\u03bd \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03b1\u03bd\u03b1\u03c0\u03c4\u03c5\u03c7\u03b8\u03b5\u03af.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 7<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 rollout \u03ba\u03b1\u03b9 fallback<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03c3\u03c4\u03b1\u03b4\u03b9\u03b1\u03ba\u03ae \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7, observability, budget \u03bf\u03c1\u03af\u03c9\u03bd \u03ba\u03b1\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b7 \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03bf\u03c6\u03ae \u03c3\u03c4\u03bf baseline \u03b1\u03bd latency, \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ae \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c7\u03b5\u03b9\u03c1\u03bf\u03c4\u03b5\u03c1\u03ad\u03c8\u03bf\u03c5\u03bd.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u0393\u03b9\u03b1 on-device agents, \u03b7 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03bb\u03ac\u03b2\u03b5\u03b9 \u03b8\u03b5\u03c1\u03bc\u03b9\u03ba\u03ae \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac, \u03b4\u03b9\u03ac\u03c1\u03ba\u03b5\u03b9\u03b1 \u03bc\u03c0\u03b1\u03c4\u03b1\u03c1\u03af\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03c4\u03b7\u03c2 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae\u03c2. \u03a4\u03bf \u03cc\u03c4\u03b9 <a href=\"https:\/\/twodots.gr\/muse-glimmer-topiki-multimodal-ai-agents\/\">\u03bf\u03b9 AI agents \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03bd \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac<\/a> \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 speculative configuration \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03bf \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 edge target.<\/p>\n<h2 id=\"ousia-eos-3-18x\">\u0397 \u03bf\u03c5\u03c3\u03af\u03b1 \u03c0\u03af\u03c3\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf \u00ab\u03ad\u03c9\u03c2 3,18\u00d7\u00bb<\/h2>\n<p>\u03a4\u03bf LFM2.5-DSpark \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 inference \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c0\u03bf\u03c3\u03bf\u03c4\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03ae \u03c3\u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5. \u0397 \u03bf\u03c1\u03b3\u03ac\u03bd\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 decoding \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7 \u03c3\u03c4\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03c5\u03be\u03ae\u03c3\u03b5\u03b9 \u03c4\u03bf throughput, \u03b5\u03bd\u03ce \u03c4\u03bf target model \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03bf \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc\u03c2 \u03ba\u03c1\u03b9\u03c4\u03ae\u03c2 \u03ba\u03ac\u03b8\u03b5 token \u03c3\u03c4\u03bf exact greedy setup.<\/p>\n<p>\u03a4\u03bf 3,18\u00d7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03bf LFM2.5-8B-A1B \u03c3\u03c4\u03bf MATH500 \u03ba\u03b1\u03b9 \u03c3\u03b5 \u03bc\u03af\u03b1 H100 80 GB\u00b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2 \u03cc\u03c1\u03bf\u03c2. \u039f\u03b9 \u03bc\u03ad\u03c3\u03bf\u03b9 \u03cc\u03c1\u03bf\u03b9 \u03ba\u03c5\u03bc\u03b1\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc 2,10\u00d7 \u03ad\u03c9\u03c2 2,67\u00d7 \u03c3\u03c4\u03b7\u03bd H100 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc 1,18\u00d7 \u03ad\u03c9\u03c2 2,54\u00d7 \u03c3\u03c4\u03bf M4 Max, \u03b1\u03bd\u03ac\u03bb\u03bf\u03b3\u03b1 \u03bc\u03b5 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u0391\u03c5\u03c4\u03ae \u03b7 \u03c0\u03bb\u03ae\u03c1\u03b7\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b9\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf headline, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 \u03c4\u03bf \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03bc\u03b5 dataset, architecture \u03ba\u03b1\u03b9 runtime.<\/p>\n<p>\u0393\u03b9\u03b1 decision makers, \u03c4\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf: \u03b1\u03be\u03af\u03b6\u03b5\u03b9 pilot \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf latency \u03ae \u03b7 \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b2\u03b1\u03c3\u03b9\u03c3\u03c4\u03b5\u03af \u03c3\u03b5 controlled benchmark \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2. \u0393\u03b9\u03b1 \u03c4\u03ad\u03c4\u03bf\u03b9\u03b1 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1, \u03b7 <a href=\"https:\/\/twodots.gr\/ai-agents-confidence-context-data-human-oversight\/\">\u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b5\u03c0\u03bf\u03c0\u03c4\u03b5\u03af\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c3\u03c9\u03c3\u03c4\u03cc context<\/a> \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03b5\u03be\u03af\u03c3\u03bf\u03c5 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b1 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">\u0410\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u0430\u0446\u0438\u044f \u043d\u0430 \u0431\u0438\u0437\u043d\u0435\u0441\u0430 \u0438 AI<\/p>\n<h3>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf AI inference \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03b7 \u03b4\u03b9\u03ba\u03ae \u03c3\u03b1\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03c1\u03bf\u03ae<\/h3>\n<p>\u0397 TWO DOTS \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03b5\u03b9 AI workflows, integrations \u03ba\u03b1\u03b9 observability \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac prompts, latency budgets, approval points \u03ba\u03b1\u03b9 fallback \u03c0\u03c1\u03b9\u03bd \u03c3\u03c5\u03bd\u03b4\u03b5\u03b8\u03bf\u03cd\u03bd \u03bc\u03b5 e-shop, CRM, ERP \u03ae customer support.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u0394\u03b5\u03af\u03c4\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd &amp; 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 LFM2.5-DSpark;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03bf\u03b9\u03ba\u03bf\u03b3\u03ad\u03bd\u03b5\u03b9\u03b1 \u03bc\u03b9\u03ba\u03c1\u03ce\u03bd draft model checkpoints \u03b3\u03b9\u03b1 \u03c4\u03c1\u03af\u03b1 LFM2.5. \u03a0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 tokens \u03ba\u03b1\u03b9 \u03c4\u03bf \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf target model \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b5\u03c2 \u03c0\u03c1\u03bf\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03ad\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0391\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03bf exact speculative setup \u03bc\u03b5 greedy decoding, \u03b7 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03af\u03b4\u03b9\u03b1 \u03bc\u03b5 \u03c4\u03bf\u03c5 target model, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03ba\u03ac\u03b8\u03b5 \u03c0\u03c1\u03bf\u03c4\u03b5\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf token \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03af\u03c3\u03c4\u03b1\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b1\u03c0\u03bf\u03c1\u03c1\u03af\u03c0\u03c4\u03b5\u03c4\u03b1\u03b9.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03cd \u03bc\u03b5\u03c4\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b7 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b5\u03c0\u03b9\u03c4\u03ac\u03c7\u03c5\u03bd\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03c5\u03bc\u03ad\u03bd\u03b7 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03ae\u03c4\u03b1\u03bd 3,18\u00d7 \u03b3\u03b9\u03b1 \u03c4\u03bf LFM2.5-8B-A1B \u03c3\u03c4\u03bf MATH500 \u03bc\u03b5 \u03bc\u03af\u03b1 H100 80 GB. \u03a3\u03c4\u03bf M4 Max \u03b7 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03ae\u03c4\u03b1\u03bd 2,87\u00d7 \u03b3\u03b9\u03b1 \u03c4\u03bf LFM2.5-1.2B-Instruct \u03c3\u03c4\u03bf HumanEval.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0399\u03c3\u03c7\u03cd\u03b5\u03b9 \u03c4\u03bf 3,18\u00d7 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0395\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5, dataset, hardware, batch size \u03ba\u03b1\u03b9 runtime. \u039f\u03b9 \u03bc\u03ad\u03c3\u03bf\u03b9 \u03cc\u03c1\u03bf\u03b9 \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03b5\u03c0\u03b9\u03bc\u03ad\u03c1\u03bf\u03c5\u03c2 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03b1\u03b9\u03c3\u03b8\u03b7\u03c4\u03ac.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 runtimes \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 Liquid AI \u03b4\u03b9\u03b1\u03b8\u03ad\u03c4\u03b5\u03b9 checkpoints \u03b3\u03b9\u03b1 SGLang \u03c3\u03b5 GPU serving \u03ba\u03b1\u03b9 GGUF \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 llama.cpp, \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03bf\u03bc\u03ad\u03bd\u03bf\u03c5 \u03c4\u03bf\u03c5 Metal backend \u03c3\u03b5 Apple silicon.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf MoE \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b5\u03af\u03c7\u03b5 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03ba\u03ad\u03c1\u03b4\u03bf\u03c2 \u03c3\u03c4\u03bf M4 Max;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 Liquid AI \u03c4\u03bf \u03b1\u03c0\u03bf\u03b4\u03af\u03b4\u03b5\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c4\u03c1\u03ad\u03c7\u03bf\u03c5\u03c3\u03b1 MoE \u03c5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 Metal backend \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b2\u03b1\u03c1\u03ce\u03bd \u03cc\u03c4\u03b1\u03bd \u03b7 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7 \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd tokens \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5\u03c2 experts.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c3\u03af\u03b3\u03bf\u03c5\u03c1\u03b1 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 inference;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1. \u0397 \u03b1\u03cd\u03be\u03b7\u03c3\u03b7 throughput \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b2\u03bf\u03b7\u03b8\u03ae\u03c3\u03b5\u03b9, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03bc\u03bd\u03ae\u03bc\u03b7, utilization, batching, concurrency, \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1, \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc overhead \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03bf\u03bb\u03bf\u03ba\u03bb\u03b7\u03c1\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03ce\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c4\u03bf \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039c\u03b5 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 baseline \u03ba\u03b1\u03b9 DSpark \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf target, hardware, runtime \u03ba\u03b1\u03b9 prompts. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 latency percentiles, throughput, memory, acceptance rate, function-call errors, \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03b1\u03c3\u03bc\u03ad\u03bd\u03bf fallback.<\/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:\/\/huggingface.co\/blog\/LiquidAI\/lfm25-dspark\" target=\"_blank\" rel=\"noopener\">Liquid AI, Up to 3.2x Faster Inference with LFM2.5-DSpark<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/LiquidAI\/LFM2.5-1.2B-Instruct-DSpark\" target=\"_blank\" rel=\"noopener\">Liquid AI, LFM2.5-1.2B-Instruct-DSpark model card<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/LiquidAI\/LFM2.5-2.6B-DSpark\" target=\"_blank\" rel=\"noopener\">Liquid AI, LFM2.5-2.6B-DSpark model card<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/LiquidAI\/LFM2.5-8B-A1B-DSpark\" target=\"_blank\" rel=\"noopener\">Liquid AI, LFM2.5-8B-A1B-DSpark model card<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2607.05147\" target=\"_blank\" rel=\"noopener\">Cheng et al., DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation<\/a><\/li>\n<li><a href=\"https:\/\/www.lmsys.org\/blog\/2026-07-06-dspark-sglang\/\" target=\"_blank\" rel=\"noopener\">LMSYS, DSpark in SGLang: confidence-driven variable-length verification<\/a><\/li>\n<li><a href=\"https:\/\/vllm-project.github.io\/2026\/08\/14\/dspark-adaptive-verification.html\" target=\"_blank\" rel=\"noopener\">vLLM, Adaptive Verification with DSpark<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf LFM2.5-DSpark \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c4\u03b1\u03b9 \u03ad\u03c9\u03c2 3,18\u00d7 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf AI inference \u03bc\u03b5 exact speculative decoding. \u0394\u03b5\u03af\u03c4\u03b5 \u03c0\u03ce\u03c2 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af, \u03c4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd H100 \u03ba\u03b1\u03b9 M4 Max \u03ba\u03b1\u03b9 \u03c0\u03ce\u03c2 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03c9\u03c3\u03c4\u03ac \u03c3\u03b5 production workload.<\/p>","protected":false},"author":1,"featured_media":89639,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[7336,19544,7203,19543,19545],"class_list":["post-89629","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ai-inference","tag-dspark","tag-edge-ai","tag-lfm2-5","tag-speculative-decoding"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":7}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/89629","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=89629"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/89629\/revisions"}],"predecessor-version":[{"id":89640,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/89629\/revisions\/89640"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media\/89639"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media?parent=89629"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/categories?post=89629"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/tags?post=89629"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}