{"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\/ro\/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\">\u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af \u0395\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd &amp; AI<\/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\">\u00centreb\u0103ri frecvente<\/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\">\u03a0\u03b7\u03b3\u03ad\u03c2<\/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\/ro\/wp-json\/wp\/v2\/posts\/89629","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/comments?post=89629"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/posts\/89629\/revisions"}],"predecessor-version":[{"id":89640,"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/posts\/89629\/revisions\/89640"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/media\/89639"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/media?parent=89629"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/categories?post=89629"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/ro\/wp-json\/wp\/v2\/tags?post=89629"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}