{"id":98265,"date":"2026-10-09T10:45:46","date_gmt":"2026-10-09T07:45:46","guid":{"rendered":"https:\/\/twodots.gr\/?p=98265"},"modified":"2026-10-09T10:45:47","modified_gmt":"2026-10-09T07:45:47","slug":"perissoteres-gpus-mikrotero-kv-cache-kostos-llm-serving","status":"publish","type":"post","link":"https:\/\/twodots.gr\/en\/perissoteres-gpus-mikrotero-kv-cache-kostos-llm-serving\/","title":{"rendered":"\u03a0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 GPUs \u03ae \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf KV cache; \u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bb\u03bf\u03b3\u03b1\u03c1\u03b9\u03b1\u03c3\u03bc\u03cc\u03c2 \u03c4\u03bf\u03c5 LLM serving"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>Answer first:<\/strong> \u0391\u03bd \u03c4\u03b1 weights \u03b4\u03b5\u03bd \u03c7\u03c9\u03c1\u03bf\u03cd\u03bd, \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 GPUs\u00b7 \u03b1\u03bd \u03b4\u03b5\u03c3\u03bc\u03b5\u03cd\u03b5\u03b9 \u03c4\u03bf KV cache, \u03b7 \u03c3\u03c5\u03bc\u03c0\u03af\u03b5\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03bd\u03ae\u03b8\u03c9\u03c2 \u03bf \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03bf\u03c2 \u03c0\u03c1\u03ce\u03c4\u03bf\u03c2 \u03bc\u03bf\u03c7\u03bb\u03cc\u03c2\u00b7 \u03b1\u03bd \u03c4\u03bf SLA latency \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b5\u03ba\u03c4\u03cc\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c5, \u03c4\u03bf tensor parallelism \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b5 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf hardware bill.<\/p>\n<\/div>\n<p>\u03a4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 LLM serving \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc bottleneck: \u03c4\u03b1 weights, \u03c4\u03bf KV cache, \u03c4\u03bf latency, \u03c4\u03bf concurrency \u03ba\u03b1\u03b9 \u03c4\u03bf \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc quality floor. \u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03b5\u03c1\u03ce\u03c4\u03b7\u03bc\u03b1 \u00ab\u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 GPUs \u03ae \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf cache;\u00bb \u03b4\u03b5\u03bd \u03ad\u03c7\u03b5\u03b9 \u03bc\u03af\u03b1 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7. \u039f\u03b9 \u03b4\u03cd\u03bf \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03bf\u03c5\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03b9\u03b8\u03bf\u03cd\u03bd \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf workload.<\/p>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 <em>More GPUs or a Smaller Cache?<\/em> \u03b2\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf tensor parallelism \u03ba\u03b1\u03b9 \u03c4\u03bf KV cache compression \u03c3\u03c4\u03bf\u03bd \u03af\u03b4\u03b9\u03bf \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03cc \u03ac\u03be\u03bf\u03bd\u03b1: \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf tokens \u03c3\u03b5 \u03c3\u03c5\u03bd\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03bc\u03b5 latency, throughput \u03ba\u03b1\u03b9 memory relief. \u03a4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1\u03c2 \u03b1\u03c0\u03bb\u03cc\u03c2 \u03bd\u03b9\u03ba\u03b7\u03c4\u03ae\u03c2. \u0395\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1\u03c2 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc\u03c2 \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03c0\u03cc\u03c4\u03b5 \u03b7 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd GPU \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03af\u03b1, \u03c0\u03cc\u03c4\u03b5 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03cc\u03c4\u03b5 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae.<\/p>\n<p>\u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03bf\u03c5 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af self-hosted \u03ae dedicated LLMs \u03b3\u03b9\u03b1 \u03b1\u03bd\u03b1\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7, customer support, enrichment, content operations \u03ae AI agents. \u03a3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03bc\u03b5 \u03c4\u03bf \u03c0\u03ce\u03c2 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bc\u03b5 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/gpu-neoclouds-2026-ti-agorazeis-pera-apo-timi\/\">\u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bc\u03b9\u03b1\u03c2 GPU \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae\u03c2 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c4\u03b9\u03bc\u03ae \u03b1\u03bd\u03ac \u03ce\u03c1\u03b1<\/a> \u03ba\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03bf \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/ai-roi-axia-ergasias\/\">AI ROI \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ad\u03c4\u03b1\u03b9 \u03b1\u03bd\u03ac \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1<\/a>, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd\u03ac token.<\/p>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">Contents<\/div>\n<ul>\n<li><a href=\"#giati-sygkrisi-dyskoli\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03b7 \u03b1\u03bb\u03bb\u03ac \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03af\u03b1<\/a><\/li>\n<li><a href=\"#dyo-stratigikes\">\u03a4\u03b9 \u03ba\u03ac\u03bd\u03bf\u03c5\u03bd \u03bf\u03b9 \u03b4\u03cd\u03bf \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2<\/a><\/li>\n<li><a href=\"#methodologia-oria\">\u039c\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03af\u03b1 \u03ba\u03b1\u03b9 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03c9\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03ac\u03c4\u03c9\u03bd<\/a><\/li>\n<li><a href=\"#7b-kostos-latency\">\u03a3\u03c4\u03b1 7B, \u03b7 \u03c3\u03c5\u03bc\u03c0\u03af\u03b5\u03c3\u03b7 \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03c3\u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/a><\/li>\n<li><a href=\"#orio-megethos-montelou\">\u03a4\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03cc\u03c1\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5<\/a><\/li>\n<li><a href=\"#allazei-70b\">\u03a4\u03b9 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c3\u03c4\u03b1 70B<\/a><\/li>\n<li><a href=\"#fthinoteri-gpu\">\u0397 \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03b7 GPU \u03b4\u03b5\u03bd \u03b4\u03af\u03bd\u03b5\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf inference<\/a><\/li>\n<li><a href=\"#kanonas-epilogis\">\u03a0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc\u03c2 \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae\u03c2 \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 AI \u03bf\u03bc\u03ac\u03b4\u03b1<\/a><\/li>\n<li><a href=\"#business-workflows\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ac AI workflows<\/a><\/li>\n<li><a href=\"#metrics-parakolouthisi\">\u03a0\u03bf\u03b9\u03b1 metrics \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9<\/a><\/li>\n<li><a href=\"#periorismoi-symperasma\">\u03a0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03b1\u03b9 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"giati-sygkrisi-dyskoli\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03b7 \u03b1\u03bb\u03bb\u03ac \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03af\u03b1<\/h2>\n<p>\u039f\u03b9 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03b3\u03b9\u03b1 parallel scaling \u03c3\u03c5\u03bd\u03ae\u03b8\u03c9\u03c2 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd throughput \u03ba\u03b1\u03b9 latency \u03ba\u03b1\u03b8\u03ce\u03c2 \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2 \u03c4\u03c9\u03bd \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ce\u03bd. \u039f\u03b9 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03b3\u03b9\u03b1 KV compression, \u03b1\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03b5\u03c3\u03c4\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd \u03c3\u03c4\u03bf \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc \u03b5\u03be\u03bf\u03b9\u03ba\u03bf\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7\u03c2 \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2 \u03ae \u03c3\u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c3\u03b5 \u03ba\u03ac\u03c0\u03bf\u03b9\u03bf benchmark. \u0388\u03c4\u03c3\u03b9, \u03bf \u03b9\u03b4\u03b9\u03bf\u03ba\u03c4\u03ae\u03c4\u03b7\u03c2 \u03bc\u03b9\u03b1\u03c2 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae\u03c2 \u03b4\u03b5\u03bd \u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03b5\u03cd\u03ba\u03bf\u03bb\u03b1 \u03c0\u03cc\u03c3\u03bf \u03ba\u03bf\u03c3\u03c4\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03ad\u03bd\u03b1 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf tokens \u03cc\u03c4\u03b1\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b7\u03b8\u03bf\u03cd\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03b1 latency, \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2.<\/p>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03cc\u03c1\u03b9\u03c3\u03b1\u03bd \u03ad\u03bd\u03b1\u03bd \u03ba\u03bf\u03b9\u03bd\u03cc \u03b4\u03b5\u03af\u03ba\u03c4\u03b7: \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf tokens, \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c9\u03c1\u03b9\u03b1\u03af\u03b1 \u03c4\u03b9\u03bc\u03ae \u03ba\u03ac\u03b8\u03b5 GPU, \u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c4\u03c9\u03bd GPUs \u03ba\u03b1\u03b9 \u03c4\u03bf simulated throughput. \u03a3\u03c4\u03b7\u03bd \u03c0\u03bb\u03b5\u03c5\u03c1\u03ac \u03c4\u03bf\u03c5 tensor parallelism \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03b2\u03b1\u03b8\u03bc\u03bf\u03af 1, 2, 4 \u03ba\u03b1\u03b9 8. \u03a3\u03c4\u03b7\u03bd \u03c0\u03bb\u03b5\u03c5\u03c1\u03ac \u03c4\u03bf\u03c5 compression \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd KV bit-width 16, 8 \u03ba\u03b1\u03b9 4, \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 keep-ratios 1, 0,5 \u03ba\u03b1\u03b9 0,25.<\/p>\n<p>\u039f \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2 \u03c4\u03c9\u03bd GPUs \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03b1\u03c3\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf hardware cost. \u0391\u03bd \u03bf \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03b1\u03c3\u03b9\u03b1\u03c3\u03c4\u03ae\u03c2 \u03b1\u03c5\u03c4\u03cc\u03c2 \u03bb\u03b5\u03af\u03c0\u03b5\u03b9, \u03c4\u03bf scale-out \u03bc\u03bf\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ac \u03b4\u03c9\u03c1\u03b5\u03ac\u03bd \u03ba\u03b1\u03b9 \u03b7 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03c1\u03ad\u03c6\u03b5\u03c4\u03b1\u03b9. \u0395\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c3\u03c6\u03ac\u03bb\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf <a href=\"https:\/\/twodots.gr\/llm4llm-gpu-kernel-dokimi-pragmatiko-montelo\/\">GPU kernel \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03ad\u03be\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf<\/a>: \u03b7 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03b5\u03af\u03be\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf end-to-end \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1.<\/p>\n<h2 id=\"dyo-stratigikes\">\u03a4\u03b9 \u03ba\u03ac\u03bd\u03bf\u03c5\u03bd \u03bf\u03b9 \u03b4\u03cd\u03bf \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2<\/h2>\n<p>\u03a4\u03bf tensor parallelism \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b1 weights \u03ba\u03b1\u03b9 \u03c4\u03bf KV cache \u03c3\u03b5 \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 GPUs. \u0397 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03b1\u03bd\u03ac \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03cc\u03c3\u03bf \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bf \u03b2\u03b1\u03b8\u03bc\u03cc\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bb\u03bb\u03b7\u03bb\u03b9\u03c3\u03bc\u03bf\u03cd, \u03bc\u03b5 \u03cc\u03c1\u03b9\u03b1 \u03c0\u03bf\u03c5 \u03b5\u03be\u03b1\u03c1\u03c4\u03ce\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae attention. \u039a\u03ac\u03b8\u03b5 layer \u03cc\u03bc\u03c9\u03c2 \u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9 \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1 all-reduce, \u03b5\u03bd\u03ce \u03bf \u03bb\u03bf\u03b3\u03b1\u03c1\u03b9\u03b1\u03c3\u03bc\u03cc\u03c2 hardware \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c4\u03c9\u03bd \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ce\u03bd.<\/p>\n<p>\u03a4\u03bf KV cache compression \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03cd\u03c0\u03c9\u03bc\u03b1 \u03c4\u03c9\u03bd keys \u03ba\u03b1\u03b9 values \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 GPUs. \u03a4\u03bf quantization \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03b5\u03b9 \u03c4\u03b1 \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03c3\u03b5 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf bit-width, \u03b5\u03bd\u03ce \u03c4\u03bf eviction \u03ba\u03c1\u03b1\u03c4\u03ac \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03c9\u03bd tokens. \u0397 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b1 weights \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03c3\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ae \u03c4\u03bf per-token latency.<\/p>\n<div class=\"td-comparison td-comparison-cards--horizontal\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-kicker\">\u0394\u03cd\u03bf \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03b3\u03bf\u03c1\u03ad\u03c2<\/p>\n<p class=\"td-chart-title\">Tensor parallelism \u03ba\u03b1\u03b9 KV compression \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c5\u03c0\u03bf\u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c4\u03b1 \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd<\/p>\n<p class=\"td-chart-intro\">\u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u03c0\u03cc\u03c1\u03bf \u03c0\u03bf\u03c5 \u03b4\u03b5\u03c3\u03bc\u03b5\u03cd\u03b5\u03b9 \u03c4\u03bf deployment \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf SLA \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c4\u03b7\u03c1\u03b7\u03b8\u03b5\u03af.<\/p>\n<\/div>\n<div class=\"td-comparison-grid\">\n<div class=\"td-platform-card\">\n<p class=\"td-platform-name\">\u03a0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 GPUs<\/p>\n<p>\u03a7\u03c9\u03c1\u03bf\u03cd\u03bd \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 weights, \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf KV cache \u03ba\u03b1\u03b9 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03bf\u03c5\u03bd \u03c4\u03bf latency. \u03a0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03bf\u03c5\u03bd all-reduce \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1 \u03ba\u03b1\u03b9 \u03b1\u03c5\u03be\u03ac\u03bd\u03bf\u03c5\u03bd \u03ac\u03bc\u03b5\u03c3\u03b1 \u03c4\u03bf hardware bill.<\/p>\n<\/div>\n<div class=\"td-platform-card\">\n<p class=\"td-platform-name\">\u039c\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf KV cache<\/p>\n<p>\u0391\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 concurrent capacity \u03c7\u03c9\u03c1\u03af\u03c2 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ad\u03c2. \u0394\u03b5\u03bd \u03c7\u03c9\u03c1\u03ac \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c4\u03bf\u03c5 \u03bf\u03c0\u03bf\u03af\u03bf\u03c5 \u03c4\u03b1 weights \u03ae\u03b4\u03b7 \u03be\u03b5\u03c0\u03b5\u03c1\u03bd\u03bf\u03cd\u03bd \u03c4\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0397 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u03cc\u03c0\u03c9\u03c2 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/minima-kv-long-context-ai-cache-compression\/\">retention-aware KV cache compression<\/a> and the <a href=\"https:\/\/twodots.gr\/glide-yvridiki-prosochi-meionei-kostos-llm-megalou-context\/\">\u03c5\u03b2\u03c1\u03b9\u03b4\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae \u03b3\u03b9\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 contexts<\/a> \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b5\u03c2, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 \u03c4\u03bf\u03bd \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c4\u03c9\u03bd weights. Weight footprint \u03ba\u03b1\u03b9 KV footprint \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac.<\/p>\n<h2 id=\"methodologia-oria\">\u039c\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03af\u03b1 \u03ba\u03b1\u03b9 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03c9\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03ac\u03c4\u03c9\u03bd<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03c4\u03bf\u03bd profiled simulator Vidur. \u039f\u03b9 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03bf\u03af \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03c7\u03b1\u03bd \u03b4\u03b9\u03ba\u03ae \u03c4\u03bf\u03c5\u03c2 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7 \u03c3\u03b5 GPUs \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03ad\u03ba\u03b1\u03bd\u03b1\u03bd hardware measurements. \u03a4\u03b1 latency \u03ba\u03b1\u03b9 throughput \u03b5\u03af\u03bd\u03b1\u03b9 simulated \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 predictors \u03c0\u03bf\u03c5 \u03b5\u03af\u03c7\u03b1\u03bd \u03b2\u03b1\u03b8\u03bc\u03bf\u03bd\u03bf\u03bc\u03b7\u03b8\u03b5\u03af \u03b1\u03c0\u03cc \u03c4\u03bf\u03c5\u03c2 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03c2 \u03c4\u03bf\u03c5 Vidur \u03bc\u03b5 profiling \u03c3\u03b5 A100, A40 \u03ba\u03b1\u03b9 H100. \u0391\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03bf\u03b9 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03bf\u03af \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2 \u03ba\u03b1\u03b9 feasibility \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03bf\u03af.<\/p>\n<p>\u03a4\u03bf \u03ba\u03cd\u03c1\u03b9\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ae\u03c4\u03b1\u03bd \u03c4\u03bf Llama-2-7B \u03c3\u03b5 A100 80 GB. \u0397 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c5\u03bb\u03b9\u03ba\u03bf\u03cd \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c6\u03b8\u03b7\u03ba\u03b5 \u03c3\u03b5 A40 \u03ba\u03b1\u03b9 H100, \u03b5\u03bd\u03ce \u03c4\u03bf Llama-2-70B \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03b6\u03ce\u03bd\u03b7 \u03cc\u03c0\u03bf\u03c5 \u03c4\u03b1 fp16 weights \u03b4\u03b5\u03bd \u03c7\u03c9\u03c1\u03bf\u03cd\u03bd \u03c3\u03b5 \u03bc\u03af\u03b1 A100. \u03a4\u03bf \u03bc\u03ad\u03c4\u03c1\u03b9\u03bf interactive workload \u03b5\u03af\u03c7\u03b5 2.048 tokens prefill, 256 decode \u03ba\u03b1\u03b9 64 requests. \u03a4\u03bf \u03ba\u03bf\u03c1\u03b5\u03c3\u03bc\u03ad\u03bd\u03bf prefill-heavy workload \u03b5\u03af\u03c7\u03b5 3.840 prefill, 256 decode, 384 requests \u03ba\u03b1\u03b9 30 QPS.<\/p>\n<aside class=\"td-article-note\"><strong>\u039a\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03cc\u03c1\u03b9\u03bf \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7\u03c2:<\/strong> \u03c4\u03bf context \u03c3\u03c4\u03b1\u03bc\u03ac\u03c4\u03b7\u03c3\u03b5 \u03c3\u03c4\u03b1 4.096 tokens, \u03c4\u03bf batch cap \u03ae\u03c4\u03b1\u03bd 128, \u03c4\u03b1 workloads \u03ae\u03c4\u03b1\u03bd \u03c3\u03c5\u03bd\u03b8\u03b5\u03c4\u03b9\u03ba\u03ac \u03ba\u03b1\u03b9 \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf compression \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03b8\u03b7\u03ba\u03b5. \u039f\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03b5\u03af\u03bd\u03b1\u03b9 evidence \u03b3\u03b9\u03b1 \u03c4\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 configurations, \u03cc\u03c7\u03b9 \u03c4\u03b9\u03bc\u03bf\u03ba\u03b1\u03c4\u03ac\u03bb\u03bf\u03b3\u03bf\u03c2 \u03ae \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03cc\u03c2 \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 runtime.<\/aside>\n<p>\u0397 compression \u03c0\u03bb\u03b5\u03c5\u03c1\u03ac \u03bc\u03bf\u03bd\u03c4\u03b5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03c4\u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2, \u03cc\u03c7\u03b9 \u03c4\u03bf kernel-level \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03bf\u03c5 dequantization. \u03a0\u03b1\u03c1\u2019 \u03cc\u03bb\u03b1 \u03b1\u03c5\u03c4\u03ac, \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf admitted batch \u03b1\u03cd\u03be\u03b7\u03c3\u03b5 \u03c4\u03bf simulated time per output token \u03bc\u03ad\u03c3\u03c9 batching contention. \u0397 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03c3\u03b5\u03b9 INT8, INT4 \u03ae eviction \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03b5\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc runtime, \u03cc\u03c0\u03c9\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03c5\u03b9\u03bf\u03b8\u03b5\u03c4\u03b5\u03af <a href=\"https:\/\/twodots.gr\/vllm-transformers-native-speed-ai-inference\/\">native-speed inference \u03bc\u03b5 vLLM \u03ba\u03b1\u03b9 Transformers<\/a>.<\/p>\n<h2 id=\"7b-kostos-latency\">\u03a3\u03c4\u03b1 7B, \u03b7 \u03c3\u03c5\u03bc\u03c0\u03af\u03b5\u03c3\u03b7 \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03c3\u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u2014 \u03cc\u03c7\u03b9 \u03c3\u03c4\u03bf latency<\/h2>\n<p>\u03a3\u03c4\u03bf prefill-heavy workload \u03c4\u03bf\u03c5 Llama-2-7B \u03c3\u03b5 A100, \u03c4\u03bf fp16 \u03bc\u03b5 \u03bc\u03af\u03b1 GPU \u03ba\u03cc\u03c3\u03c4\u03b9\u03b6\u03b5 2,717 \u03b4\u03bf\u03bb\u03ac\u03c1\u03b9\u03b1 \u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf tokens. \u03a4\u03bf TP2 \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03c3\u03c4\u03b1 3,115, \u03c4\u03bf TP4 \u03c3\u03c4\u03b1 3,899 \u03ba\u03b1\u03b9 \u03c4\u03bf TP8 \u03c3\u03c4\u03b1 4,888. \u039f\u03b9 compressed \u03b4\u03b9\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03c3\u03b5\u03b9\u03c2 INT8, INT4 \u03ba\u03b1\u03b9 INT4 \u03bc\u03b5 keep-ratio 0,5 \u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03c3\u03b1\u03bd \u03c3\u03c4\u03b1 2,591 \u03b4\u03bf\u03bb\u03ac\u03c1\u03b9\u03b1, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf INT8 \u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ad\u03c0\u03b1\u03c8\u03b5 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf \u03b4\u03b5\u03c3\u03bc\u03b5\u03c5\u03c4\u03b9\u03ba\u03cc\u03c2 \u03c0\u03cc\u03c1\u03bf\u03c2.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-kicker\">Llama-2-7B \u00b7 A100 80 GB \u00b7 prefill-heavy workload<\/p>\n<p class=\"td-chart-title\">\u03a4\u03bf scale-out \u03b1\u03b3\u03cc\u03c1\u03b1\u03c3\u03b5 latency, \u03b7 \u03c3\u03c5\u03bc\u03c0\u03af\u03b5\u03c3\u03b7 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/p>\n<p class=\"td-chart-intro\">\u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 simulated \u03ba\u03b1\u03b9 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03c4\u03b7\u03bd \u03c5\u03c0\u03bf\u03b8\u03b5\u03c4\u03b9\u03ba\u03ae \u03c4\u03b9\u03bc\u03ae 2 \u03b4\u03bf\u03bb\u03ac\u03c1\u03b9\u03b1 \u03b1\u03bd\u03ac GPU-\u03ce\u03c1\u03b1 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2.<\/p>\n<\/div>\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">2,591 $<\/span><span class=\"td-metric-label\">\u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4. tokens \u03b3\u03b9\u03b1 INT8, INT4 \u03ba\u03b1\u03b9 INT4+k50 \u03c3\u03c4\u03bf TP1<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">4,888 $<\/span><span class=\"td-metric-label\">\u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4. tokens \u03b3\u03b9\u03b1 fp16 TP8<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">4,5\u00d7<\/span><span class=\"td-metric-label\">\u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf P99 TTFT \u03b1\u03c0\u03cc TP1 \u03c3\u03b5 TP8<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">16,5\u00d7<\/span><span class=\"td-metric-label\">capacity \u03b1\u03bd\u03ac \u03b4\u03bf\u03bb\u03ac\u03c1\u03b9\u03bf \u03bc\u03b5 \u03c4\u03b7 \u03b2\u03b1\u03b8\u03cd\u03c4\u03b5\u03c1\u03b7 compression \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7<\/span><\/div>\n<\/div>\n<\/div>\n<p>\u03a3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf setup, \u03c4\u03bf TP8 \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03c4\u03bf P99 time-to-first-token \u03b1\u03c0\u03cc 234,9 \u03c3\u03b5 52,4 \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03b1, \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 4,5 \u03c6\u03bf\u03c1\u03ad\u03c2. \u03a4\u03bf throughput \u03b1\u03c5\u03be\u03ae\u03b8\u03b7\u03ba\u03b5, \u03b1\u03bb\u03bb\u03ac \u03cc\u03c7\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c8\u03b5\u03b9 \u03c4\u03b7\u03bd \u03bf\u03ba\u03c4\u03b1\u03c0\u03bb\u03ac\u03c3\u03b9\u03b1 \u03c4\u03b9\u03bc\u03ae \u03c4\u03c9\u03bd \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ce\u03bd. \u03a3\u03b5 matched memory relief, \u03c4\u03bf compression \u03ae\u03c4\u03b1\u03bd \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf \u03ba\u03b1\u03c4\u03ac 1,20 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c3\u03c4\u03bf \u03bc\u03b9\u03c3\u03cc relief, 1,50 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c3\u03c4\u03bf \u03ad\u03bd\u03b1 \u03c4\u03ad\u03c4\u03b1\u03c1\u03c4\u03bf \u03ba\u03b1\u03b9 1,89 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c3\u03c4\u03bf \u03ad\u03bd\u03b1 \u03cc\u03b3\u03b4\u03bf\u03bf.<\/p>\n<p>\u03a4\u03bf latency trade-off \u03ae\u03c4\u03b1\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03b7 simulation. \u03a4\u03bf INT4 \u03b1\u03cd\u03be\u03b7\u03c3\u03b5 \u03c4\u03bf median time per output token \u03b1\u03c0\u03cc 72,79 \u03c3\u03b5 78,83 ms, \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 8%, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b7 \u03b5\u03bb\u03b5\u03cd\u03b8\u03b5\u03c1\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03b5\u03c0\u03ad\u03c4\u03c1\u03b5\u03c8\u03b5 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf batch \u03ba\u03b1\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf contention. \u0386\u03c1\u03b1 \u03b7 \u03c3\u03c5\u03bc\u03c0\u03af\u03b5\u03c3\u03b7 \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03b9 capacity\u00b7 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 latency optimization. \u0393\u03b9\u03b1 latency-sensitive serving, \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03cc\u03c0\u03c9\u03c2 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/llm-routing-latency-accuracy-cost\/\">LLM routing \u03bc\u03b5 \u03ba\u03bf\u03b9\u03bd\u03cc \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf latency, accuracy \u03ba\u03b1\u03b9 cost<\/a>.<\/p>\n<h2 id=\"orio-megethos-montelou\">\u03a4\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03cc\u03c1\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5<\/h2>\n<p>\u0393\u03b9\u03b1 \u03c4\u03bf Llama-2-7B \u03c3\u03b5 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae 80 GB, \u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae\u03c2 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc\u03c2 \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 29 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03c9\u03bd requests \u03c3\u03c4\u03bf \u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03bf context \u03c4\u03c9\u03bd 4.096 tokens. \u0397 \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 \u03b8\u03b1 \u03b1\u03b4\u03c5\u03bd\u03b1\u03c4\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03b4\u03b5\u03c7\u03b8\u03b5\u03af \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 request \u03bc\u03cc\u03bd\u03bf \u03c3\u03c4\u03b1 131.072 tokens, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae 32 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf context window \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5. \u03a3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf setup, \u03c4\u03bf 7B \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03b5\u03be\u03b1\u03bd\u03c4\u03bb\u03ae\u03c3\u03b5\u03b9 \u03c4\u03bf\u03bd KV \u03c0\u03c1\u03bf\u03cb\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03b1 \u03b4\u03b9\u03ba\u03ac \u03c4\u03bf\u03c5 \u03cc\u03c1\u03b9\u03b1.<\/p>\n<p>\u0397 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae \u03b3\u03c1\u03b1\u03bc\u03bc\u03ae \u03c7\u03c9\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd \u03b2\u03c1\u03ad\u03b8\u03b7\u03ba\u03b5 \u03c3\u03c4\u03b1 weights. \u039c\u03b5 usable budget 72 GB \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc safety margin 10%, \u03c4\u03bf CodeLlama-34B \u03bc\u03b5 61,9 GB fp16 weights \u03c7\u03c9\u03c1\u03bf\u03cd\u03c3\u03b5 \u03c3\u03b5 \u03bc\u03af\u03b1 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae, \u03b5\u03bd\u03ce \u03c4\u03b1 Llama-2-70B \u03ba\u03b1\u03b9 Llama-3-70B \u03b1\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03c3\u03b1\u03bd 127,5 GB. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c4\u03bf\u03cd\u03bd \u03c4\u03bf \u03cc\u03c1\u03b9\u03bf \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03c3\u03c4\u03b1 36B parameters \u03b3\u03b9\u03b1 fp16 weights \u03c3\u03b5 \u03ba\u03ac\u03c1\u03c4\u03b1 80 GB.<\/p>\n<p>\u03a4\u03bf 36B \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03cc\u03c2 \u03bd\u03cc\u03bc\u03bf\u03c2. \u0395\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03c4\u03bf\u03c5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 memory model \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03b1\u03ba\u03b9\u03bd\u03b5\u03af\u03c4\u03b1\u03b9 \u03bc\u03b5 weight quantization, \u03ac\u03bb\u03bb\u03b7 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae, \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc runtime \u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc safety margin. \u0397 \u03b1\u03c1\u03c7\u03ae \u03cc\u03bc\u03c9\u03c2 \u03bc\u03ad\u03bd\u03b5\u03b9: \u03b1\u03bd \u03b4\u03b5\u03c3\u03bc\u03b5\u03cd\u03bf\u03c5\u03bd \u03c4\u03b1 weights, \u03c4\u03bf KV compression \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b5\u03c6\u03b9\u03ba\u03c4\u03cc.<\/p>\n<h2 id=\"allazei-70b\">\u03a4\u03b9 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c3\u03c4\u03b1 70B<\/h2>\n<p>\u03a3\u03c4\u03bf Llama-2-70B, \u03c4\u03bf TP1 \u03ae\u03c4\u03b1\u03bd \u03b1\u03bd\u03ad\u03c6\u03b9\u03ba\u03c4\u03bf \u03ba\u03b1\u03b9 \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03be\u03b5\u03ba\u03af\u03bd\u03b7\u03c3\u03b5 \u03b1\u03c0\u03cc TP2. \u0397 fp16 TP2 \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03c3\u03b5 \u03bc\u03b5 99% \u03ad\u03c9\u03c2 100% KV occupancy \u03ba\u03b1\u03b9 \u03ba\u03c1\u03b1\u03c4\u03bf\u03cd\u03c3\u03b5 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 13 concurrent requests. \u0395\u03ba\u03b5\u03af \u03c4\u03bf compression \u03ad\u03c0\u03b1\u03c8\u03b5 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03b4\u03c1\u03b1\u03bd\u03ad\u03c2: \u03c4\u03bf INT8 \u03ad\u03b4\u03c9\u03c3\u03b5 68,3 tokens \u03b1\u03bd\u03ac \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf \u03ba\u03b1\u03b9 \u03c4\u03bf INT4 71,8, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 53,3 \u03b3\u03b9\u03b1 fp16 TP2.<\/p>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 16,268 \u03b4\u03bf\u03bb\u03ac\u03c1\u03b9\u03b1 \u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf tokens \u03b3\u03b9\u03b1 INT8 TP2 \u03ba\u03b1\u03b9 15,475 \u03b3\u03b9\u03b1 INT4 TP2, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 17,950 \u03b3\u03b9\u03b1 fp16 TP4 \u03ba\u03b1\u03b9 24,089 \u03b3\u03b9\u03b1 fp16 TP8 \u03c3\u03c4\u03bf prefill-heavy workload. \u03a4\u03bf compression \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03bf \u03c3\u03c4\u03b1 matched relief \u03c3\u03b7\u03bc\u03b5\u03af\u03b1, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf \u03b5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf \u03b5\u03c6\u03b9\u03ba\u03c4\u03cc TP2 \u03b4\u03b5\u03bd \u03ae\u03c4\u03b1\u03bd \u03c4\u03bf \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf parallel configuration.<\/p>\n<p>\u03a3\u03c4\u03bf interactive workload, \u03c4\u03bf \u03b2\u03ae\u03bc\u03b1 \u03b1\u03c0\u03cc TP2 \u03c3\u03b5 TP4 \u03ad\u03b4\u03c9\u03c3\u03b5 3,38 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf throughput \u03b3\u03b9\u03b1 \u03b4\u03b9\u03c0\u03bb\u03ac\u03c3\u03b9\u03b1 \u03c4\u03b9\u03bc\u03ae \u03ba\u03b1\u03b9 \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac token \u03ba\u03b1\u03c4\u03ac 41%. \u03a3\u03c4\u03bf prefill-heavy workload \u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03ae\u03c4\u03b1\u03bd 14%. \u0391\u03c5\u03c4\u03cc \u03c4\u03bf superlinear \u03ba\u03ad\u03c1\u03b4\u03bf\u03c2 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03bf TP4 \u03b1\u03c0\u03b5\u03bb\u03b5\u03c5\u03b8\u03ad\u03c1\u03c9\u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd \u03b4\u03b5\u03c3\u03bc\u03b5\u03c5\u03c4\u03b9\u03ba\u03cc \u03c0\u03cc\u03c1\u03bf\u00b7 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf TP4, \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03c5\u03be\u03ae\u03b8\u03b7\u03ba\u03b5 \u03be\u03b1\u03bd\u03ac.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-kicker\">Serving capacity decision<\/p>\n<p class=\"td-decision-title\">\u03a4\u03bf \u03b5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf \u03b5\u03c6\u03b9\u03ba\u03c4\u03cc configuration \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03bf\u03c4\u03b5 \u03c4\u03bf \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03bf<\/p>\n<p>\u0391\u03bd \u03c4\u03bf p<sub>min<\/sub> \u03bc\u03cc\u03bb\u03b9\u03c2 \u03c7\u03c9\u03c1\u03ac \u03c4\u03b1 weights \u03b1\u03bb\u03bb\u03ac \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 memory-saturated, \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b5 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03bc\u03ad\u03c3\u03c9\u03c2 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b2\u03b1\u03b8\u03bc\u03af\u03b4\u03b1 tensor parallelism. \u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03b1\u03bd \u03b7 \u03b1\u03c0\u03b5\u03bb\u03b5\u03c5\u03b8\u03ad\u03c1\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 bottleneck \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf throughput \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03cc\u03c3\u03bf \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf hardware bill\u00b7 \u03bc\u03b5\u03c4\u03ac \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 \u03be\u03b1\u03bd\u03ac \u03bc\u03b5 compression \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf quality floor.<\/p>\n<\/div>\n<h2 id=\"fthinoteri-gpu\">\u0397 \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03b7 GPU \u03b4\u03b5\u03bd \u03b4\u03af\u03bd\u03b5\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf inference<\/h2>\n<p>\u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 A40, A100 \u03ba\u03b1\u03b9 H100 \u03ad\u03b4\u03c9\u03c3\u03b5 \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ad\u03bd\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1. \u039c\u03b5 \u03c4\u03b9\u03c2 \u03c5\u03c0\u03bf\u03b8\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c9\u03c1\u03b9\u03b1\u03af\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03c4\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2 \u2014 1,00, 2,00 \u03ba\u03b1\u03b9 3,50 \u03b4\u03bf\u03bb\u03ac\u03c1\u03b9\u03b1 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1 \u2014 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf tokens \u03c4\u03b1\u03be\u03b9\u03bd\u03bf\u03bc\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03c1\u03bf\u03c6\u03b1: H100 1,96, A100 2,59 \u03ba\u03b1\u03b9 A40 3,29 \u03b4\u03bf\u03bb\u03ac\u03c1\u03b9\u03b1.<\/p>\n<p>\u0397 \u03c7\u03b1\u03bc\u03b7\u03bb\u03ae \u03c9\u03c1\u03b9\u03b1\u03af\u03b1 \u03c4\u03b9\u03bc\u03ae \u03c4\u03b7\u03c2 A40 \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03ac\u03b8\u03bc\u03b9\u03c3\u03b5 \u03c4\u03bf \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf throughput \u03c4\u03b7\u03c2. \u0395\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd, \u03b7 A40 \u03ae\u03c4\u03b1\u03bd memory-bound \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 7B \u03bc\u03b5 99% occupancy \u03c3\u03c4\u03bf fp16, \u03bf\u03c0\u03cc\u03c4\u03b5 \u03c4\u03bf INT4 \u03b1\u03c0\u03ad\u03b4\u03c9\u03c3\u03b5 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf INT8. \u03a4\u03bf hardware model \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03b7\u03bd \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1, \u03b1\u03bb\u03bb\u03ac \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03c3\u03c4\u03bf \u03bf\u03c0\u03bf\u03af\u03bf \u03b7 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd \u03c3\u03c5\u03bc\u03c0\u03af\u03b5\u03c3\u03b7 \u03ad\u03b3\u03b9\u03bd\u03b5 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 H100 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03b7 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03b3\u03bf\u03c1\u03ac. \u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03ae\u03c4\u03b1\u03bd round-number assumptions \u03ba\u03b1\u03b9 \u03b7 \u03b4\u03b9\u03b1\u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03b9\u03ba\u03ae \u03c3\u03b5\u03b9\u03c1\u03ac \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03b2\u03cc\u03bb\u03b1\u03b9\u03bf, \u03c4\u03b9\u03c2 \u03b5\u03ba\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2, \u03c4\u03bf interconnect, \u03c4\u03bf workload \u03ba\u03b1\u03b9 \u03c4\u03bf software stack. \u03a4\u03bf lesson \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b1\u03b3\u03bf\u03c1\u03ac \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c4\u03b1\u03bc\u03b1\u03c4\u03ac \u03c3\u03c4\u03bf hourly rate, \u03cc\u03c0\u03c9\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03b7 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b3\u03b9\u03b1 <a href=\"https:\/\/twodots.gr\/neoclouds-ai-ypodomes-isorropia-cloud\/\">neoclouds \u03ba\u03b1\u03b9 AI \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ad\u03c2<\/a>.<\/p>\n<h2 id=\"kanonas-epilogis\">\u03a0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc\u03c2 \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae\u03c2 \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 AI \u03bf\u03bc\u03ac\u03b4\u03b1<\/h2>\n<p>\u0397 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bf\u03c1\u03b3\u03b1\u03bd\u03c9\u03b8\u03b5\u03af \u03c9\u03c2 \u03c3\u03b5\u03b9\u03c1\u03ac \u03b5\u03bb\u03ad\u03b3\u03c7\u03c9\u03bd \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c9\u03c2 \u03c0\u03c1\u03bf\u03c4\u03af\u03bc\u03b7\u03c3\u03b7 \u03c3\u03b5 \u03bc\u03af\u03b1 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae. \u03a0\u03c1\u03ce\u03c4\u03b1 \u03b5\u03be\u03b1\u03c3\u03c6\u03b1\u03bb\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 feasibility, \u03bc\u03b5\u03c4\u03ac \u03bc\u03b5\u03c4\u03c1\u03b9\u03ad\u03c4\u03b1\u03b9 saturation, \u03cd\u03c3\u03c4\u03b5\u03c1\u03b1 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 optimization \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 latency, \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf \u03c4\u03ad\u03bb\u03bf\u03c2 \u03b5\u03bb\u03ad\u03b3\u03c7\u03b5\u03c4\u03b1\u03b9 \u03b1\u03bd \u03c4\u03bf quality floor \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0391\u03c0\u03cc \u03c4\u03bf bottleneck \u03c3\u03c4\u03bf \u03c3\u03c9\u03c3\u03c4\u03cc LLM serving configuration<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">Step 1<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac weights \u03ba\u03b1\u03b9 KV footprint<\/strong>\n<p>\u03a5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03c3\u03c4\u03b5 fp16 \u03ae quantized weights, KV bytes \u03b1\u03bd\u03ac token, context, concurrency, runtime overhead \u03ba\u03b1\u03b9 \u03c1\u03b5\u03b1\u03bb\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc safety margin.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 2<\/span><strong>\u0392\u03c1\u03b5\u03af\u03c4\u03b5 \u03c4\u03bf \u03b5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf \u03b5\u03c6\u03b9\u03ba\u03c4\u03cc TP degree<\/strong>\n<p>\u0391\u03bd \u03c4\u03b1 weights \u03b4\u03b5\u03bd \u03c7\u03c9\u03c1\u03bf\u03cd\u03bd, \u03b1\u03c5\u03be\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf tensor parallelism \u03bc\u03ad\u03c7\u03c1\u03b9 \u03bd\u03b1 \u03c7\u03c9\u03c1\u03bf\u03cd\u03bd weights \u03ba\u03b1\u03b9 KV cache \u03c7\u03c9\u03c1\u03af\u03c2 OOM.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 3<\/span><strong>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03b1\u03bd \u03c4\u03bf p<sub>min<\/sub> \u03b5\u03af\u03bd\u03b1\u03b9 memory-saturated<\/strong>\n<p>\u0391\u03bd \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03c4\u03bf 100% occupancy, \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b5 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b2\u03b1\u03b8\u03bc\u03af\u03b4\u03b1, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03ce\u03c3\u03b5\u03b9 superlinear throughput \u03ba\u03b1\u03b9 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac token.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 4<\/span><strong>\u03a3\u03c5\u03bc\u03c0\u03b9\u03ad\u03c3\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03b4\u03b5\u03c3\u03bc\u03b5\u03cd\u03b5\u03b9 \u03c4\u03bf KV cache<\/strong>\n<p>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 INT8, INT4 \u03ba\u03b1\u03b9 eviction \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf workload. \u0391\u03bd \u03c4\u03bf cache \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 binding, \u03b7 \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03b7 compression \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03b4\u03c1\u03b1\u03bd\u03ae\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 5<\/span><strong>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03c4\u03bf SLA latency<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 P50 \u03ba\u03b1\u03b9 P99 TTFT \u03ba\u03b1\u03b9 TPOT. \u0391\u03bd \u03c4\u03bf SLA \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03c4\u03c5\u03b3\u03c7\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9, \u03c4\u03bf tensor parallelism \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03af\u03bf\u03c2 \u03bc\u03bf\u03c7\u03bb\u03cc\u03c2, \u03bc\u03b5 \u03c3\u03c5\u03bd\u03b5\u03b9\u03b4\u03b7\u03c4\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 6<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 quality floor \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 dataset<\/strong>\n<p>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 accuracy, retrieval, groundedness \u03ba\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b1 failure modes \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03c7\u03b8\u03b5\u03af\u03c4\u03b5 low-bit KV \u03ae \u03b5\u03c0\u03b9\u03b8\u03b5\u03c4\u03b9\u03ba\u03cc eviction.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 7<\/span><strong>\u0395\u03c0\u03b1\u03bd\u03b1\u03ba\u03bf\u03c3\u03c4\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03c4\u03b5 \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 cloud rate, interconnect, utilization, idle time \u03ba\u03b1\u03b9 operational overhead, \u03cc\u03c7\u03b9 \u03c4\u03b9\u03c2 \u03c5\u03c0\u03bf\u03b8\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03c4\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u039f \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03af\u03b5\u03b9 \u03ac\u03bb\u03bb\u03bf\u03c5\u03c2 \u03bc\u03bf\u03c7\u03bb\u03bf\u03cd\u03c2. Speculative decoding, efficient attention, routing \u03ba\u03b1\u03b9 weight quantization \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c1\u03b8\u03bf\u03b3\u03ce\u03bd\u03b9\u03b5\u03c2 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2. \u0393\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/multimodal-speculative-decoding-parallili-ai\/\">speculative decoding<\/a> \u03c3\u03c4\u03bf\u03c7\u03b5\u03cd\u03b5\u03b9 \u03c3\u03c4\u03bf\u03bd \u03c7\u03c1\u03cc\u03bd\u03bf \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2, \u03b5\u03bd\u03ce \u03b7 KV compression \u03c3\u03c4\u03bf footprint \u03c4\u03bf\u03c5 state. \u039a\u03ac\u03b8\u03b5 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf metric \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ac\u03b3\u03bc\u03b1\u03c4\u03b9 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9.<\/p>\n<h2 id=\"business-workflows\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ac AI workflows<\/h2>\n<p>\u03a3\u03b5 product enrichment \u03ae campaign generation, \u03ad\u03bd\u03b1 SLA \u03bc\u03b5 \u03bc\u03b9\u03ba\u03c1\u03ae \u03b1\u03bd\u03bf\u03c7\u03ae \u03ba\u03b1\u03b8\u03c5\u03c3\u03c4\u03ad\u03c1\u03b7\u03c3\u03b7\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 batches \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c5\u03bd\u03bf\u03b5\u03af \u03c4\u03b7 \u03c3\u03c5\u03bc\u03c0\u03af\u03b5\u03c3\u03b7. \u03a3\u03b5 live customer support, \u03cc\u03c0\u03bf\u03c5 \u03bf \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7\u03c2 \u03b1\u03bd\u03c4\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03c4\u03bf TTFT, \u03c4\u03bf parallelism \u03ae \u03c4\u03bf routing \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03b7 \u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03b7 \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03bd\u03ac \u03b4\u03bf\u03bb\u03ac\u03c1\u03b9\u03bf.<\/p>\n<p>\u03a3\u03b5 workloads \u03bc\u03b5 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf prefix, \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7 cache \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03c0\u03cc \u03bc\u03b9\u03b1 \u03bd\u03ad\u03b1 GPU. \u03a4\u03bf <a href=\"https:\/\/twodots.gr\/kvboost-taxytera-llm-koino-periexomeno-oxi-stin-arxi\/\">KVBoost \u03b3\u03b9\u03b1 \u03ba\u03bf\u03b9\u03bd\u03cc \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03ae<\/a> \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf pattern \u03c4\u03c9\u03bd prompts \u03ad\u03c7\u03b5\u03b9 \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1. \u0391\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 70B \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b4\u03b5\u03bd \u03c7\u03c9\u03c1\u03ac \u03ba\u03b1\u03bd, \u03b7 \u03c3\u03c5\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7 \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03b1\u03c0\u03cc feasibility \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc prompt optimization.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 e-commerce \u03ae marketing, \u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 \u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03b1: customer journey, quality floor, workload shape \u03ba\u03b1\u03b9 infrastructure economics. \u0388\u03bd\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf cache \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03ba\u03ae \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03af\u03b1 \u03b1\u03bd \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03c4\u03c9\u03bd \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03c9\u03bd \u03ae \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03b1 escalations. \u039c\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd GPU \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03ba\u03b1\u03ba\u03ae \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03af\u03b1 \u03b1\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03b9 latency \u03c0\u03bf\u03c5 \u03bf \u03c0\u03b5\u03bb\u03ac\u03c4\u03b7\u03c2 \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9.<\/p>\n<h2 id=\"metrics-parakolouthisi\">\u03a0\u03bf\u03b9\u03b1 metrics \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9<\/h2>\n<p>\u0388\u03bd\u03b1 dashboard \u03c0\u03bf\u03c5 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf GPU utilization \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03c0\u03bf\u03b9\u03bf\u03c2 \u03c0\u03cc\u03c1\u03bf\u03c2 \u03b4\u03b5\u03c3\u03bc\u03b5\u03cd\u03b5\u03b9. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 TTFT \u03ba\u03b1\u03b9 TPOT \u03c3\u03b5 P50\/P99, throughput, KV occupancy, concurrent requests, OOM failures, batch size, queueing time \u03ba\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf input \u03ba\u03b1\u03b9 output tokens.<\/p>\n<p>\u0397 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf report: task success, groundedness, retrieval hit rate, fallback rate, \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b1 escalations \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 rework. \u0394\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac, \u03bc\u03b9\u03b1 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 INT4 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c3\u03c4\u03b7\u03bd \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae \u03b1\u03bb\u03bb\u03ac \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c3\u03c4\u03b7\u03bd \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1. \u0391\u03c5\u03c4\u03cc\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf \u03af\u03b4\u03b9\u03bf\u03c2 \u03bb\u03cc\u03b3\u03bf\u03c2 \u03c0\u03bf\u03c5 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/kryfo-kostos-ai-coding-output-rework\/\">\u03ba\u03c1\u03c5\u03c6\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03bf\u03c5 AI output<\/a> \u03b4\u03b5\u03bd \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03b1\u03c0\u03bb\u03cc token bill.<\/p>\n<p>\u03a4\u03ad\u03bb\u03bf\u03c2, \u03c4\u03b1 metrics \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03cc\u03b2\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bd\u03ac workload: \u03bc\u03b9\u03ba\u03c1\u03ac chat requests, long-context retrieval, batch enrichment \u03ba\u03b1\u03b9 agentic flows \u03b4\u03b5\u03bd \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf bottleneck. \u0397 \u03bc\u03af\u03b1 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ae \u03bc\u03ad\u03c3\u03b7 \u03c4\u03b9\u03bc\u03ae \u03ba\u03c1\u03cd\u03b2\u03b5\u03b9 \u03c4\u03b1 saturation regimes \u03c3\u03c4\u03b1 \u03bf\u03c0\u03bf\u03af\u03b1 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7.<\/p>\n<h2 id=\"periorismoi-symperasma\">\u03a0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03b1\u03b9 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1<\/h2>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b4\u03b5\u03bd \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b5 hardware \u03bc\u03b5 \u03b4\u03b9\u03ba\u03ad\u03c2 \u03c4\u03b7\u03c2 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ad\u03c2, \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b5 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1, \u03b4\u03b5\u03bd \u03bc\u03bf\u03bd\u03c4\u03b5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03c4\u03bf dequantization overhead \u03c4\u03c9\u03bd low-bit kernels \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03c3\u03b5 long-context \u03ba\u03b1\u03b8\u03b5\u03c3\u03c4\u03ce\u03c2 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc 4K. \u03a4\u03bf batch cap \u03b4\u03ad\u03c3\u03bc\u03b5\u03c5\u03c3\u03b5 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b4\u03b9\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03c3\u03b5\u03b9\u03c2, \u03c4\u03bf 70B \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03bc\u03cc\u03bd\u03bf \u03c3\u03b5 A100 \u03ba\u03b1\u03b9 \u03b7 A40 \u03b4\u03b5\u03bd \u03b5\u03af\u03c7\u03b5 all-reduce profiling \u03b3\u03b9\u03b1 TP \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc 1.<\/p>\n<p>\u039f\u03b9 \u03c9\u03c1\u03b9\u03b1\u03af\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03ae\u03c4\u03b1\u03bd \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03c5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c5\u03c0\u03bf\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2, \u03cc\u03c7\u03b9 \u03c3\u03c5\u03bc\u03b2\u03cc\u03bb\u03b1\u03b9\u03b1. \u03a4\u03bf \u03cc\u03c1\u03b9\u03bf \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 36B \u03b1\u03c6\u03bf\u03c1\u03ac fp16 weights \u03c3\u03b5 usable budget 72 GB. \u0397 weight quantization \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c4\u03bf \u03bc\u03b5\u03c4\u03b1\u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9, \u03b5\u03bd\u03ce \u03bd\u03ad\u03b1 kernels \u03ba\u03b1\u03b9 runtimes \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03bf\u03c5\u03bd \u03c4\u03b7 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7. \u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03bd \u03c9\u03c2 \u03c5\u03c0\u03cc\u03b8\u03b5\u03c3\u03b7 \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03bf\u03cd \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 profiling.<\/p>\n<p>\u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03b8\u03b5\u03ba\u03c4\u03b9\u03ba\u03cc: \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 GPUs \u03ba\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf KV cache \u03b4\u03b5\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c0\u03c1\u03ac\u03b3\u03bc\u03b1. \u03a4\u03bf tensor parallelism \u03bb\u03cd\u03bd\u03b5\u03b9 feasibility \u03ba\u03b1\u03b9 latency. \u03a4\u03bf compression \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03b9 capacity \u03bc\u03b5 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf hardware cost, \u03cc\u03c4\u03b1\u03bd \u03c4\u03b1 weights \u03ae\u03b4\u03b7 \u03c7\u03c9\u03c1\u03bf\u03cd\u03bd \u03ba\u03b1\u03b9 \u03c4\u03bf quality floor \u03c4\u03bf \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9. \u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03b1\u03c0\u03cc \u03c4\u03bf bottleneck \u03ba\u03b1\u03b9 \u03c4\u03b5\u03bb\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03bc\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">AI \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af \u03bc\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf serving cost<\/p>\n<p class=\"td-service-cta-title\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 \u03c4\u03bf AI workflow \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc bottleneck<\/p>\n<p>\u0397 TWO DOTS \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 workload, context, concurrency, quality floor, latency \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf\u03c5\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03bf\u03cd\u03c2 \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2.<\/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 \u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u0395\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\">Frequently Asked Questions (FAQs)<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf KV cache;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03c0\u03bf\u03c5 \u03ba\u03c1\u03b1\u03c4\u03ac \u03c4\u03b1 key \u03ba\u03b1\u03b9 value tensors \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03c9\u03bd tokens, \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03bc\u03b7\u03bd \u03c4\u03b1 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03b6\u03b5\u03b9 \u03be\u03b1\u03bd\u03ac \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03b2\u03ae\u03bc\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf tensor parallelism;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae weights, \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03ce\u03bd \u03ba\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c5\u03c2 \u03c4\u03bf\u03c5 KV cache \u03c3\u03b5 \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 GPUs. \u039c\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03b1\u03bd\u03ac \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae, \u03b1\u03bb\u03bb\u03ac \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1 \u03ba\u03b1\u03b9 hardware cost.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03bb\u03ad\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf KV compression \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0389\u03c4\u03b1\u03bd \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf \u03c3\u03c4\u03b1 matched memory-relief \u03c3\u03b5\u03bd\u03ac\u03c1\u03b9\u03b1, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b1 weights, \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03ae\u03c4\u03b1\u03bd \u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c2 \u03bc\u03bf\u03c7\u03bb\u03cc\u03c2 \u03b3\u03b9\u03b1 latency.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03cc\u03c4\u03b5 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03af\u03bf \u03c4\u03bf tensor parallelism;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c4\u03b1\u03bd \u03c4\u03b1 weights \u03b4\u03b5\u03bd \u03c7\u03c9\u03c1\u03bf\u03cd\u03bd \u03c3\u03b5 \u03bc\u03af\u03b1 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae \u03ae \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf deployment \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b5\u03c4\u03cd\u03c7\u03b5\u03b9 latency \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b7 \u03c3\u03c5\u03bc\u03c0\u03af\u03b5\u03c3\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c1\u03b9\u03bf \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 36B parameters;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03cc\u03c0\u03bf\u03c5 fp16 weights \u03c0\u03b1\u03cd\u03bf\u03c5\u03bd \u03bd\u03b1 \u03c7\u03c9\u03c1\u03bf\u03cd\u03bd \u03c3\u03c4\u03bf usable budget \u03c4\u03c9\u03bd 72 GB \u03bc\u03b9\u03b1\u03c2 \u03ba\u03ac\u03c1\u03c4\u03b1\u03c2 80 GB \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf setup. \u039c\u03b5 weight quantization \u03ae \u03ac\u03bb\u03bb\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7, \u03c4\u03bf \u03cc\u03c1\u03b9\u03bf \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03b7 \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03b7 GPU \u03bd\u03b1 \u03ba\u03bf\u03c3\u03c4\u03af\u03b6\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03bd\u03ac token;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039d\u03b1\u03b9. \u03a3\u03c4\u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7, \u03b7 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c9\u03c1\u03b9\u03b1\u03af\u03b1 \u03c4\u03b9\u03bc\u03ae \u03c4\u03b7\u03c2 A40 \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03ac\u03b8\u03bc\u03b9\u03c3\u03b5 \u03c4\u03bf \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf throughput, \u03bf\u03c0\u03cc\u03c4\u03b5 \u03b5\u03af\u03c7\u03b5 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf tokens \u03b1\u03c0\u03cc A100 \u03ba\u03b1\u03b9 H100.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 metrics \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03ac \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>TTFT, TPOT, throughput, KV occupancy, concurrent requests, OOM failures, queueing, quality \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c4\u03b7\u03c2 dataset \u03ba\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf tokens.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03bf \u03ba\u03b5\u03bd\u03cc \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 \u03b1\u03c0\u03bf\u03c5\u03c3\u03af\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf compression. \u03a7\u03c9\u03c1\u03af\u03c2 \u03b1\u03c5\u03c4\u03ae\u03bd, \u03c4\u03b1 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03ac \u03bf\u03c6\u03ad\u03bb\u03b7 \u03b9\u03c3\u03c7\u03cd\u03bf\u03c5\u03bd \u03bc\u03cc\u03bd\u03bf \u03b5\u03c6\u03cc\u03c3\u03bf\u03bd \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b1\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf floor.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">Sources<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2608.23962\" target=\"_blank\" rel=\"noopener\">Tumkur et al. \u2014 More GPUs or a Smaller Cache? Tensor Parallelism versus KV Compression for Memory-Bound LLM Serving, arXiv<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2405.05465\" target=\"_blank\" rel=\"noopener\">Agrawal et al. \u2014 Vidur: A Large-Scale Simulation Framework for LLM Inference, arXiv<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2402.02750\" target=\"_blank\" rel=\"noopener\">Liu et al. \u2014 KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache, arXiv<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2306.14048\" target=\"_blank\" rel=\"noopener\">Zhang et al. \u2014 H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models, arXiv<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 LLM serving \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc bottleneck: \u03c4\u03b1 weights, \u03c4\u03bf KV cache, \u03c4\u03bf latency, \u03c4\u03bf concurrency \u03ba\u03b1\u03b9 \u03c4\u03bf \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc quality floor.<\/p>","protected":false},"author":1,"featured_media":106025,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","footnotes":""},"categories":[199],"tags":[8988,20984,20246,20982,20983],"class_list":["post-98265","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ai-infrastructure","tag-gpu-cost","tag-kv-cache","tag-llm-serving","tag-tensor-parallelism"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/98265","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/comments?post=98265"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/98265\/revisions"}],"predecessor-version":[{"id":106026,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/98265\/revisions\/106026"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media\/106025"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media?parent=98265"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/categories?post=98265"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/tags?post=98265"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}