{"id":87616,"date":"2026-07-30T16:44:43","date_gmt":"2026-07-30T13:44:43","guid":{"rendered":"https:\/\/twodots.gr\/?p=87616"},"modified":"2026-07-30T16:44:43","modified_gmt":"2026-07-30T13:44:43","slug":"glide-yvridiki-prosochi-meionei-kostos-llm-megalou-context","status":"publish","type":"post","link":"https:\/\/twodots.gr\/en\/glide-yvridiki-prosochi-meionei-kostos-llm-megalou-context\/","title":{"rendered":"GLIDE: \u03c0\u03ce\u03c2 \u03b7 \u03c5\u03b2\u03c1\u03b9\u03b4\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03c9\u03bd LLM \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf\u03c5 context"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u03a4\u03bf GLIDE \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03b2\u03ac\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 KV cache \u03ba\u03b1\u03c4\u03b1\u03bd\u03ad\u03bc\u03bf\u03bd\u03c4\u03b1\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc softmax \u03ba\u03b1\u03b9 \u03c4\u03bf \u03b1\u03c0\u03bf\u03b4\u03bf\u03c4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03bf linear attention \u03ba\u03b1\u03c4\u03ac \u03bc\u03ae\u03ba\u03bf\u03c2 \u03c4\u03c9\u03bd layers \u03b5\u03bd\u03cc\u03c2 LLM.<\/strong> \u0394\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf softmax \u03c3\u03c4\u03b1 \u03c0\u03c1\u03ce\u03c4\u03b1, \u03c0\u03b9\u03bf \u03b5\u03c5\u03b1\u03af\u03c3\u03b8\u03b7\u03c4\u03b1 layers, \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03bc\u03b9\u03ba\u03c4\u03cc \u03c3\u03c7\u03ae\u03bc\u03b1 \u03c3\u03c4\u03b7 \u03bc\u03ad\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c3\u03c4\u03b1 \u03b2\u03b1\u03b8\u03cd\u03c4\u03b5\u03c1\u03b1 layers.<\/p>\n<p>\u03a3\u03c4\u03b9\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2, \u03b1\u03c5\u03c4\u03ae \u03b7 \u03bc\u03b7 \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03b4\u03b7\u03bc\u03b9\u03bf\u03cd\u03c1\u03b3\u03b7\u03c3\u03b5 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf trade-off \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1, memory I\/O \u03ba\u03b1\u03b9 latency \u03b1\u03c0\u03cc \u03bc\u03b9\u03b1 \u03b5\u03bd\u03b9\u03b1\u03af\u03b1 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae \u03b3\u03b9\u03b1 \u03cc\u03bb\u03bf \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03c4\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u00ab\u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf cache \u03bc\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2\u00bb, \u03b1\u03bb\u03bb\u03ac \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae configuration \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload \u03ba\u03b1\u03b9 \u03c3\u03b5 \u03c3\u03b1\u03c6\u03ad\u03c2 \u03cc\u03c1\u03b9\u03bf \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2.<\/p>\n<\/div>\n<div class=\"td-article-note\">\n<p><strong>\u03a3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03cc\u03c1\u03b9\u03bf:<\/strong> \u03c4\u03bf GLIDE \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf, \u03cc\u03c7\u03b9 \u03ad\u03c4\u03bf\u03b9\u03bc\u03b7 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03c3\u03b5 \u03ad\u03bd\u03b1 SaaS dashboard. \u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03b4\u03cd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 7\u20138B, \u03ad\u03be\u03b9 benchmarks \u03ba\u03b1\u03b9 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03c5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c3\u03b5 NVIDIA GH200 120GB\u00b7 \u03c4\u03b1 speedups \u03ba\u03b1\u03b9 \u03b7 serving capacity \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bd\u03ad\u03b1 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03cc stack.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">Contents<\/div>\n<ul>\n<li><a href=\"#kv-cache-steno-simeio-long-context\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf KV cache \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c4\u03bf \u03c3\u03c4\u03b5\u03bd\u03cc \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03c3\u03c4\u03bf long-context inference<\/a><\/li>\n<li><a href=\"#eviction-kai-retention\">\u039f\u03b9 \u03b4\u03cd\u03bf \u03c3\u03c5\u03bd\u03b7\u03b8\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2: eviction \u03ba\u03b1\u03b9 retention<\/a><\/li>\n<li><a href=\"#layers-eyaisthisia-grammikopoiisi\">\u0397 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03c0\u03b1\u03c1\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7: \u03c4\u03b1 layers \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03b4\u03c1\u03bf\u03cd\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c3\u03c4\u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7<\/a><\/li>\n<li><a href=\"#pos-leitourgei-guided-layerwise-hybrid-attention\">\u03a0\u03ce\u03c2 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03b7 Guided Layerwise Hybrid Attention<\/a><\/li>\n<li><a href=\"#block-wise-rythmisi-ana-layer\">\u0393\u03b9\u03b1\u03c4\u03af \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03c4\u03b1\u03b9 block-wise \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03b1\u03bd\u03ac layer<\/a><\/li>\n<li><a href=\"#dokimes-llama-3-8b-mistral-7b\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03bf\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c3\u03b5 Llama-3-8B \u03ba\u03b1\u03b9 Mistral-7B<\/a><\/li>\n<li><a href=\"#lora-fine-tuning-anaktisi-poiotitas\">\u039f \u03c1\u03cc\u03bb\u03bf\u03c2 \u03c4\u03bf\u03c5 LoRA fine-tuning \u03c3\u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2<\/a><\/li>\n<li><a href=\"#latency-out-of-memory-megala-contexts\">Latency, out-of-memory \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03c3\u03b5 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 contexts<\/a><\/li>\n<li><a href=\"#akribeia-kv-cache-io\">\u03a4\u03b9 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b7 \u03b1\u03bd\u03c4\u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1\u03c2 \u03ba\u03b1\u03b9 KV-cache I\/O<\/a><\/li>\n<li><a href=\"#epicheiriseis-ai-ypodomi\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03bf\u03c5\u03bd \u03ae \u03c7\u03c4\u03af\u03b6\u03bf\u03c5\u03bd AI \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae<\/a><\/li>\n<li><a href=\"#oria-meletis-glide\">\u03a4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03b7\u03bb\u03ce\u03bd\u03b5\u03b9 \u03b7 \u03af\u03b4\u03b9\u03b1 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7<\/a><\/li>\n<li><a href=\"#ousia-glide-pera-apo-arithmous\">\u0397 \u03bf\u03c5\u03c3\u03af\u03b1 \u03c4\u03bf\u03c5 GLIDE \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf\u03c5\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03cd\u03c2<\/a><\/li>\n<\/ul>\n<\/div>\n<p>\u038c\u03c3\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf context \u03b5\u03bd\u03cc\u03c2 Large Language Model, \u03c4\u03cc\u03c3\u03bf \u03c0\u03b9\u03bf \u03ad\u03bd\u03c4\u03bf\u03bd\u03bf \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03bf\u03b8\u03cc\u03bd\u03b7 \u03c4\u03bf\u03c5 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7: \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03bd\u03ad\u03bf token, \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03b5\u03b9 \u03cc\u03bb\u03bf \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf Key-Value cache. \u0397 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ae \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 <strong>GLIDE: Guided Layerwise Hybrid Attention for Efficient LLM Inference<\/strong> \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03c3\u03c5\u03bc\u03c6\u03cc\u03c1\u03b7\u03c3\u03b7\u03c2. \u0397 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ae \u03c4\u03b7\u03c2 \u03b9\u03b4\u03ad\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03cc\u03bb\u03b1 \u03c4\u03b1 layers \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03c4\u03bf\u03bd \u03af\u03b4\u03b9\u03bf \u03c4\u03cd\u03c0\u03bf attention \u03bf\u03cd\u03c4\u03b5 \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7\u03c2 \u03c3\u03c4\u03bf \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf context.<\/p>\n<p>\u039f\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 Vimal William, Ravi Tandon \u03ba\u03b1\u03b9 Jyotikrishna Dass \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03bc\u03b9\u03b1 \u03c5\u03b2\u03c1\u03b9\u03b4\u03b9\u03ba\u03ae \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03c0\u03bf\u03c5 \u03ba\u03c1\u03b1\u03c4\u03ac \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf softmax attention \u03c3\u03c4\u03b1 \u03c0\u03c1\u03ce\u03c4\u03b1 layers \u03ba\u03b1\u03b9 \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c3\u03c4\u03b1\u03b4\u03b9\u03b1\u03ba\u03ac \u03c4\u03bf linear attention \u03c3\u03c4\u03b1 \u03b2\u03b1\u03b8\u03cd\u03c4\u03b5\u03c1\u03b1 layers. \u0397 \u03c0\u03c1\u03cc\u03c4\u03b1\u03c3\u03b7 \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf softmax \u03ba\u03b1\u03c4\u03b1\u03c1\u03b3\u03b5\u03af\u03c4\u03b1\u03b9. \u0391\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b5\u03af \u03bd\u03b1 \u03c4\u03bf \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03b5\u03ba\u03b5\u03af \u03cc\u03c0\u03bf\u03c5 \u03b7 \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03bf, \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03b1 \u03c4\u03bf memory I\/O \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03b2\u03b1\u03c1\u03cd\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03bc\u03b5\u03b3\u03ac\u03bb\u03c9\u03bd \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b9\u03ce\u03bd.<\/p>\n<h2 id=\"kv-cache-steno-simeio-long-context\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf KV cache \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c4\u03bf \u03c3\u03c4\u03b5\u03bd\u03cc \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03c3\u03c4\u03bf long-context inference<\/h2>\n<p>\u03a3\u03c4\u03b7\u03bd \u03b1\u03c5\u03c4\u03bf\u03c0\u03b1\u03bb\u03af\u03bd\u03b4\u03c1\u03bf\u03bc\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5, \u03ba\u03ac\u03b8\u03b5 \u03bd\u03ad\u03bf token \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac \u03c3\u03c4\u03b1 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03b1. \u03a4\u03bf KV cache \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03b5\u03b9 \u03c4\u03b1 key \u03ba\u03b1\u03b9 value states, \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03bc\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c4\u03b1 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03b6\u03b5\u03b9 \u03be\u03b1\u03bd\u03ac. \u0391\u03c5\u03c4\u03ae \u03b7 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03b5\u03be\u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b5\u03af \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf cache \u03bc\u03b5\u03b3\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03ac \u03bc\u03b5 \u03c4\u03bf \u03bc\u03ae\u03ba\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1\u03c2. \u0388\u03c4\u03c3\u03b9, \u03ba\u03ac\u03b8\u03b5 \u03b2\u03ae\u03bc\u03b1 decoding \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c5 \u03cc\u03b3\u03ba\u03bf\u03c5 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03b1\u03c0\u03cc \u03c4\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7.<\/p>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c5\u03c0\u03bf\u03b3\u03c1\u03b1\u03bc\u03bc\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9, \u03c3\u03b5 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 contexts, \u03b7 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c5\u03c7\u03bd\u03ac \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf memory bandwidth \u03c0\u03b1\u03c1\u03ac \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03ba\u03b1\u03b8\u03b1\u03c1\u03ae \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03b9\u03c3\u03c7\u03cd. \u0391\u03c5\u03c4\u03cc \u03ad\u03c7\u03b5\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1 \u03b3\u03b9\u03b1 \u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03af\u03b5\u03c2 AI: \u03b7 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03b1\u03bd \u03b7 \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 cache \u03ba\u03b1\u03b8\u03c5\u03c3\u03c4\u03b5\u03c1\u03b5\u03af \u03ba\u03ac\u03b8\u03b5 token, \u03b5\u03bd\u03ce \u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03ac\u03bb\u03c9\u03c3\u03b7 \u03b1\u03bd\u03ac request \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c4\u03c9\u03bd \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03c9\u03bd \u03c7\u03c1\u03b7\u03c3\u03c4\u03ce\u03bd \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03be\u03c5\u03c0\u03b7\u03c1\u03b5\u03c4\u03ae\u03c3\u03b5\u03b9 \u03ad\u03bd\u03b1\u03c2 accelerator.<\/p>\n<h2 id=\"eviction-kai-retention\">\u039f\u03b9 \u03b4\u03cd\u03bf \u03c3\u03c5\u03bd\u03b7\u03b8\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2: eviction \u03ba\u03b1\u03b9 retention<\/h2>\n<p>\u0397 \u03b2\u03b9\u03b2\u03bb\u03b9\u03bf\u03b3\u03c1\u03b1\u03c6\u03af\u03b1 \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03bf\u03c8\u03af\u03b6\u03b5\u03b9 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03c0\u03c1\u03bf\u03c3\u03b5\u03b3\u03b3\u03af\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b5 \u03b4\u03cd\u03bf \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2. \u039f\u03b9 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03b9 eviction \u03b1\u03c0\u03bf\u03bc\u03b1\u03ba\u03c1\u03cd\u03bd\u03bf\u03c5\u03bd \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03b1 cached tokens, \u03c3\u03c5\u03bd\u03ae\u03b8\u03c9\u03c2 \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03c3\u03c6\u03b1\u03c4\u03b7 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03ae \u03ba\u03ac\u03c0\u03bf\u03b9\u03b1 \u03b5\u03ba\u03c4\u03af\u03bc\u03b7\u03c3\u03b7 \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1\u03c2. \u039c\u03b5\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03c4\u03bf memory footprint, \u03b1\u03bb\u03bb\u03ac \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c1\u03c1\u03af\u03c8\u03bf\u03c5\u03bd \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03c0\u03bf\u03c5 \u03b1\u03c1\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7\u03c2 \u03b1\u03c0\u03cc\u03c3\u03c4\u03b1\u03c3\u03b7\u03c2.<\/p>\n<p>\u039f\u03b9 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03b9 retention \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03bf\u03cd\u03bd \u03bc\u03b9\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b9\u03b5\u03c3\u03bc\u03ad\u03bd\u03b7 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03bc\u03b1\u03ba\u03c1\u03b9\u03bd\u03bf\u03cd context. \u03a3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03bf\u03c5\u03bd \u03ad\u03bd\u03b1 \u03c4\u03bf\u03c0\u03b9\u03ba\u03cc sliding window softmax \u03bc\u03b5 \u03bc\u03b9\u03b1 recurrent \u03bc\u03bf\u03c1\u03c6\u03ae linear attention. \u03a3\u03c4\u03bf paper \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ad\u03c1\u03b3\u03b1 \u03cc\u03c0\u03c9\u03c2 LESS, BASED, Infini-Transformer, EdgeInfinite, LoLCats \u03ba\u03b1\u03b9 Liger. \u03a4\u03bf GLIDE \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c4\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03b7 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b1 \u03ba\u03b1\u03b9 \u03c7\u03c4\u03af\u03b6\u03b5\u03b9 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03b7\u03bd \u03b9\u03b4\u03ad\u03b1 \u03cc\u03c4\u03b9 \u03b7 \u03bc\u03b1\u03ba\u03c1\u03b9\u03bd\u03ae \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b5\u03c1\u03ac\u03c3\u03b5\u03b9 \u03c3\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03bf\u03cd \u03bc\u03b5\u03b3\u03ad\u03b8\u03bf\u03c5\u03c2 recurrent state, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af\u03c4\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 KV retrieval \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03b2\u03ae\u03bc\u03b1.<\/p>\n<h2 id=\"layers-eyaisthisia-grammikopoiisi\">\u0397 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03c0\u03b1\u03c1\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7: \u03c4\u03b1 layers \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03b4\u03c1\u03bf\u03cd\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c3\u03c4\u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7<\/h2>\n<p>\u0397 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 GLIDE \u03b5\u03af\u03bd\u03b1\u03b9 layer-wise. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b4\u03bf\u03ba\u03af\u03bc\u03b1\u03c3\u03b1\u03bd \u03c4\u03b9 \u03c3\u03c5\u03bc\u03b2\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b6\u03b5\u03cd\u03b3\u03b7 layers \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03b1, \u03b5\u03bd\u03ce \u03c4\u03b1 \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03b1 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03bf\u03cd\u03bd softmax. \u03a3\u03c4\u03bf Llama-3 8B, \u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c4\u03c9\u03bd \u03c0\u03c1\u03ce\u03c4\u03c9\u03bd layers 1 \u03ba\u03b1\u03b9 2 \u03bf\u03b4\u03ae\u03b3\u03b7\u03c3\u03b5 \u03c4\u03b7\u03bd \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03c3\u03c4\u03bf 36%, \u03b5\u03bd\u03ce \u03b7 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b7 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c3\u03c4\u03b1 \u03bc\u03b5\u03c3\u03b1\u03af\u03b1 layers 16 \u03ba\u03b1\u03b9 17 \u03ae \u03c3\u03c4\u03b1 \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03b1 31 \u03ba\u03b1\u03b9 32 \u03b5\u03af\u03c7\u03b5 \u03c0\u03bf\u03bb\u03cd \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 \u03c3\u03b5 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 softmax baseline.<\/p>\n<p>\u0397 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1 \u03c4\u03c9\u03bd \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ce\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03c0\u03c1\u03ce\u03c4\u03b1 layers \u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03af\u03b6\u03bf\u03c5\u03bd \u03b2\u03b1\u03c3\u03b9\u03ba\u03ad\u03c2 token representations \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b1\u03ba\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c7\u03ad\u03c3\u03b5\u03b9\u03c2, \u03ac\u03c1\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c3\u03c4\u03b5\u03c1\u03b7 softmax \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae. \u03a4\u03b1 \u03b2\u03b1\u03b8\u03cd\u03c4\u03b5\u03c1\u03b1 layers \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03bd \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03c0\u03b9\u03bf \u03b1\u03c6\u03b7\u03c1\u03b7\u03bc\u03ad\u03bd\u03b1 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03ba\u03b1\u03b9 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03c5\u03bd \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03bd\u03bf\u03c7\u03ae \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7. \u03a0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03b9\u03ba\u03cc \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1 \u03c3\u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 benchmarks \u03c0\u03bf\u03c5 \u03bc\u03b5\u03bb\u03b5\u03c4\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd, \u03cc\u03c7\u03b9 \u03b3\u03b9\u03b1 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03cc \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1 \u03c0\u03bf\u03c5 \u03ad\u03c7\u03b5\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03c7\u03b8\u03b5\u03af \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae.<\/p>\n<h2 id=\"pos-leitourgei-guided-layerwise-hybrid-attention\">\u03a0\u03ce\u03c2 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03b7 Guided Layerwise Hybrid Attention<\/h2>\n<p>\u03a4\u03bf GLIDE \u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c3\u03b5 \u03c4\u03c1\u03af\u03b1 \u03c3\u03c5\u03bd\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1 blocks: \u03c0\u03c1\u03ce\u03b9\u03bc\u03b1, \u03bc\u03b5\u03c3\u03b1\u03af\u03b1 \u03ba\u03b1\u03b9 \u03cc\u03c8\u03b9\u03bc\u03b1 layers. \u03a3\u03c4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf block \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 softmax \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf sliding window. \u03a3\u03c4\u03bf \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03bf \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03bb\u03ae\u03c1\u03b7 linear attention. \u03a3\u03c4\u03bf \u03bc\u03b5\u03c3\u03b1\u03af\u03bf block \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03b5\u03bd\u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03b7 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b1, \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03ac\u03bc\u03b5\u03c4\u03c1\u03bf \u03b1 \u03ba\u03b1\u03b9 \u03c4\u03bf \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b8\u03cd\u03c1\u03bf\u03c5 w.<\/p>\n<p>\u0397 \u03c0\u03b1\u03c1\u03ac\u03bc\u03b5\u03c4\u03c1\u03bf\u03c2 \u03b4 \u03b5\u03ba\u03c6\u03c1\u03ac\u03b6\u03b5\u03b9 \u03c0\u03cc\u03c3\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03c0\u03b1\u03c1\u03b1\u03b8\u03cd\u03c1\u03bf\u03c5 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc softmax \u03c3\u03b5 linear attention. \u038c\u03c4\u03b1\u03bd \u03b4=0, \u03c4\u03bf layer \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af softmax \u03b3\u03b9\u03b1 \u03cc\u03bb\u03bf \u03c4\u03bf window. \u038c\u03c4\u03b1\u03bd \u03b4=w, \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 linear. \u039c\u03b9\u03b1 \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 (0, w\/2, w), \u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 softmax \u03c3\u03c4\u03b1 \u03c0\u03c1\u03ce\u03c4\u03b1 layers, \u03bc\u03b9\u03c3\u03cc linear \u03ba\u03b1\u03b9 \u03bc\u03b9\u03c3\u03cc softmax \u03c3\u03c4\u03b1 \u03bc\u03b5\u03c3\u03b1\u03af\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 linear \u03c3\u03c4\u03b1 \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03b1.<\/p>\n<p>\u03a4\u03b1 tokens \u03c0\u03bf\u03c5 \u03b2\u03c1\u03af\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ad\u03be\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b5\u03bd\u03b5\u03c1\u03b3\u03cc softmax window \u03b4\u03b5\u03bd \u03b5\u03be\u03b1\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2. \u0395\u03bd\u03c3\u03c9\u03bc\u03b1\u03c4\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ad\u03bd\u03b1 recurrent state \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03bf\u03cd \u03bc\u03b5\u03b3\u03ad\u03b8\u03bf\u03c5\u03c2. \u0397 \u03ad\u03be\u03bf\u03b4\u03bf\u03c2 \u03c4\u03bf\u03c5 layer \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf \u03c4\u03bf\u03c0\u03b9\u03ba\u03cc softmax \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03bc\u03b5 \u03c4\u03bf linear recurrent \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1, \u03c3\u03c4\u03b1\u03b8\u03bc\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 \u03bc\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf\u03c5\u03c2 \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03b7\u03c4\u03ad\u03c2. \u039c\u03b5 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf\u03bd \u03c4\u03c1\u03cc\u03c0\u03bf \u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03b5\u03b9 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03b5\u03c1\u03b5\u03af\u03c2 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03b9\u03b4\u03c1\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b1 \u03bc\u03b9\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b9\u03b5\u03c3\u03bc\u03ad\u03bd\u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf\u03c5 \u03c4\u03bf\u03c5 prefix.<\/p>\n<h2 id=\"block-wise-rythmisi-ana-layer\">\u0393\u03b9\u03b1\u03c4\u03af \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03c4\u03b1\u03b9 block-wise \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03b1\u03bd\u03ac layer<\/h2>\n<p>\u0398\u03b5\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03ac, \u03ba\u03ac\u03b8\u03b5 layer \u03b8\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03c4\u03b7 \u03b4\u03b9\u03ba\u03ae \u03c4\u03bf\u03c5 \u03c4\u03b9\u03bc\u03ae \u03b4. \u0391\u03c5\u03c4\u03cc \u03cc\u03bc\u03c9\u03c2 \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf\u03bd \u03c7\u03ce\u03c1\u03bf \u03b1\u03bd\u03b1\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03ba\u03b1\u03bb\u03ad\u03c3\u03b5\u03b9 \u03c3\u03c5\u03c7\u03bd\u03ad\u03c2 \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 kernels, \u03b4\u03c5\u03c3\u03ba\u03bf\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf operator fusion \u03ba\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc runtime overhead. \u0397 block-wise \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03bf\u03bb\u03c5\u03c0\u03bb\u03bf\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1: \u03c4\u03b1 \u03c0\u03c1\u03ce\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03b1 blocks \u03ad\u03c7\u03bf\u03c5\u03bd \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae, \u03b5\u03bd\u03ce \u03b3\u03b9\u03b1 \u03c4\u03bf \u03bc\u03b5\u03c3\u03b1\u03af\u03bf \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c4\u03b9\u03bc\u03ce\u03bd, \u03cc\u03c0\u03c9\u03c2 0, w\/2, 15w\/16 \u03ba\u03b1\u03b9 w.<\/p>\n<p>\u03a3\u03c4\u03b1 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1, \u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b5\u03af\u03c7\u03b1\u03bd 32 layers \u03ba\u03b1\u03b9 \u03c7\u03c9\u03c1\u03af\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03c3\u03b5 layers 1\u201311, 12\u201321 \u03ba\u03b1\u03b9 22\u201332. \u0397 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 \u03c4\u03b7 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03bc\u03b5\u03c3\u03b1\u03af\u03bf\u03c5 block \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b7\u03bd task accuracy. \u0386\u03c1\u03b1 \u03c4\u03bf GLIDE \u03b4\u03b5\u03bd \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03b1\u03b4\u03b9\u03ba\u03cc, \u03b1\u03bc\u03b5\u03c4\u03ac\u03b2\u03bb\u03b7\u03c4\u03bf configuration, \u03b1\u03bb\u03bb\u03ac \u03c9\u03c2 \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae\u03c2 \u03c4\u03bf\u03c5 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf\u03c5 softmax budget.<\/p>\n<h2 id=\"dokimes-llama-3-8b-mistral-7b\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03bf\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c3\u03b5 Llama-3-8B \u03ba\u03b1\u03b9 Mistral-7B<\/h2>\n<p>\u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03ad\u03b3\u03b9\u03bd\u03b5 \u03c3\u03b5 \u03ad\u03be\u03b9 benchmarks: PiQA, ARC-Easy, ARC-Challenge, HellaSwag, WinoGrande \u03ba\u03b1\u03b9 MMLU. \u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd Llama-3-8B \u03ba\u03b1\u03b9 Mistral-7B, PyTorch FlexAttention \u03bc\u03b5 Flash Attention backend \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 NVIDIA Grace Hopper GH200 Superchip 120GB. \u0397 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03ad\u03b3\u03b9\u03bd\u03b5 \u03bc\u03b5 LoRA rank 8 \u03ba\u03b1\u03b9 scaling coefficient 8. \u03a4\u03bf paper \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7 \u03b4\u03cd\u03bf epochs \u03c3\u03b5 \u03ba\u03b1\u03b8\u03b1\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 Alpaca instruction samples \u03ba\u03b1\u03b9 \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc window w=1024 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03ba\u03cd\u03c1\u03b9\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7.<\/p>\n<p>\u03a3\u03c4\u03bf\u03bd \u03c0\u03af\u03bd\u03b1\u03ba\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03ac\u03c4\u03c9\u03bd, \u03c4\u03bf vanilla softmax \u03b5\u03af\u03c7\u03b5 \u03bc\u03ad\u03c3\u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 72,16% \u03b3\u03b9\u03b1 \u03c4\u03bf Llama \u03ba\u03b1\u03b9 72,55% \u03b3\u03b9\u03b1 \u03c4\u03bf Mistral, \u03bc\u03b5 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03bf KV I\/O 4.000 MB \u03b1\u03bd\u03ac token. \u03a4\u03bf hybrid baseline (0,0,0) \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 69,12% \u03ba\u03b1\u03b9 71,31% \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1, \u03bc\u03b5 128 MB \u03b1\u03bd\u03ac token. \u0397 \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 (0,0,w) \u03ad\u03b4\u03c9\u03c3\u03b5 67,84% \u03c3\u03c4\u03bf Llama \u03ba\u03b1\u03b9 69,80% \u03c3\u03c4\u03bf Mistral, \u03bc\u03b5 88 MB \u03b1\u03bd\u03ac token.<\/p>\n<p>\u0397 \u03c0\u03b9\u03bf \u03b5\u03c0\u03b9\u03b8\u03b5\u03c4\u03b9\u03ba\u03ae (0,15w\/16,w) \u03b5\u03af\u03c7\u03b5 66,74% \u03ba\u03b1\u03b9 68,23%, \u03bc\u03b5 43 MB \u03b1\u03bd\u03ac token. \u0391\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03b7 \u03c0\u03bb\u03ae\u03c1\u03b7\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03ad\u03c1\u03b9\u03be\u03b5 \u03c4\u03b7 \u03bc\u03ad\u03c3\u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03c3\u03c4\u03bf 33,96% \u03ba\u03b1\u03b9 34,34%. \u03a4\u03b1 \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf paper \u03b4\u03b5\u03bd \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c0\u03bb\u03ae \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03cc\u03bb\u03bf\u03c5 \u03c4\u03bf\u03c5 softmax: \u03b7 \u03b8\u03ad\u03c3\u03b7 \u03c4\u03c9\u03bd layers \u03c0\u03bf\u03c5 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae.<\/p>\n<h2 id=\"lora-fine-tuning-anaktisi-poiotitas\">\u039f \u03c1\u03cc\u03bb\u03bf\u03c2 \u03c4\u03bf\u03c5 LoRA fine-tuning \u03c3\u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2<\/h2>\n<p>\u03a9\u03c2 \u03ac\u03bc\u03b5\u03c3\u03bf drop-in replacement, \u03bf\u03b9 \u03bc\u03b7 \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03b5\u03c2 \u03b4\u03b9\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1\u03c3\u03b1\u03bd \u03c0\u03c4\u03ce\u03c3\u03b7 5% \u03ad\u03c9\u03c2 10% \u03c3\u03b5 downstream performance \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03bf\u03c5 hybrid baseline. \u039f\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b1\u03bd parameter-efficient fine-tuning \u03bc\u03b5 LoRA \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03c3\u03bf\u03c5\u03bd \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7\u03bd \u03b1\u03c0\u03ce\u03bb\u03b5\u03b9\u03b1. \u0391\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03b1\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7 6 \u03ad\u03c9\u03c2 8 \u03bc\u03bf\u03bd\u03ac\u03b4\u03c9\u03bd \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03b7 fine-tuned \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 (0,0,w).<\/p>\n<p>\u039c\u03b5\u03c4\u03ac \u03c4\u03bf fine-tuning, \u03b1\u03c5\u03c4\u03ae \u03b7 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 68%\u201370% accuracy, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 96% \u03c4\u03b7\u03c2 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7\u03c2 \u03c4\u03bf\u03c5 vanilla baseline, \u03bc\u03b5 88 MB\/token. \u03a4\u03bf paper \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03c9\u03c2 45 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf memory bandwidth \u03b1\u03c0\u03cc \u03c4\u03bf vanilla softmax. \u03a4\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: \u03b7 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03ba\u03b1\u03b9 \u03b7 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03bd \u03bc\u03b1\u03b6\u03af. \u0394\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03ba\u03b1\u03bd\u03b5\u03af\u03c2 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc attention \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b8\u03b5\u03c9\u03c1\u03ae\u03c3\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b8\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03b5\u03af\u03bd\u03b5\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 \u03af\u03b4\u03b9\u03b1.<\/p>\n<h2 id=\"latency-out-of-memory-megala-contexts\">Latency, out-of-memory \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03c3\u03b5 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 contexts<\/h2>\n<p>\u03a3\u03c4\u03bf profiling \u03c4\u03bf\u03c5 Llama-3-8B \u03b1\u03c0\u03cc prefill 20K tokens, \u03b7 \u03bc\u03ad\u03c3\u03b7 inter-token latency \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03ad\u03c0\u03b5\u03c3\u03b5 \u03b1\u03c0\u03cc \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 180 ms \u03c3\u03b5 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 55 ms \u03c3\u03c4\u03b7\u03bd \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 linearized \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae 3,3 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03b1. \u0397 attention-only latency \u03c4\u03c9\u03bd linear layers \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03bd\u03b5 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 7\u20139 ms \u03ba\u03b1\u03b9 \u03c4\u03bf paper \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03ad\u03c9\u03c2 17,4 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 softmax \u03b3\u03b9\u03b1 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03c4\u03bc\u03ae\u03bc\u03b1 \u03c4\u03bf\u03c5 operator.<\/p>\n<p>\u03a3\u03c4\u03b1 accumulated latency tests, \u03c4\u03bf vanilla Llama baseline \u03bf\u03bb\u03bf\u03ba\u03bb\u03ae\u03c1\u03c9\u03c3\u03b5 \u03c4\u03b1 4K \u03c3\u03b5 264,63 \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b1 8K \u03c3\u03b5 765,92 \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03b1, \u03b1\u03bb\u03bb\u03ac \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b5 out-of-memory \u03c3\u03c4\u03b1 16K \u03ba\u03b1\u03b9 32K. \u0397 GLIDE \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 (0,0,w) \u03bf\u03bb\u03bf\u03ba\u03bb\u03ae\u03c1\u03c9\u03c3\u03b5 \u03c4\u03b1 32K \u03c3\u03b5 2.638,35 \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03b1. \u0397 \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 linear \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03ae\u03c4\u03b1\u03bd \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03b7, \u03c3\u03c4\u03b1 1.640,75 \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03b1, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b5 \u03c4\u03b7 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03c0\u03c4\u03ce\u03c3\u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1\u03c2 \u03c0\u03bf\u03c5 \u03ae\u03b4\u03b7 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03b7\u03ba\u03b5.<\/p>\n<p>\u03a3\u03c4\u03bf Mistral-7B, \u03bf\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 latency \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03c4\u03c9\u03bd GLIDE configurations \u03ae\u03c4\u03b1\u03bd \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03c0\u03bf\u03b4\u03af\u03b4\u03bf\u03c5\u03bd \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c3\u03b5 hardware-level overhead, \u03cc\u03c0\u03c9\u03c2 warp divergence, \u03b1\u03ba\u03b1\u03bd\u03cc\u03bd\u03b9\u03c3\u03c4\u03b1 memory access patterns \u03ba\u03b1\u03b9 kernel launch overhead. \u0395\u03af\u03bd\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ae \u03c5\u03c0\u03b5\u03bd\u03b8\u03cd\u03bc\u03b9\u03c3\u03b7: \u03b7 \u03b8\u03b5\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03ae \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 FLOPs \u03b4\u03b5\u03bd \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 \u03c3\u03b5 \u03af\u03c3\u03b7 wall-clock \u03b5\u03c0\u03b9\u03c4\u03ac\u03c7\u03c5\u03bd\u03c3\u03b7. \u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03bf \u03b9\u03c3\u03c7\u03cd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf <a href=\"https:\/\/twodots.gr\/llm-routing-latency-accuracy-cost\/\">LLM routing \u03bc\u03b5 \u03b5\u03c0\u03af\u03b3\u03bd\u03c9\u03c3\u03b7 latency<\/a>: \u03bc\u03b5\u03c4\u03c1\u03ac \u03b7 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf\u03c5 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bf \u03b8\u03b5\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 \u03c0\u03c1\u03bf\u03cb\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc\u03c2 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03bf\u03cd.<\/p>\n<h2 id=\"akribeia-kv-cache-io\">\u03a4\u03b9 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b7 \u03b1\u03bd\u03c4\u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1\u03c2 \u03ba\u03b1\u03b9 KV-cache I\/O<\/h2>\n<p>\u03a4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03c4\u03bf\u03c5 \u03b2\u03b1\u03c3\u03b9\u03ba\u03bf\u03cd \u03c0\u03af\u03bd\u03b1\u03ba\u03b1 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03bf\u03c5\u03bd \u03bc\u03b9\u03b1 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ae \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03bf Llama-3-8B. \u038c\u03c3\u03bf \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7, \u03c4\u03bf KV I\/O \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc 4.000 \u03c3\u03b5 128, 88, 64 \u03ba\u03b1\u03b9 43 MB\/token, \u03b5\u03bd\u03ce \u03b7 \u03bc\u03ad\u03c3\u03b7 accuracy \u03c5\u03c0\u03bf\u03c7\u03c9\u03c1\u03b5\u03af \u03b1\u03c0\u03cc 72,16% \u03c3\u03b5 69,12%, 67,84%, 67,73% \u03ba\u03b1\u03b9 66,74%. \u0397 \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 linear \u03c0\u03b5\u03c1\u03af\u03c0\u03c4\u03c9\u03c3\u03b7 \u03bc\u03b7\u03b4\u03b5\u03bd\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc KV I\/O \u03c3\u03c4\u03bf\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc, \u03b1\u03bb\u03bb\u03ac \u03b7 accuracy \u03c0\u03ad\u03c6\u03c4\u03b5\u03b9 \u03c3\u03c4\u03bf 33,96%.<\/p>\n<p>\u0397 \u03c0\u03b9\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u00ab\u03cc\u03c3\u03bf \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03c4\u03cc\u03c3\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1\u00bb. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 Pareto frontier: \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b4\u03b9\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03c4\u03b1\u03bb\u03bb\u03ac\u03c3\u03c3\u03bf\u03c5\u03bd \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03bc\u03b5 bandwidth. \u039c\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ce\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03bb\u03ad\u03be\u03b5\u03b9 \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1\u03c2 \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03bf workload, \u03c4\u03bf \u03b5\u03c0\u03b9\u03c4\u03c1\u03b5\u03c0\u03c4\u03cc quality loss \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc hardware profile.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<p class=\"td-chart-title\">\u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c4\u03bf\u03c5 GLIDE \u03c3\u03c4\u03bf \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc setup<\/p>\n<p class=\"td-chart-intro\">\u039f\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac configurations \u03c3\u03b5 Llama-3-8B \u03ba\u03b1\u03b9 Mistral-7B \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03ad\u03bd\u03b1 GH200. \u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ac benchmarks \u03bf\u03cd\u03c4\u03b5 \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03af\u03b4\u03b9\u03bf\u03c5 \u03bf\u03c6\u03ad\u03bb\u03bf\u03c5\u03c2 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03bf hardware.<\/p>\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">45\u00d7\u201393\u00d7<\/span><strong>\u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf KV-cache I\/O<\/strong><\/p>\n<p>\u03a4\u03bf \u03b5\u03cd\u03c1\u03bf\u03c2 \u03c4\u03c9\u03bd fine-tuned \u03bc\u03b7 \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03c9\u03bd configurations \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 vanilla softmax, \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2.<\/p>\n<\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">90%\u201396%<\/span><strong>\u03b4\u03b9\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7 baseline accuracy<\/strong><\/p>\n<p>\u03a4\u03bf \u03b5\u03cd\u03c1\u03bf\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b3\u03b9\u03b1 \u03bc\u03ad\u03c4\u03c1\u03b9\u03b1 \u03ad\u03c9\u03c2 \u03b5\u03c0\u03b9\u03b8\u03b5\u03c4\u03b9\u03ba\u03ae \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf fine-tuning.<\/p>\n<\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">1,4\u00d7\u20132\u00d7<\/span><strong>\u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 latency<\/strong><\/p>\n<p>\u0397 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03bf\u03c8\u03af\u03b6\u03bf\u03c5\u03bd \u03bf\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03b1 \u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03b7\u03bc\u03ad\u03bd\u03b1 GLIDE configurations, \u03cc\u03c7\u03b9 \u03b3\u03b9\u03b1 \u03c0\u03bb\u03ae\u03c1\u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7.<\/p>\n<\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">2\u20133\u00d7<\/span><strong>\u03b5\u03ba\u03c4\u03b9\u03bc\u03ce\u03bc\u03b5\u03bd\u03b7 serving capacity<\/strong><\/p>\n<p>\u03a0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf\u03b9 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03bf\u03b9 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b5\u03c2 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf hardware \u03bb\u03cc\u03b3\u03c9 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 KV footprint, \u03c0\u03c1\u03bf\u03c2 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03af\u03c9\u03c3\u03b7 \u03c3\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03cc workload.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"epicheiriseis-ai-ypodomi\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03bf\u03c5\u03bd \u03ae \u03c7\u03c4\u03af\u03b6\u03bf\u03c5\u03bd AI \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae<\/h2>\n<p>\u0393\u03b9\u03b1 \u03ad\u03bd\u03b1\u03bd e-commerce owner \u03ae marketer, \u03c4\u03bf GLIDE \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03c0\u03bf\u03c5 \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03ad\u03c4\u03bf\u03b9\u03bc\u03bf SaaS dashboard. \u0395\u03af\u03bd\u03b1\u03b9 \u03ad\u03c1\u03b5\u03c5\u03bd\u03b1 \u03c3\u03b5 \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03bf \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae\u03c2 \u03ba\u03b1\u03b9 serving. \u03a0\u03b1\u03c1\u2019 \u03cc\u03bb\u03b1 \u03b1\u03c5\u03c4\u03ac, \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03b3\u03b9\u03b1\u03c4\u03af \u03b4\u03cd\u03bf \u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03af\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03bf \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ad\u03c7\u03bf\u03c5\u03bd \u03c0\u03bf\u03bb\u03cd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1, \u03cc\u03c1\u03b9\u03b1 context \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae\u03c2. \u0397 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae, \u03c4\u03bf attention backend, \u03c4\u03bf cache management \u03ba\u03b1\u03b9 \u03c4\u03bf fine-tuning \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03af\u03b1.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03b1\u03c0\u03c4\u03cd\u03c3\u03c3\u03bf\u03c5\u03bd \u03b4\u03b9\u03ba\u03ac \u03c4\u03bf\u03c5\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ae self-hosted inference \u2014 \u03cc\u03c0\u03bf\u03c5 \u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae serving stack, \u03cc\u03c0\u03c9\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b3\u03b9\u03b1 <a href=\"https:\/\/twodots.gr\/vllm-transformers-native-speed-ai-inference\/\">vLLM \u03ba\u03b1\u03b9 native-speed inference<\/a>, \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u2014 \u03c4\u03b1 \u03c3\u03c9\u03c3\u03c4\u03ac \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1: \u03a3\u03b5 \u03c0\u03bf\u03b9\u03b1 sequence lengths \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 memory bottleneck; \u03a0\u03bf\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf KV I\/O \u03b1\u03bd\u03ac token; \u03a0\u03bf\u03b9\u03b1 benchmarks \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf\u03cd\u03bd \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload; \u03a0\u03cc\u03c3\u03bf quality loss \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc; \u03a5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 PEFT; \u039a\u03b1\u03b9 \u03ba\u03c5\u03c1\u03af\u03c9\u03c2, \u03ad\u03c7\u03bf\u03c5\u03bd \u03bc\u03b5\u03c4\u03c1\u03b7\u03b8\u03b5\u03af latency \u03ba\u03b1\u03b9 concurrency \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf hardware \u03cc\u03c0\u03bf\u03c5 \u03b8\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1;<\/p>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b5\u03ba\u03c4\u03b9\u03bc\u03bf\u03cd\u03bd, \u03b2\u03ac\u03c3\u03b5\u03b9 \u03c4\u03b7\u03c2 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7\u03c2 \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2, \u03cc\u03c4\u03b9 \u03bf\u03b9 \u03b5\u03be\u03b5\u03c4\u03b1\u03b6\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03b4\u03b9\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03c3\u03b5\u03b9\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03be\u03bf\u03c5\u03bd \u03b4\u03cd\u03bf \u03ad\u03c9\u03c2 \u03c4\u03c1\u03b5\u03b9\u03c2 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5\u03c2 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03bf\u03c5\u03c2 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b5\u03c2 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf hardware. \u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7\u03c2 \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \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 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 deployment. Batch size, prompt distribution, kernels, quantization \u03ba\u03b1\u03b9 orchestration \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u03a0\u03ad\u03bd\u03c4\u03b5 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03b9 \u03c0\u03c1\u03b9\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b7\u03b8\u03b5\u03af \u03c4\u03bf GLIDE \u03c3\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">Step 1<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c4\u03b7\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae context<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 prompt length, generated tokens, batch size \u03ba\u03b1\u03b9 concurrency\u00b7 \u03c7\u03c9\u03c1\u03af\u03c2 \u03b1\u03c5\u03c4\u03ac \u03b4\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03bf baseline \u03b3\u03b9\u03b1 KV-cache I\/O \u03ae latency.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 2<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 workload-specific quality tests<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03b4\u03b9\u03ba\u03ac \u03c3\u03b1\u03c2 long-context \u03c3\u03b5\u03bd\u03ac\u03c1\u03b9\u03b1, retrieval failures \u03ba\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03b8\u03b5\u03c9\u03c1\u03ae\u03c3\u03b5\u03c4\u03b5 \u03cc\u03c4\u03b9 \u03ad\u03be\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ac benchmarks \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 3<\/span><strong>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 \u03af\u03b4\u03b9\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf hardware<\/strong>\n<p>\u039a\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac precision, quantization, serving engine \u03ba\u03b1\u03b9 kernels \u03ce\u03c3\u03c4\u03b5 \u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03af\u03b4\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd attention policy \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c3\u03b5 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c4\u03bf\u03c5 stack.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 4<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 tail latency \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf<\/strong>\n<p>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 throughput, p95 latency, out-of-memory \u03c3\u03c5\u03bc\u03b2\u03ac\u03bd\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c5\u03c0\u03cc \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b1 requests, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b5\u03ba\u03b5\u03af \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c0\u03af\u03b5\u03c3\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 5<\/span><strong>\u0395\u03c0\u03b9\u03bb\u03ad\u03be\u03c4\u03b5 Pareto \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03bc\u03b5 \u03cc\u03c1\u03b9\u03bf \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2<\/strong>\n<p>\u0391\u03c0\u03bf\u03b4\u03b5\u03c7\u03b8\u03b5\u03af\u03c4\u03b5 \u03ad\u03bd\u03b1 configuration \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03b7 \u03b5\u03be\u03bf\u03b9\u03ba\u03bf\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 I\/O \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 \u03b7 \u03c0\u03c4\u03ce\u03c3\u03b7 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7\u03c2 \u03bc\u03ad\u03bd\u03b5\u03b9 \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc threshold.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<h2 id=\"oria-meletis-glide\">\u03a4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03b7\u03bb\u03ce\u03bd\u03b5\u03b9 \u03b7 \u03af\u03b4\u03b9\u03b1 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7<\/h2>\n<p>\u03a4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03cc\u03c1\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf information dilution. \u039a\u03b1\u03b8\u03ce\u03c2 \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1, \u03c4\u03bf \u03c3\u03c5\u03bc\u03c0\u03b9\u03b5\u03c3\u03bc\u03ad\u03bd\u03bf recurrent state \u03c4\u03b7\u03c2 linear attention \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c7\u03ac\u03c3\u03b5\u03b9 \u03bb\u03b5\u03c0\u03c4\u03ad\u03c2 \u03b4\u03b9\u03b1\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 tokens. \u03a4\u03bf \u03c4\u03bf\u03c0\u03b9\u03ba\u03cc softmax window \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03b1\u03b8\u03bc\u03af\u03b6\u03b5\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b1\u03c0\u03ce\u03bb\u03b5\u03b9\u03b1\u03c2, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03c4\u03b7\u03bd \u03b5\u03be\u03b1\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03b9. \u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03ba\u03b1\u03b9 \u03b7 accuracy-efficiency \u03b1\u03bd\u03c4\u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03bf\u03c1\u03b1\u03c4\u03ae \u03c3\u03c4\u03b1 long-context workloads.<\/p>\n<p>\u03a4\u03bf \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03cc\u03c1\u03b9\u03bf \u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03b1 kernels. \u0393\u03b9\u03b1 \u03bc\u03b9\u03ba\u03c1\u03ac windows, \u03b5\u03b9\u03b4\u03b9\u03ba\u03ac w\u2264128, \u03c4\u03b1 \u03b8\u03b5\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03ac \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 FLOPs \u03b4\u03b5\u03bd \u03ad\u03b4\u03c9\u03c3\u03b1\u03bd \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b7 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03bf kernel launch \u03ba\u03b1\u03b9 \u03c4\u03bf memory scheduling \u03ba\u03cc\u03c3\u03c4\u03b9\u03b6\u03b1\u03bd \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03b9\u03ba\u03ac \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf. \u039f\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03bf\u03c5\u03bd fused kernels \u03b5\u03b9\u03b4\u03b9\u03ba\u03ac \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03bc\u03b5\u03af\u03b3\u03bc\u03b1 sliding-window softmax \u03ba\u03b1\u03b9 linear recurrence.<\/p>\n<p>\u03a4\u03ad\u03bb\u03bf\u03c2, \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c3\u03b5 distributed inference \u03bc\u03b5 tensor \u03ae pipeline parallelism \u03ba\u03b1\u03b9 \u03c3\u03b5 reasoning-heavy workloads \u03cc\u03c0\u03bf\u03c5 \u03b7 layer-wise \u03b5\u03c5\u03b1\u03b9\u03c3\u03b8\u03b7\u03c3\u03af\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03b6\u03b5\u03b9 \u03b4\u03cd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 7\u20138B \u03ba\u03b1\u03b9 \u03ad\u03be\u03b9 benchmarks \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf GH200 \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1. \u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ae \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03cc\u03c4\u03b9 \u03b7 \u03af\u03b4\u03b9\u03b1 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03b8\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b2\u03ad\u03bb\u03c4\u03b9\u03c3\u03c4\u03b7 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, accelerator \u03ae \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae.<\/p>\n<h2 id=\"ousia-glide-pera-apo-arithmous\">\u0397 \u03bf\u03c5\u03c3\u03af\u03b1 \u03c4\u03bf\u03c5 GLIDE \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf\u03c5\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03cd\u03c2<\/h2>\n<p>\u03a4\u03bf \u03c0\u03b9\u03bf \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd \u03bc\u03ae\u03bd\u03c5\u03bc\u03b1 \u03c4\u03bf\u03c5 GLIDE \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 LLM \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03b7. \u0388\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ad\u03c7\u03b5\u03b9 \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03b5\u03c4\u03b5\u03c1\u03bf\u03b3\u03ad\u03bd\u03b5\u03b9\u03b1: \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 layers \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b9\u03bf \u03b5\u03c5\u03b1\u03af\u03c3\u03b8\u03b7\u03c4\u03b1, \u03ac\u03bb\u03bb\u03b1 \u03c0\u03b9\u03bf \u03b1\u03bd\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac. \u0391\u03bd\u03c4\u03af \u03bd\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03c3\u03c4\u03b5\u03af \u03b7 \u03af\u03b4\u03b9\u03b1 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd, \u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 \u03bc\u03b5\u03c4\u03c1\u03ac \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b1\u03bd\u03ad\u03bc\u03b5\u03b9 \u03c4\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc softmax \u03b5\u03ba\u03b5\u03af \u03cc\u03c0\u03bf\u03c5 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c4\u03b1\u03c4\u03b5\u03cd\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1.<\/p>\n<p>\u0393\u03b9\u03b1 \u03c4\u03b9\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, \u03b1\u03c5\u03c4\u03cc \u03bc\u03b5\u03c4\u03b1\u03c6\u03c1\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bc\u03b9\u03b1 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c1\u03c7\u03ae \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2 AI: \u03c4\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 claims \u03b3\u03b9\u03b1 speedup \u03ae memory reduction \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03bf\u03b4\u03b5\u03cd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03c4\u03bf hardware, \u03c4\u03bf context length, \u03c4\u03bf benchmark, \u03c4\u03bf fine-tuning \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c0\u03ce\u03bb\u03b5\u03b9\u03b1 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u03a4\u03bf GLIDE \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03c0\u03bf\u03bb\u03bb\u03ac \u03c5\u03c0\u03bf\u03c3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03ba\u03b1\u03c4\u03b5\u03cd\u03b8\u03c5\u03bd\u03c3\u03b7 \u03b3\u03b9\u03b1 long-context serving, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03c5\u03c0\u03b5\u03cd\u03b8\u03c5\u03bd\u03b7 \u03c5\u03b9\u03bf\u03b8\u03ad\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd\u03c4\u03b9\u03b3\u03c1\u03b1\u03c6\u03ae \u03b5\u03bd\u03cc\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03c4\u03b7\u03c1\u03b9\u03b1\u03ba\u03bf\u03cd configuration.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-eyebrow\">Business automation and AI from TWO DOTS<\/p>\n<p class=\"td-service-cta-title\">\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf AI workflow \u03c0\u03c1\u03b9\u03bd \u03b5\u03c0\u03b5\u03bd\u03b4\u03cd\u03c3\u03b5\u03c4\u03b5 \u03c3\u03b5 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae.<\/p>\n<p>\u0397 TWO DOTS \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af workload, \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, integrations, latency, quality gates \u03ba\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ce\u03c3\u03c4\u03b5 \u03b7 AI \u03bb\u03cd\u03c3\u03b7 \u03bd\u03b1 \u03c5\u03c0\u03b7\u03c1\u03b5\u03c4\u03b5\u03af \u03bc\u03b9\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03c1\u03bf\u03ae.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">See business automation with 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 GLIDE;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf Guided Layerwise Hybrid Attention \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 sliding-window softmax \u03ba\u03b1\u03b9 linear recurrent attention, \u03ba\u03b1\u03c4\u03b1\u03bd\u03ad\u03bc\u03bf\u03bd\u03c4\u03ac\u03c2 \u03c4\u03b1 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03b1\u03bd\u03ac \u03b2\u03ac\u03b8\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03b4\u03b5\u03bd \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03bc\u03cc\u03bd\u03bf linear attention;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03b1 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1, \u03b7 \u03c0\u03bb\u03ae\u03c1\u03b7\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03b4\u03c1\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03c4\u03b7\u03bd \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1. \u03a4\u03b1 \u03c0\u03c1\u03ce\u03c4\u03b1 layers \u03ae\u03c4\u03b1\u03bd \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b1 \u03b5\u03c5\u03b1\u03af\u03c3\u03b8\u03b7\u03c4\u03b1 \u03c3\u03c4\u03b7\u03bd \u03b1\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03b7 \u03c4\u03bf\u03c5 softmax.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b5 Llama-3-8B \u03ba\u03b1\u03b9 Mistral-7B \u03c3\u03b5 \u03ad\u03be\u03b9 reasoning \u03ba\u03b1\u03b9 knowledge benchmarks.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03cc\u03c3\u03bf \u03bc\u03b5\u03b9\u03ce\u03b8\u03b7\u03ba\u03b5 \u03c4\u03bf KV-cache I\/O;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03ad\u03c9\u03c2 62 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03c3\u03b5 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03ad\u03c9\u03c2 93 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03b3\u03b9\u03b1 \u03c0\u03b9\u03bf \u03b5\u03c0\u03b9\u03b8\u03b5\u03c4\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7, \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03bf \u03b4\u03b9\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7\u03c2 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1\u03c2 \u03b1\u03bd\u03ac configuration.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 fine-tuning;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039f\u03b9 \u03bc\u03b7 \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03b5\u03c2 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03c3\u03c4\u03bf\u03cd\u03bd \u03c9\u03c2 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7, \u03b1\u03bb\u03bb\u03ac \u03c3\u03c4\u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c4\u03bf LoRA fine-tuning \u03b1\u03bd\u03ad\u03ba\u03c4\u03b7\u03c3\u03b5 6\u20138 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b5\u03c2 accuracy \u03ba\u03b1\u03b9 \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b5 \u03c4\u03bf trade-off.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03bf GLIDE \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf latency;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 hardware \u03ae configuration. \u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 \u03c3\u03c4\u03bf \u03b5\u03be\u03b5\u03c4\u03b1\u03b6\u03cc\u03bc\u03b5\u03bd\u03bf setup, \u03b1\u03bb\u03bb\u03ac \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03bf\u03c5\u03bd kernel overhead \u03ba\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 \u03bf\u03c6\u03ad\u03bb\u03b7 \u03c3\u03c4\u03bf Mistral.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5\u03c2 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03bf\u03c5\u03c2 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b5\u03c2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf paper \u03b5\u03ba\u03c4\u03b9\u03bc\u03ac 2\u20133 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 serving capacity \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf hardware \u03bb\u03cc\u03b3\u03c9 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 KV footprint. \u0397 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03b7\u03b8\u03b5\u03af \u03c3\u03c4\u03bf \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03cc workload.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc \u03c1\u03af\u03c3\u03ba\u03bf \u03c3\u03b5 \u03c0\u03bf\u03bb\u03cd \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf context;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf compressed recurrent state \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03c3\u03b5\u03b9 information dilution \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c7\u03ac\u03c3\u03b5\u03b9 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03b5\u03c1\u03b5\u03af\u03c2 token-level \u03b4\u03b9\u03b1\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03cc\u03c3\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 \u03b7 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1.<\/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\/2607.24788\" target=\"_blank\" rel=\"noopener\">William, Tandon &amp; Dass: GLIDE \u2014 Guided Layerwise Hybrid Attention for Efficient LLM Inference<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2106.09685\" target=\"_blank\" rel=\"noopener\">Hu et al.: LoRA \u2014 Low-Rank Adaptation of Large Language Models<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2006.16236\" target=\"_blank\" rel=\"noopener\">Katharopoulos et al.: Transformers are RNNs \u2014 Fast Autoregressive Transformers with Linear Attention<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2310.06825\" target=\"_blank\" rel=\"noopener\">Jiang et al.: Mistral 7B<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf GLIDE \u03ba\u03b1\u03c4\u03b1\u03bd\u03ad\u03bc\u03b5\u03b9 softmax \u03ba\u03b1\u03b9 linear attention \u03b1\u03bd\u03ac layer \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 \u03c4\u03bf KV-cache I\/O \u03c3\u03c4\u03b1 LLM \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf\u03c5 context. \u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03bf\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c3\u03b5 Llama-3-8B \u03ba\u03b1\u03b9 Mistral-7B, \u03c0\u03bf\u03cd \u03b2\u03bf\u03b7\u03b8\u03ac \u03c4\u03bf LoRA \u03ba\u03b1\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03ba\u03ac\u03b8\u03b5 deployment \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b9\u03ba\u03ac \u03c4\u03bf\u03c5 quality \u03ba\u03b1\u03b9 latency tests.<\/p>","protected":false},"author":1,"featured_media":87717,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[17366],"tags":[8988,7204,18352,3975,3597],"class_list":["post-87616","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-marketing","tag-ai-infrastructure","tag-generative-ai","tag-large-language-models","tag-epicheirimatiki-technologia","tag-techniti-noimosyni"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/87616","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=87616"}],"version-history":[{"count":0,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/87616\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media\/87717"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media?parent=87616"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/categories?post=87616"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/tags?post=87616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}