{"id":11344,"date":"2026-10-04T00:25:59","date_gmt":"2026-10-04T00:25:59","guid":{"rendered":"https:\/\/bitep.net\/blog\/2026\/10\/04\/mediumcom\/"},"modified":"2026-10-04T00:25:59","modified_gmt":"2026-10-04T00:25:59","slug":"mediumcom","status":"publish","type":"post","link":"https:\/\/bitep.net\/blog\/2026\/10\/04\/mediumcom\/","title":{"rendered":"medium.com"},"content":{"rendered":"<h2>S\u00fcni \u0130ntellektd\u0259 Yeni M\u0259rh\u0259l\u0259: M\u0259tn Qenerasiyas\u0131ndan M\u00fcr\u0259kk\u0259b MoE v\u0259 Agentik Sisteml\u0259r\u0259 Ke\u00e7id<\/h2>\n<p>Qlobal s\u00fcni intellekt ekosistemi sad\u0259 dil modell\u0259rinin m\u0259tn generasiyas\u0131 etdiyi d\u00f6vr\u00fc s\u00fcr\u0259tl\u0259 gerid\u0259 qoyur. M\u00fcasir t\u0259dqiqatlar v\u0259 s\u0259naye t\u0259tbiql\u0259ri art\u0131q birba\u015fa d\u0259rin m\u0259ntiqi m\u00fchakim\u0259 (advanced reasoning), avtonom q\u0259rarverm\u0259 qabiliyy\u0259tin\u0259 malik agentik i\u015f ax\u0131nlar\u0131 (agentic workflows) v\u0259 n\u0259h\u0259ng miqyasl\u0131 M\u00fct\u0259x\u0259ssisl\u0259r Qar\u0131\u015f\u0131\u011f\u0131 (Mixture-of-Experts &#8211; MoE) arxitekturalar\u0131na fokuslan\u0131b. X\u00fcsusil\u0259 Moonshot AI \u015firk\u0259tinin Kimi seriyas\u0131 kimi a\u00e7\u0131q v\u0259 qapal\u0131 \u00e7\u0259kili modell\u0259r parametrl\u0259rin say\u0131n\u0131 trilyonlar s\u0259viyy\u0259sin\u0259 \u00e7atd\u0131raraq s\u00fcni intellektin miqyaslanma f\u0259ls\u0259f\u0259sini k\u00f6k\u00fcnd\u0259n d\u0259yi\u015fib.<\/p>\n<h2>Mixture-of-Experts (MoE) Arxitekturas\u0131n\u0131n Texniki Mahiyy\u0259ti<\/h2>\n<p>\u018fn\u0259n\u0259vi s\u0131x (dense) neyron \u015f\u0259b\u0259k\u0259l\u0259rind\u0259 h\u0259r bir daxil olan token \u00fc\u00e7\u00fcn modelin b\u00fct\u00fcn parametrl\u0259ri hesablamaya c\u0259lb olunur. Bu yana\u015fma model b\u00f6y\u00fcd\u00fckc\u0259 h\u0259nd\u0259si silsil\u0259 il\u0259 artan hesablama x\u0259rcl\u0259rin\u0259 v\u0259 gecikm\u0259l\u0259r\u0259 (latency) s\u0259b\u0259b olur. MoE arxitekturas\u0131 is\u0259 bu problemi seyr\u0259k aktivasiya (sparse activation) mexanizmi il\u0259 h\u0259ll edir.<\/p>\n<p>MoE arxitekturas\u0131nda \u015f\u0259b\u0259k\u0259 bir ne\u00e7\u0259 alt-\u015f\u0259b\u0259k\u0259y\u0259 \u2014 &#8220;m\u00fct\u0259x\u0259ssis\u0259&#8221; (expert) b\u00f6l\u00fcn\u00fcr v\u0259 prosesi y\u00f6nl\u0259ndirici qap\u0131 \u015f\u0259b\u0259k\u0259si (gating\/routing network) idar\u0259 edir:<\/p>\n<ul>\n<li><strong>Y\u00f6nl\u0259ndirm\u0259 \u015e\u0259b\u0259k\u0259si (Router\/Gating Network):<\/strong> Daxil olan h\u0259r bir tokenin m\u0259zmununu t\u0259hlil edir v\u0259 onun emal\u0131 \u00fc\u00e7\u00fcn \u0259n uy\u011fun m\u00fct\u0259x\u0259ssisl\u0259ri se\u00e7ir.<\/li>\n<li><strong>Top-K Se\u00e7imi:<\/strong> Trilyonlarla parametr\u0259 malik modeld\u0259 ad\u0259t\u0259n h\u0259r token \u00fc\u00e7\u00fcn yaln\u0131z 2 v\u0259 ya 4 m\u00fct\u0259x\u0259ssis (Top-2 v\u0259 ya Top-4) aktivl\u0259\u015fdirilir.<\/li>\n<li><strong>Hesablama S\u0259m\u0259r\u0259liliyi:<\/strong> Model \u00fcmumilikd\u0259 1-2 trilyon parametr\u0259 malik olsa bel\u0259, aktiv parametrl\u0259rin h\u0259cmi ki\u00e7ik model s\u0259viyy\u0259sind\u0259 qal\u0131r v\u0259 bu da FLOPs (Floating Point Operations) x\u0259rcl\u0259rini minimuma endirir.<\/li>\n<\/ul>\n<h2>\u0130nfrastruktur B\u00f6hran\u0131: GPU Klasterl\u0259rinin Limitl\u0259ri<\/h2>\n<p>MoE arxitekturalar\u0131 h\u0259r token \u00fc\u00e7\u00fcn hesablama \u0259m\u0259liyyatlar\u0131n\u0131 azaltsa da, aparat t\u0259minat\u0131 s\u0259viyy\u0259sind\u0259 ciddi infrastruktur \u00e7a\u011f\u0131r\u0131\u015flar\u0131 yarad\u0131r. B\u00fct\u00fcn m\u00fct\u0259x\u0259ssisl\u0259rin \u00e7\u0259kil\u0259ri (weights) video yadda\u015fda (VRAM) saxlan\u0131lmal\u0131d\u0131r. Trilyonluq parametrl\u0259r\u0259 malik sisteml\u0259rin yerl\u0259\u015fdirilm\u0259si NVIDIA H100 v\u0259 ya B200 kimi qabaqc\u0131l qrafik prosessor klasterl\u0259rini bel\u0259 texniki h\u0259dl\u0259rin\u0259 \u00e7atd\u0131r\u0131r.<\/p>\n<p>\u018fsas texniki darbo\u011fazlar a\u015fa\u011f\u0131dak\u0131lard\u0131r:<\/p>\n<ol>\n<li><strong>Yadda\u015f H\u0259cmi v\u0259 \u00d6t\u00fcrm\u0259 Zola\u011f\u0131 (Memory Bandwidth):<\/strong> M\u00fct\u0259x\u0259ssis \u00e7\u0259kil\u0259rinin dinamik \u015f\u0259kild\u0259 aktivl\u0259\u015fm\u0259si yadda\u015f \u00f6t\u00fcrm\u0259 zola\u011f\u0131nda b\u00f6y\u00fck y\u00fckl\u0259nm\u0259l\u0259r yarad\u0131r.<\/li>\n<li><strong>\u015e\u0259b\u0259k\u0259l\u0259raras\u0131 Gecikm\u0259 (Inter-node Communication):<\/strong> M\u00fct\u0259x\u0259ssisl\u0259r m\u00fcxt\u0259lif GPU d\u00fcy\u00fcnl\u0259rin\u0259 payland\u0131qda (Expert Parallelism), tokenl\u0259rin laz\u0131mi GPU-ya \u00f6t\u00fcr\u00fclm\u0259si \u00fc\u00e7\u00fcn y\u00fcks\u0259k s\u00fcr\u0259tli NVLink v\u0259 InfiniBand ba\u011flant\u0131lar\u0131 kritik amil\u0259 \u00e7evrilir.<\/li>\n<li><strong>All-to-All Kommunikasiya T\u0259l\u0259bi:<\/strong> Router mexanizminin tokenl\u0259ri m\u00fct\u0259x\u0259ssisl\u0259r aras\u0131nda paylamas\u0131 intensiv \u015f\u0259b\u0259k\u0259 m\u00fcbadil\u0259si t\u0259l\u0259b edir, bu da klaster miqyasland\u0131qca \u015f\u0259b\u0259k\u0259 daralmalar\u0131na s\u0259b\u0259b olur.<\/li>\n<\/ol>\n<h2>Dense v\u0259 Sparse MoE Arxitekturalar\u0131n\u0131n M\u00fcqayis\u0259si<\/h2>\n<table border=\"1\" cellpadding=\"8\" cellspacing=\"0\">\n<thead>\n<tr>\n<th>X\u00fcsusiyy\u0259t<\/th>\n<th>\u018fn\u0259n\u0259vi S\u0131x (Dense) Modell\u0259r<\/th>\n<th>M\u00fct\u0259x\u0259ssisl\u0259r Qar\u0131\u015f\u0131\u011f\u0131 (MoE)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Parametr Say\u0131<\/td>\n<td>Ad\u0259t\u0259n 7B &#8211; 70B aral\u0131\u011f\u0131nda m\u0259hdudla\u015f\u0131r<\/td>\n<td>Y\u00fcz milyardlarla v\u0259 ya trilyonlarla parametr<\/td>\n<\/tr>\n<tr>\n<td>Token Ba\u015f\u0131na Aktiv Hesablama<\/td>\n<td>Parametrl\u0259rin 100%-i aktivl\u0259\u015fir<\/td>\n<td>Parametrl\u0259rin yaln\u0131z 5-15%-i aktivl\u0259\u015fir<\/td>\n<\/tr>\n<tr>\n<td>VRAM T\u0259l\u0259bat\u0131<\/td>\n<td>Modelin \u00e7\u0259kisi il\u0259 m\u00fct\u0259nasibdir<\/td>\n<td>B\u00fct\u00fcn m\u00fct\u0259x\u0259ssisl\u0259ri saxlamaq \u00fc\u00e7\u00fcn h\u0259ddind\u0259n art\u0131q y\u00fcks\u0259k VRAM laz\u0131md\u0131r<\/td>\n<\/tr>\n<tr>\n<td>\u0130nferensiya S\u00fcr\u0259ti<\/td>\n<td>B\u00f6y\u00fck modell\u0259rd\u0259 a\u015fa\u011f\u0131d\u0131r<\/td>\n<td>Aktiv parametr azl\u0131\u011f\u0131na g\u00f6r\u0259 daha s\u00fcr\u0259tli hesablama<\/td>\n<\/tr>\n<tr>\n<td>\u0130nfrastruktur \u00c7\u0259tinliyi<\/td>\n<td>Standart paralell\u0259\u015fdirm\u0259 kifay\u0259tdir<\/td>\n<td>D\u0259rin Expert Parallelism v\u0259 ultra-s\u00fcr\u0259tli \u015f\u0259b\u0259k\u0259 t\u0259l\u0259b olunur<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Praktiki B\u0259l\u0259d\u00e7i: Sad\u0259 MoE Router Mexanizminin Simulyasiyas\u0131<\/h2>\n<p>MoE arxitekturas\u0131n\u0131n t\u0259m\u0259l i\u015f prinsipini anlamaq \u00fc\u00e7\u00fcn PyTorch m\u00fchitind\u0259 sad\u0259 y\u00f6nl\u0259ndirm\u0259 (routing) mexanizmini qurmaq m\u00fcmk\u00fcnd\u00fcr. A\u015fa\u011f\u0131dak\u0131 add\u0131mlarla z\u0259ruri m\u00fchiti t\u0259min edib s\u0131naqdan ke\u00e7ir\u0259 bil\u0259rsiniz.<\/p>\n<h3>1. Virtual M\u00fchitin Haz\u0131rlanmas\u0131 v\u0259 As\u0131l\u0131l\u0131qlar\u0131n Qura\u015fd\u0131r\u0131lmas\u0131<\/h3>\n<pre><code class=\"language-bash\"># Virtual m\u00fchit yarad\u0131n v\u0259 aktivl\u0259\u015fdirin\npython3 -m venv moe_env\nsource moe_env\/bin\/activate\n\n# T\u0259l\u0259b olunan kitabxanalar\u0131 qura\u015fd\u0131r\u0131n\npip install torch numpy\n<\/code><\/pre>\n<h3>2. Sad\u0259 Top-K MoE \u015e\u0259b\u0259k\u0259sinin Qurulmas\u0131<\/h3>\n<p>A\u015fa\u011f\u0131dak\u0131 Python skripti (moe_core.py) token daxiletm\u0259sini q\u0259bul ed\u0259n, \u0259n y\u00fcks\u0259k ehtimala malik m\u00fct\u0259x\u0259ssisl\u0259ri se\u00e7\u0259n v\u0259 n\u0259tic\u0259ni birl\u0259\u015fdir\u0259n router mexanizmini \u0259ks etdirir:<\/p>\n<pre><code class=\"language-bash\">cat &lt;&lt; 'EOF' &gt; moe_core.py\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass Expert(nn.Module):\n    def __init__(self, d_model, d_ff):\n        super().__init__()\n        self.net = nn.Sequential(\n            nn.Linear(d_model, d_ff),\n            nn.ReLU(),\n            nn.Linear(d_ff, d_model)\n        )\n    def forward(self, x):\n        return self.net(x)\n\nclass SimpleMoERouter(nn.Module):\n    def __init__(self, d_model, num_experts, top_k=2):\n        super().__init__()\n        self.num_experts = num_experts\n        self.top_k = top_k\n        self.gate = nn.Linear(d_model, num_experts)\n        self.experts = nn.ModuleList([Expert(d_model, d_model * 4) for _ in range(num_experts)])\n\n    def forward(self, x):\n        batch_size, seq_len, d_model = x.shape\n        x_flat = x.view(-1, d_model)\n        \n        # Router \u00e7\u0131x\u0131\u015flar\u0131n\u0131 v\u0259 m\u00fct\u0259x\u0259ssis \u00e7\u0259kil\u0259rini hesablamaq\n        gate_logits = self.gate(x_flat)\n        weights, indices = torch.topk(F.softmax(gate_logits, dim=-1), self.top_k)\n        \n        # N\u0259tic\u0259ni birl\u0259\u015fdirm\u0259k\n        final_output = torch.zeros_like(x_flat)\n        for i in range(self.top_k):\n            expert_idx = indices[:, i]\n            expert_weight = weights[:, i].unsqueeze(1)\n            \n            for e in range(self.num_experts):\n                mask = (expert_idx == e)\n                if mask.any():\n                    selected_inputs = x_flat[mask]\n                    out = self.experts[e](selected_inputs)\n                    final_output[mask] += out * expert_weight[mask]\n                    \n        return final_output.view(batch_size, seq_len, d_model)\n\nif __name__ == \"__main__\":\n    # Test parametrl\u0259ri\n    tokens = torch.randn(2, 4, 128) # Batch=2, Seq=4, Dim=128\n    moe = SimpleMoERouter(d_model=128, num_experts=8, top_k=2)\n    output = moe(tokens)\n    print(\"Giri\u015f \u00f6l\u00e7\u00fcs\u00fc:\", tokens.shape)\n    print(\"MoE \u00e7\u0131x\u0131\u015f \u00f6l\u00e7\u00fcs\u00fc:\", output.shape)\nEOF\npython3 moe_core.py\n<\/code><\/pre>\n<h2>S\u0259nayey\u0259 v\u0259 Son \u0130stifad\u0259\u00e7il\u0259r\u0259 T\u0259siri<\/h2>\n<p>MoE arxitekturalar\u0131na v\u0259 agentik strukturlara ke\u00e7id texnologiya sektorunda bir s\u0131ra k\u00f6kl\u00fc d\u0259yi\u015fiklikl\u0259r\u0259 s\u0259b\u0259b olur:<\/p>\n<ul>\n<li><strong>Daha S\u00fcr\u0259tli v\u0259 D\u0259qiq Agentik \u0130\u015f Ax\u0131nlar\u0131:<\/strong> Bir ne\u00e7\u0259 add\u0131ml\u0131 planla\u015fd\u0131rma t\u0259l\u0259b ed\u0259n proqramla\u015fd\u0131rma, h\u00fcquqi analiz v\u0259 riyazi m\u00fchakim\u0259 t\u0259l\u0259b ed\u0259n tap\u015f\u0131r\u0131qlar art\u0131q gecikm\u0259 olmadan icra olunur.<\/li>\n<li><strong>Hesablama X\u0259rcl\u0259rinin Yenid\u0259n B\u00f6l\u00fcnm\u0259si:<\/strong> Modelin t\u0259limi (training) y\u00fcks\u0259k enerji t\u0259l\u0259b ets\u0259 d\u0259, inferensiya m\u0259rh\u0259l\u0259sind\u0259 h\u0259r sor\u011fu \u00fc\u00e7\u00fcn x\u0259rcl\u0259n\u0259n v\u0259sait n\u0259z\u0259r\u0259\u00e7arpacaq d\u0259r\u0259c\u0259d\u0259 azal\u0131r.<\/li>\n<li><strong>\u0130nfrastruktur As\u0131l\u0131l\u0131\u011f\u0131:<\/strong> N\u0259h\u0259ng modell\u0259ri yerli serverl\u0259rd\u0259 (on-premise) i\u015f\u0259 salmaq yaln\u0131z y\u00fcks\u0259k \u00f6t\u00fcrm\u0259 qabiliyy\u0259tin\u0259 v\u0259 b\u00f6y\u00fck VRAM-a malik korporativ sisteml\u0259r \u00fc\u00e7\u00fcn \u0259l\u00e7atan qal\u0131r. Bu is\u0259 bulud provayderl\u0259rin\u0259 t\u0259l\u0259bat\u0131 art\u0131r\u0131r.<\/li>\n<\/ul>\n<p>N\u0259tic\u0259 etibaril\u0259, s\u00fcni intellektin inki\u015faf\u0131 art\u0131q t\u0259kc\u0259 model parametrl\u0259rinin b\u00f6y\u00fcd\u00fclm\u0259si il\u0259 deyil, hesablama resurslar\u0131n\u0131n a\u011f\u0131ll\u0131 paylanmas\u0131, seyr\u0259k aktivasiya mexanizml\u0259ri v\u0259 aparat t\u0259minat\u0131n\u0131n optimalla\u015fd\u0131r\u0131lmas\u0131 il\u0259 birba\u015fa \u0259laq\u0259lidir. MoE v\u0259 agentik arxitekturalar bu transformasiyan\u0131n \u0259sas h\u0259r\u0259k\u0259tverici q\u00fcvv\u0259si olma\u011fa davam ed\u0259c\u0259k.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>S\u00fcni \u0130ntellektd\u0259 Yeni M\u0259rh\u0259l\u0259: M\u0259tn Qenerasiyas\u0131ndan M\u00fcr\u0259kk\u0259b MoE v\u0259 Agentik Sisteml\u0259r\u0259 Ke\u00e7id Qlobal s\u00fcni intellekt ekosistemi sad\u0259 dil modell\u0259rinin m\u0259tn generasiyas\u0131 etdiyi d\u00f6vr\u00fc s\u00fcr\u0259tl\u0259 gerid\u0259 qoyur. M\u00fcasir t\u0259dqiqatlar v\u0259 s\u0259naye t\u0259tbiql\u0259ri art\u0131q birba\u015fa d\u0259rin m\u0259ntiqi m\u00fchakim\u0259 (advanced reasoning), avtonom q\u0259rarverm\u0259 qabiliyy\u0259tin\u0259 malik agentik i\u015f ax\u0131nlar\u0131 (agentic workflows) v\u0259 n\u0259h\u0259ng miqyasl\u0131 M\u00fct\u0259x\u0259ssisl\u0259r Qar\u0131\u015f\u0131\u011f\u0131 (Mixture-of-Experts &#8211; MoE) [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":11343,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-11344","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/bitep.net\/blog\/wp-content\/uploads\/2026\/10\/bitep-ai-1791073554425-8.png","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/posts\/11344","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/comments?post=11344"}],"version-history":[{"count":0,"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/posts\/11344\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/media\/11343"}],"wp:attachment":[{"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/media?parent=11344"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/categories?post=11344"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bitep.net\/blog\/wp-json\/wp\/v2\/tags?post=11344"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}