{"id":2350,"date":"2026-09-03T12:31:53","date_gmt":"2026-09-03T10:31:53","guid":{"rendered":"https:\/\/jsoncrew.com\/2026\/09\/03\/prywatny-llm-w-firmie-on-premise\/"},"modified":"2026-10-09T23:17:56","modified_gmt":"2026-10-09T21:17:56","slug":"prywatny-llm-w-firmie-on-premise","status":"publish","type":"post","link":"https:\/\/jsoncrew.com\/en\/2026\/09\/03\/prywatny-llm-w-firmie-on-premise\/","title":{"rendered":"Private LLM in the company: when to put the AI model on-premise (2026)"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Coraz wi\u0119cej polskich firm pyta o \u201eprywatny LLM&#8221;: model j\u0119zykowy postawiony we w\u0142asnej infrastrukturze, do kt\u00f3rego dane nie opuszczaj\u0105 Twojej serwerowni. Powod\u00f3w jest kilka: RODO w regulowanych bran\u017cach, ryzyko wycieku wra\u017cliwych danych do chmury publicznej, rosn\u0105ce rachunki za API przy skali i ch\u0119\u0107 uniezale\u017cnienia si\u0119 od jednego dostawcy. Ale prywatny LLM to nie zawsze dobra decyzja i nie zawsze ta\u0144sza.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In this article, we show, czym r\u00f3\u017cni si\u0119 prywatny LLM od publicznego, kiedy warto postawi\u0107 go on-premise, jakie s\u0105 realne koszty (kalkulator poni\u017cej), kt\u00f3re modele open source s\u0105 dzi\u015b na poziomie GPT-4o oraz jak zaprojektowa\u0107 bezpieczn\u0105 architektur\u0119. To rozszerzenie tematu, kt\u00f3ry zacz\u0119li\u015bmy w artykule <a href=\"https:\/\/jsoncrew.com\/automatyzacja-ai-chatgpt-alternatywy\/\">AI Automation with ChatGPT? 6 alternatives for companies in 2026<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What is Private LLM<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Prywatny LLM (private LLM, self-hosted LLM) to model j\u0119zykowy, kt\u00f3ry dzia\u0142a na infrastrukturze kontrolowanej przez Twoj\u0105 firm\u0119, nie na serwerach dostawcy typu OpenAI, Anthropic czy Google. Kluczowa r\u00f3\u017cnica: dane wysy\u0142ane do modelu i odpowiedzi, kt\u00f3re generuje, nigdy nie wychodz\u0105 poza granice Twojej sieci.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">W praktyce \u201eprywatny LLM&#8221; pojawia si\u0119 w czterech wariantach:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>On-premise<\/strong>, czyli w\u0142asny serwer z GPU stoj\u0105cy w Twojej serwerowni albo colocation. Pe\u0142na kontrola, brak transferu poza budynek.<\/li>\n<li><strong>VPC in the public cloud<\/strong> (AWS, Azure, GCP), gdzie model dzia\u0142a w wydzielonej wirtualnej sieci, do kt\u00f3rej masz wy\u0142\u0105czny dost\u0119p. Fizycznie w chmurze, logicznie \u201eu Ciebie&#8221;.<\/li>\n<li><strong>Hosting in the Polish cloud<\/strong> (OVH, Beyond.pl, Netia, Atman), where the GPU server is located in a Polish data center and is subject to Polish jurisdiction.<\/li>\n<li><strong>Hybrid<\/strong>, gdzie prywatny LLM obs\u0142uguje dane wra\u017cliwe, a publiczne API (GPT-4o, Claude) obs\u0142uguje wszystko inne. Dwa modele, jedna warstwa orkiestracji.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">W ka\u017cdym z tych wariant\u00f3w silnikiem jest zwykle model open source, kt\u00f3rego wagi mo\u017cesz pobra\u0107 i uruchomi\u0107. Najcz\u0119\u015bciej: Llama 3.3 (Meta), Mistral Large 2 (Mistral AI), Qwen 2.5 (Alibaba) albo DeepSeek-V3.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Pi\u0119\u0107 sygna\u0142\u00f3w, \u017ce publiczny LLM Ci nie wystarczy<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Prywatny LLM to du\u017ca inwestycja, wi\u0119c zanim go zaproponujemy klientowi, sprawdzamy, czy publiczny model naprawd\u0119 nie da rady. Oto pi\u0119\u0107 sygna\u0142\u00f3w, kt\u00f3re w praktyce przechylaj\u0105 decyzj\u0119 na on-premise:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Compliance prohibits.<\/strong> Sektor medyczny (dane osobowe pacjent\u00f3w), prawny (tajemnica zawodowa), obronny (klauzule tajno\u015bci), finansowy (RODO + tajemnica bankowa), publiczny (KRI, ustawy sektorowe) cz\u0119sto wykluczaj\u0105 chmur\u0119 zewn\u0119trzn\u0105 w regulaminach wewn\u0119trznych. Ten temat rozwin\u0119li\u015bmy w opracowaniu <a href=\"https:\/\/jsoncrew.com\/ai-dla-prawnikow-pulapki-kancelaria\/\">AI dla prawnik\u00f3w: 5 pu\u0142apek<\/a>.<\/li>\n<li><strong>The client requires in the contract.<\/strong> W przetargach publicznych i du\u017cych kontraktach B2B coraz cz\u0119\u015bciej pojawia si\u0119 klauzula \u201edane nie mog\u0105 opuszcza\u0107 granic Polski\/UE&#8221; albo \u201eprzetwarzanie tylko na infrastrukturze wykonawcy&#8221;.<\/li>\n<li><strong>Rachunek za API ro\u015bnie liniowo z u\u017cyciem.<\/strong> Kiedy miesi\u0119czny koszt GPT-4o przekracza 20-30 tysi\u0119cy z\u0142otych, matematyka zaczyna faworyzowa\u0107 w\u0142asn\u0105 infrastruktur\u0119.<\/li>\n<li><strong>Potrzebujesz fine-tuningu na wra\u017cliwych danych.<\/strong> Trening modelu na dokumentach firmowych w publicznym API oznacza wgranie tych dokument\u00f3w do dostawcy. Prywatny model rozwi\u0105zuje ten problem.<\/li>\n<li><strong>Krytyczne procesy nie mog\u0105 zale\u017ce\u0107 od zewn\u0119trznego API.<\/strong> Publiczne API maj\u0105 downtime (OpenAI kilka razy w 2024 i 2025), zmieniaj\u0105 ceny (GPT-4 potania\u0142 10x, potem zn\u00f3w podro\u017ca\u0142 w Enterprise), zmieniaj\u0105 modele (deprecation). On-premise = pe\u0142na kontrola nad wersj\u0105.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Kt\u00f3re modele open source mo\u017cna postawi\u0107 lokalnie<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">W 2026 open source LLM dogoni\u0142y komercyjne modele w wi\u0119kszo\u015bci zada\u0144. Poni\u017cej modele, kt\u00f3re faktycznie warto rozwa\u017cy\u0107 do wdro\u017cenia produkcyjnego.<\/p>\n\n\n\n<div class=\"pll-models-wrap\">\n<style>\n.pll-models-wrap{margin:2rem 0;overflow-x:auto;}\n.pll-models{width:100%;border-collapse:collapse;font-size:0.95rem;min-width:800px;}\n.pll-models th,.pll-models td{padding:0.75rem 0.9rem;text-align:left;border-bottom:1px solid #e5e7eb;vertical-align:top;}\n.pll-models thead th{background:#0c1322;color:#fff;font-weight:600;}\n.pll-models tbody tr:nth-child(even){background:#f9fafb;}\n.pll-models .pll-badge{display:inline-block;padding:2px 8px;border-radius:4px;font-size:0.75rem;font-weight:700;color:#fff;}\n.pll-models .pll-b-meta{background:#1877f2;}\n.pll-models .pll-b-mistral{background:#ff7000;}\n.pll-models .pll-b-alibaba{background:#ff6a00;}\n.pll-models .pll-b-deepseek{background:#4d6bfe;}\n.pll-models .pll-b-msft{background:#5e5e5e;}\n<\/style>\n<table class=\"pll-models\">\n<thead>\n<tr><th>Model<\/th><th>Parameters<\/th><th>Context<\/th><th>J\u0119zyk polski<\/th><th>Kiedy wybra\u0107<\/th><\/tr>\n<\/thead>\n<tbody>\n<tr>\n  <td><strong>Llama 3.3<\/strong> <span class=\"pll-badge pll-b-meta\">Meta<\/span><\/td>\n  <td>70B<\/td>\n  <td>128k<\/td>\n  <td>very good<\/td>\n  <td>uniwersalny wyb\u00f3r, najwi\u0119ksza spo\u0142eczno\u015b\u0107, mn\u00f3stwo narz\u0119dzi ekosystemu<\/td>\n<\/tr>\n<tr>\n  <td><strong>Mistral Large 2<\/strong> <span class=\"pll-badge pll-b-mistral\">Mistral AI<\/span><\/td>\n  <td>123B<\/td>\n  <td>128k<\/td>\n  <td>Good<\/td>\n  <td>europejski dostawca, dobra jako\u015b\u0107 reasoning, licencja komercyjna wymagana<\/td>\n<\/tr>\n<tr>\n  <td><strong>Qwen 2.5<\/strong> <span class=\"pll-badge pll-b-alibaba\">Alibaba<\/span><\/td>\n  <td>72B, 32B<\/td>\n  <td>128k<\/td>\n  <td>\u015bredni<\/td>\n  <td>najlepszy jako\u015bciowo w klasie, ale ryzyko geopolityczne (Chiny)<\/td>\n<\/tr>\n<tr>\n  <td><strong>DeepSeek-V3<\/strong> <span class=\"pll-badge pll-b-deepseek\">DeepSeek<\/span><\/td>\n  <td>671B (MoE, 37B active)<\/td>\n  <td>128k<\/td>\n  <td>\u015bredni<\/td>\n  <td>najbardziej efektywny w klasie ceny, wymaga wi\u0119cej pami\u0119ci ni\u017c zwyk\u0142y 70B<\/td>\n<\/tr>\n<tr>\n  <td><strong>Phi-4<\/strong> <span class=\"pll-badge pll-b-msft\">Microsoft<\/span><\/td>\n  <td>14B<\/td>\n  <td>16k<\/td>\n  <td>s\u0142aby<\/td>\n  <td>gdy potrzebujesz ma\u0142ego modelu do zada\u0144 specyficznych, dzia\u0142a na jednej A100<\/td>\n<\/tr>\n<tr>\n  <td><strong>Bielik<\/strong> <span class=\"pll-badge pll-b-mistral\">SpeakLeash + Cyfronet<\/span><\/td>\n  <td>11B<\/td>\n  <td>32k<\/td>\n  <td>wybitny (polski)<\/td>\n  <td>polska adaptacja Mistrala, najlepszy jako\u015bciowo w polskim w\u015br\u00f3d open source<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Wyb\u00f3r modelu wp\u0142ywa bezpo\u015brednio na wymagania sprz\u0119towe. Nie da si\u0119 uruchomi\u0107 Llamy 3.3 70B na laptopie, tak samo jak nie ma sensu stawia\u0107 rack serwera z 8x H100 dla modelu 14B. Kalkulator poni\u017cej pokazuje, co jest realnie potrzebne.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Dobierak GPU: kt\u00f3ry sprz\u0119t do kt\u00f3rego modelu<\/h2>\n\n\n\n<div id=\"pll-gpu\" class=\"pll-gpu-wrap\">\n<style>\n.pll-gpu-wrap{background:#0c1322;color:#fff;padding:2rem;border-radius:12px;margin:2rem 0;font-family:-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;}\n.pll-gpu-wrap h3{color:#d4b06b;margin:0 0 0.5rem;font-size:1.4rem;}\n.pll-gpu-desc{color:#9ca3af;font-size:0.95rem;margin-bottom:1.5rem;}\n.pll-gpu-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:1rem;margin-bottom:1.5rem;}\n.pll-gpu-field label{display:block;font-size:0.8rem;color:#9ca3af;text-transform:uppercase;letter-spacing:0.05em;margin-bottom:0.4rem;font-weight:600;}\n.pll-gpu-field select,.pll-gpu-field input[type=range]{width:100%;padding:0.6rem;border-radius:6px;border:none;background:#1a2340;color:#fff;font-family:inherit;font-size:0.95rem;}\n.pll-gpu-field input[type=range]{padding:0;accent-color:#d4b06b;}\n.pll-gpu-value{color:#d4b06b;font-size:1.2rem;font-weight:600;margin-bottom:0.25rem;}\n.pll-gpu-result{background:#1a2340;border-radius:8px;padding:1.5rem;display:grid;grid-template-columns:1fr 1fr;gap:1rem;}\n.pll-gpu-box{padding:0.75rem;border-left:3px solid #d4b06b;}\n.pll-gpu-box-label{color:#9ca3af;font-size:0.8rem;text-transform:uppercase;letter-spacing:0.05em;margin-bottom:0.4rem;}\n.pll-gpu-box-value{color:#fff;font-size:1.3rem;font-weight:600;}\n.pll-gpu-box-sub{color:#9ca3af;font-size:0.85rem;margin-top:0.25rem;}\n@media (max-width:600px){.pll-gpu-result{grid-template-columns:1fr;}}\n<\/style>\n<h3>Dobierak GPU pod prywatny LLM<\/h3>\n<p class=\"pll-gpu-desc\">Wybierz model i tryb pracy. Zobacz wymagany VRAM, sugerowany zestaw GPU oraz szacowany koszt miesi\u0119czny w polskiej chmurze.<\/p>\n\n<div class=\"pll-gpu-grid\">\n  <div class=\"pll-gpu-field\">\n    <label>Model<\/label>\n    <select id=\"pll-gpu-model\">\n      <option value=\"phi4\">Phi-4 (14B)<\/option>\n      <option value=\"bielik\">Bielik (11B)<\/option>\n      <option value=\"qwen32\">Qwen 2.5 (32B)<\/option>\n      <option value=\"llama70\" selected>Llama 3.3 (70B)<\/option>\n      <option value=\"qwen72\">Qwen 2.5 (72B)<\/option>\n      <option value=\"mistral\">Mistral Large 2 (123B)<\/option>\n      <option value=\"deepseek\">DeepSeek-V3 (671B MoE)<\/option>\n    <\/select>\n  <\/div>\n  <div class=\"pll-gpu-field\">\n    <label>Kwantyzacja<\/label>\n    <select id=\"pll-gpu-quant\">\n      <option value=\"fp16\">FP16 (najlepsza jako\u015b\u0107)<\/option>\n      <option value=\"int8\" selected>INT8 (jako\u015b\u0107 ~99%)<\/option>\n      <option value=\"int4\">INT4 (jako\u015b\u0107 ~95%)<\/option>\n    <\/select>\n  <\/div>\n  <div class=\"pll-gpu-field\">\n    <label>Batch (r\u00f3wnoleg\u0142ych zapyta\u0144)<\/label>\n    <div class=\"pll-gpu-value\" id=\"pll-gpu-batch-val\">4<\/div>\n    <input type=\"range\" id=\"pll-gpu-batch\" min=\"1\" max=\"32\" step=\"1\" value=\"4\">\n  <\/div>\n<\/div>\n\n<div class=\"pll-gpu-result\">\n  <div class=\"pll-gpu-box\">\n    <div class=\"pll-gpu-box-label\">Wymagany VRAM<\/div>\n    <div class=\"pll-gpu-box-value\" id=\"pll-gpu-vram\">\u2014<\/div>\n    <div class=\"pll-gpu-box-sub\" id=\"pll-gpu-vram-sub\">\u2014<\/div>\n  <\/div>\n  <div class=\"pll-gpu-box\">\n    <div class=\"pll-gpu-box-label\">Rekomendacja GPU<\/div>\n    <div class=\"pll-gpu-box-value\" id=\"pll-gpu-rec\">\u2014<\/div>\n    <div class=\"pll-gpu-box-sub\" id=\"pll-gpu-rec-sub\">\u2014<\/div>\n  <\/div>\n  <div class=\"pll-gpu-box\">\n    <div class=\"pll-gpu-box-label\">Koszt miesi\u0119czny w PL chmurze<\/div>\n    <div class=\"pll-gpu-box-value\" id=\"pll-gpu-cost\">\u2014<\/div>\n    <div class=\"pll-gpu-box-sub\">wynajem serwera GPU, brutto<\/div>\n  <\/div>\n  <div class=\"pll-gpu-box\">\n    <div class=\"pll-gpu-box-label\">Zakup sprz\u0119tu on-prem<\/div>\n    <div class=\"pll-gpu-box-value\" id=\"pll-gpu-capex\">\u2014<\/div>\n    <div class=\"pll-gpu-box-sub\">jednorazowo, bez podatk\u00f3w<\/div>\n  <\/div>\n<\/div>\n<\/div>\n\n<script>\n(function(){\n  const models = {\n    phi4:     {name:\"Phi-4 (14B)\",         params:14},\n    bielik:   {name:\"Bielik (11B)\",         params:11},\n    qwen32:   {name:\"Qwen 2.5 (32B)\",       params:32},\n    llama70:  {name:\"Llama 3.3 (70B)\",      params:70},\n    qwen72:   {name:\"Qwen 2.5 (72B)\",       params:72},\n    mistral:  {name:\"Mistral Large (123B)\", params:123},\n    deepseek: {name:\"DeepSeek-V3 (37B akt.)\", params:60}\n  };\n  const quantBytes = {fp16:2, int8:1, int4:0.5};\n\n  function recGPU(vramGB){\n    if(vramGB <= 24)  return {gpu:\"1x RTX 4090 (24GB)\",  cloud: 1800,  capex: 12000};\n    if(vramGB <= 48)  return {gpu:\"1x RTX 6000 Ada (48GB)\", cloud: 3600, capex: 45000};\n    if(vramGB <= 80)  return {gpu:\"1x A100 80GB\",         cloud: 7500,  capex: 90000};\n    if(vramGB <= 160) return {gpu:\"2x A100 80GB\",         cloud: 14000, capex: 180000};\n    if(vramGB <= 320) return {gpu:\"4x A100 80GB\",         cloud: 26000, capex: 340000};\n    if(vramGB <= 640) return {gpu:\"8x A100 80GB\",         cloud: 48000, capex: 650000};\n    if(vramGB <= 1280)return {gpu:\"8x H100 80GB\",         cloud: 90000, capex: 1200000};\n    return                    {gpu:\"cluster wielo-w\u0119z\u0142owy H100\/H200\", cloud: 180000, capex: 2500000};\n  }\n\n  const m  = document.getElementById(\"pll-gpu-model\");\n  const q  = document.getElementById(\"pll-gpu-quant\");\n  const b  = document.getElementById(\"pll-gpu-batch\");\n  const bV = document.getElementById(\"pll-gpu-batch-val\");\n  const vram = document.getElementById(\"pll-gpu-vram\");\n  const vramSub = document.getElementById(\"pll-gpu-vram-sub\");\n  const rec = document.getElementById(\"pll-gpu-rec\");\n  const recSub = document.getElementById(\"pll-gpu-rec-sub\");\n  const cost = document.getElementById(\"pll-gpu-cost\");\n  const capex = document.getElementById(\"pll-gpu-capex\");\n  const fmt = n => n.toLocaleString(\"pl-PL\",{maximumFractionDigits:0}).replace(\/,\/g,\" \");\n\n  function render(){\n    const model = models[m.value];\n    const bytes = quantBytes[q.value];\n    const batch = +b.value;\n    bV.textContent = batch;\n    const baseVram   = model.params * bytes;                     \/\/ wagi\n    const kvCacheGB  = 0.5 * batch * (model.params\/70);          \/\/ proste przyblizenie KV\n    const overheadGB = 2;                                        \/\/ system+bufor\n    const totalVram  = Math.ceil(baseVram + kvCacheGB + overheadGB);\n    vram.textContent = totalVram + \" GB\";\n    vramSub.textContent = `wagi ${Math.round(baseVram)} GB + KV cache ${kvCacheGB.toFixed(1)} GB + system ${overheadGB} GB`;\n    const g = recGPU(totalVram);\n    rec.textContent = g.gpu;\n    recSub.textContent = g.gpu.includes(\"cluster\") ? \"wymaga in\u017cynierii sieci i orkiestracji\" : \"1 serwer wystarczy\";\n    cost.textContent = fmt(g.cloud) + \" PLN\/mc\";\n    capex.textContent= fmt(g.capex) + \" PLN\";\n  }\n  m.addEventListener(\"change\", render);\n  q.addEventListener(\"change\", render);\n  b.addEventListener(\"input\",  render);\n  render();\n})();\n<\/script>\n\n\n\n<p class=\"wp-block-paragraph\">Kilka obserwacji z praktyki. Batch wi\u0119kszy ni\u017c 1 to must, je\u015bli model ma obs\u0142u\u017cy\u0107 wielu u\u017cytkownik\u00f3w jednocze\u015bnie, ale ka\u017cda dodatkowa r\u00f3wnoleg\u0142a sesja zjada VRAM na KV cache. Kwantyzacja INT8 to standard produkcyjny (jako\u015b\u0107 praktycznie nierozr\u00f3\u017cnialna od FP16 dla wi\u0119kszo\u015bci zada\u0144), INT4 stosujemy tylko w bardzo ograniczonych scenariuszach jako\u015bciowych. Modele MoE (DeepSeek) maj\u0105 aktywne tylko ~10% parametr\u00f3w w danym momencie, ale wagi trzeba trzyma\u0107 wszystkie w VRAM.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Kalkulator TCO: on-premise vs API publiczne<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Podstawowe pytanie: kiedy on-premise si\u0119 op\u0142aca? Odpowied\u017a zale\u017cy od wolumenu. Poni\u017cej kalkulator pokazuje break-even point na 3 lata (typowy horyzont amortyzacji sprz\u0119tu). Kalkulator zak\u0142ada Llama 3.3 70B na 1x A100 80GB w polskiej chmurze (7500 z\u0142\/mc) vs GPT-4o API po cenach z wrze\u015bnia 2026.<\/p>\n\n\n\n<div id=\"pll-tco\" class=\"pll-tco-wrap\">\n<style>\n.pll-tco-wrap{background:#fff;border:2px solid #0c1322;border-radius:12px;padding:2rem;margin:2rem 0;}\n.pll-tco-wrap h3{margin:0 0 1rem;color:#0c1322;font-size:1.35rem;}\n.pll-tco-controls{display:grid;grid-template-columns:repeat(auto-fit,minmax(240px,1fr));gap:1.25rem;margin-bottom:1.5rem;}\n.pll-tco-controls label{display:block;font-size:0.85rem;color:#6b7280;margin-bottom:0.4rem;text-transform:uppercase;letter-spacing:0.05em;font-weight:600;}\n.pll-tco-controls .pll-tco-val{color:#0c1322;font-size:1.4rem;font-weight:700;margin-bottom:0.4rem;}\n.pll-tco-controls input[type=range]{width:100%;accent-color:#0c1322;}\n.pll-tco-summary{display:grid;grid-template-columns:1fr 1fr 1fr;gap:1rem;margin-bottom:1.5rem;}\n.pll-tco-card{padding:1.25rem;border-radius:8px;text-align:center;}\n.pll-tco-card.tco-api{background:#fee2e2;}\n.pll-tco-card.tco-prem{background:#dcfce7;}\n.pll-tco-card.tco-diff{background:#0c1322;color:#fff;}\n.pll-tco-card .pll-tco-label{font-size:0.85rem;text-transform:uppercase;letter-spacing:0.05em;margin-bottom:0.4rem;opacity:0.8;font-weight:600;}\n.pll-tco-card .pll-tco-num{font-size:1.6rem;font-weight:700;margin-bottom:0.25rem;}\n.pll-tco-card .pll-tco-sub{font-size:0.85rem;opacity:0.7;}\n.pll-tco-verdict{background:#0c1322;color:#fff;padding:1.25rem;border-radius:8px;text-align:center;font-size:1.05rem;line-height:1.6;}\n.pll-tco-verdict strong{color:#d4b06b;font-size:1.15em;}\n@media (max-width:600px){.pll-tco-summary{grid-template-columns:1fr;}}\n<\/style>\n<h3>TCO 3 lata: prywatny LLM vs publiczne API<\/h3>\n\n<div class=\"pll-tco-controls\">\n  <div>\n    <label>Zapyta\u0144 dziennie<\/label>\n    <div class=\"pll-tco-val\" id=\"pll-tco-q-val\">10 000<\/div>\n    <input type=\"range\" id=\"pll-tco-q\" min=\"100\" max=\"500000\" step=\"100\" value=\"10000\">\n  <\/div>\n  <div>\n    <label>\u015ar. tokeny wej\u015bcia \/ zapytanie<\/label>\n    <div class=\"pll-tco-val\" id=\"pll-tco-in-val\">2 000<\/div>\n    <input type=\"range\" id=\"pll-tco-in\" min=\"200\" max=\"30000\" step=\"100\" value=\"2000\">\n  <\/div>\n  <div>\n    <label>\u015ar. tokeny wyj\u015bcia \/ zapytanie<\/label>\n    <div class=\"pll-tco-val\" id=\"pll-tco-out-val\">500<\/div>\n    <input type=\"range\" id=\"pll-tco-out\" min=\"50\" max=\"5000\" step=\"50\" value=\"500\">\n  <\/div>\n<\/div>\n\n<div class=\"pll-tco-summary\">\n  <div class=\"pll-tco-card tco-api\">\n    <div class=\"pll-tco-label\">GPT-4o API, 3 lata<\/div>\n    <div class=\"pll-tco-num\" id=\"pll-tco-api\">\u2014<\/div>\n    <div class=\"pll-tco-sub\" id=\"pll-tco-api-sub\">\u2014<\/div>\n  <\/div>\n  <div class=\"pll-tco-card tco-prem\">\n    <div class=\"pll-tco-label\">Prywatny LLM (chmura PL), 3 lata<\/div>\n    <div class=\"pll-tco-num\" id=\"pll-tco-prem\">\u2014<\/div>\n    <div class=\"pll-tco-sub\">7500 z\u0142\/mc infra + 3000 z\u0142\/mc DevOps<\/div>\n  <\/div>\n  <div class=\"pll-tco-card tco-diff\">\n    <div class=\"pll-tco-label\">R\u00f3\u017cnica<\/div>\n    <div class=\"pll-tco-num\" id=\"pll-tco-diff\">\u2014<\/div>\n    <div class=\"pll-tco-sub\" id=\"pll-tco-diff-sub\">\u2014<\/div>\n  <\/div>\n<\/div>\n\n<div class=\"pll-tco-verdict\" id=\"pll-tco-verdict\">\u2014<\/div>\n<\/div>\n\n<script>\n(function(){\n  const USD = 4.00;\n  const API_IN = 2.50, API_OUT = 10.00; \/\/ GPT-4o per 1M tokens\n  const PREM_MONTHLY = 7500 + 3000; \/\/ infra + DevOps (cz\u0119\u015b\u0107 etatu)\n\n  const q  = document.getElementById(\"pll-tco-q\");\n  const iN = document.getElementById(\"pll-tco-in\");\n  const o  = document.getElementById(\"pll-tco-out\");\n  const qV = document.getElementById(\"pll-tco-q-val\");\n  const iV = document.getElementById(\"pll-tco-in-val\");\n  const oV = document.getElementById(\"pll-tco-out-val\");\n  const apiEl = document.getElementById(\"pll-tco-api\");\n  const apiSub= document.getElementById(\"pll-tco-api-sub\");\n  const premEl= document.getElementById(\"pll-tco-prem\");\n  const diffEl= document.getElementById(\"pll-tco-diff\");\n  const diffSub= document.getElementById(\"pll-tco-diff-sub\");\n  const verd  = document.getElementById(\"pll-tco-verdict\");\n  const fmt = n => n.toLocaleString(\"pl-PL\",{maximumFractionDigits:0}).replace(\/,\/g,\" \");\n\n  function render(){\n    const qN=+q.value, inN=+iN.value, outN=+o.value;\n    qV.textContent=fmt(qN); iV.textContent=fmt(inN); oV.textContent=fmt(outN);\n    const monthlyApi = ((qN*30*inN\/1e6)*API_IN + (qN*30*outN\/1e6)*API_OUT) * USD;\n    const tcoApi     = monthlyApi * 36;\n    const tcoPrem    = PREM_MONTHLY * 36;\n    const diff       = tcoApi - tcoPrem;\n\n    apiEl.textContent  = fmt(tcoApi) + \" PLN\";\n    apiSub.textContent = \"~\" + fmt(monthlyApi) + \" PLN\/mc\";\n    premEl.textContent = fmt(tcoPrem) + \" PLN\";\n    diffEl.textContent = (diff>=0 ? \"+\" : \"\") + fmt(diff) + \" PLN\";\n\n    if(diff > 200000){\n      verd.innerHTML = `<strong>On-premise wygrywa zdecydowanie.<\/strong> Przy tym wolumenie w\u0142asny model zwraca si\u0119 w ${Math.ceil(PREM_MONTHLY*36\/monthlyApi)} miesi\u0119cy, potem generuje oszcz\u0119dno\u015b\u0107 ~${fmt(monthlyApi-PREM_MONTHLY)} PLN\/mc.`;\n      diffSub.textContent = \"on-prem oszcz\u0119dza\";\n    } else if(diff > 0){\n      verd.innerHTML = `<strong>On-premise wygrywa, ale nieznacznie.<\/strong> Realna kalkulacja powinna uwzgl\u0119dni\u0107 koszt migracji (typowo 60-120k jednorazowo) i ryzyko techniczne. Rozwa\u017c hybryd\u0119: prywatny LLM tylko dla danych wra\u017cliwych.`;\n      diffSub.textContent = \"on-prem ta\u0144szy\";\n    } else if(diff > -100000){\n      verd.innerHTML = `<strong>API publiczne wygrywa niewielk\u0105 r\u00f3\u017cnic\u0105.<\/strong> On-premise ma sens tylko je\u015bli wymaga tego compliance albo umowa z klientem, bo nie zwr\u00f3ci si\u0119 finansowo.`;\n      diffSub.textContent = \"API ta\u0144sze\";\n    } else {\n      verd.innerHTML = `<strong>API publiczne wygrywa zdecydowanie.<\/strong> Przy tym wolumenie on-premise to strata pieni\u0119dzy, o ile nie masz twardych wymog\u00f3w RODO albo klauzul kontraktowych, kt\u00f3re wykluczaj\u0105 chmur\u0119 zewn\u0119trzn\u0105.`;\n      diffSub.textContent = \"API du\u017co ta\u0144sze\";\n    }\n  }\n  q.addEventListener(\"input\",render);\n  iN.addEventListener(\"input\",render);\n  o.addEventListener(\"input\",render);\n  render();\n})();\n<\/script>\n\n\n\n<p class=\"wp-block-paragraph\">Kalkulator nie uwzgl\u0119dnia jednej wa\u017cnej rzeczy: kosztu migracji. Postawienie prywatnego LLM to typowo projekt na 60-120 tysi\u0119cy z\u0142otych jednorazowo (in\u017cynieria infrastruktury, warstwa RAG, integracje, testy, monitoring). Dopiero po tym startuje TCO powy\u017cej.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Fine-tuning czy RAG: co wybra\u0107 do prywatnego LLM<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">W momencie, w kt\u00f3rym decydujesz si\u0119 na prywatny LLM, pojawia si\u0119 drugie pytanie: czy dostroi\u0107 model do Twoich danych (fine-tuning), czy podawa\u0107 mu dane w czasie zapytania (RAG). W praktyce niemal zawsze zaczynamy od RAG, o kt\u00f3rym pisali\u015bmy w tek\u015bcie <a href=\"https:\/\/jsoncrew.com\/co-to-jest-rag\/\">Co to jest RAG i dlaczego bez niego AI zmy\u015bla<\/a>.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>RAG<\/strong> (Retrieval Augmented Generation) \u2014 model dostaje dane w promptcie w momencie zapytania. Zalety: dane zawsze aktualne (edytujesz \u017ar\u00f3d\u0142o, zmiana jest natychmiast), \u0142atwe dodawanie dokument\u00f3w, brak treningu. Koszt: kilka tysi\u0119cy z\u0142 infrastruktury wektorowej.<\/li>\n<li><strong>Fine-tuning<\/strong> \u2014 trenujesz model na Twoich dokumentach, zapami\u0119tuje je \u201ena sta\u0142e&#8221;. Zalety: kr\u00f3tsze prompty (nie musisz dokleja\u0107 kontekstu), lepsza wydajno\u015b\u0107 w w\u0105skiej dziedzinie. Koszt: 20-80 tysi\u0119cy z\u0142 jednorazowo za projekt, plus konieczno\u015b\u0107 re-treningu przy zmianie danych.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Regu\u0142a kciuka: fine-tuning ma sens, gdy dane s\u0105 stabilne (nie zmieniaj\u0105 si\u0119 co tydzie\u0144) i w\u0105skie tematycznie (dokumentacja techniczna, prawo z jednej dziedziny). Wszystko poza tym to RAG.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Bezpiecze\u0144stwo prywatnego LLM: 4 warstwy<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Prywatny LLM sam w sobie nie gwarantuje bezpiecze\u0144stwa, gwarantuje tylko, \u017ce dane nie wychodz\u0105 do dostawcy modelu. Ca\u0142a reszta zabezpiecze\u0144 musi by\u0107 zaprojektowana:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Sie\u0107.<\/strong> Model dost\u0119pny tylko z sieci firmowej (VPN, private subnet). Nigdy publiczne API bez autoryzacji.<\/li>\n<li><strong>Autoryzacja.<\/strong> Ka\u017cde zapytanie tokenowane, powi\u0105zane z u\u017cytkownikiem, poziomy dost\u0119pu do r\u00f3\u017cnych \u017ar\u00f3de\u0142 danych (nie ka\u017cdy widzi wszystkie dokumenty).<\/li>\n<li><strong>Logs and audit.<\/strong> Ka\u017cde zapytanie i odpowied\u017a zapisane z timestampem i u\u017cytkownikiem. Wym\u00f3g compliance w wi\u0119kszo\u015bci regulowanych bran\u017c.<\/li>\n<li><strong>Retention and encryption.<\/strong> Logi szyfrowane at-rest, polityka retencji (typowo 90-365 dni), automatyczne kasowanie danych osobowych z log\u00f3w po zdefiniowanym czasie.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">When a private LLM does NOT make sense<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Prywatny LLM to modne has\u0142o, ale nie zawsze dobra decyzja. Sygna\u0142y, \u017ce warto zosta\u0107 przy publicznym API:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Wolumen jest ma\u0142y<\/strong> (poni\u017cej 1000 zapyta\u0144 dziennie). Kalkulator TCO pokazuje, \u017ce wtedy publiczne API jest dziesi\u0105tki razy ta\u0144sze.<\/li>\n<li><strong>Nie masz zespo\u0142u DevOps\/MLOps.<\/strong> Prywatny LLM wymaga sta\u0142ej opieki: aktualizacje modelu, monitoring, incident response. Bez zespo\u0142u, kt\u00f3ry to utrzyma, projekt si\u0119 rozjedzie po 6 miesi\u0105cach.<\/li>\n<li><strong>Dane nie s\u0105 wra\u017cliwe.<\/strong> Je\u015bli publiczne API s\u0105 dozwolone przez compliance, a wolumen umiarkowany, prywatny LLM to overengineering.<\/li>\n<li><strong>Potrzebujesz absolutnie najwy\u017cszej jako\u015bci.<\/strong> W topowych benchmarkach GPT-4o, Claude 4.7 i Gemini 2.5 Pro nadal wyprzedzaj\u0105 open source o kilka procent. Dla cz\u0119\u015bci zastosowa\u0144 to ma znaczenie.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">W wielu firmach najsensowniejszy jest scenariusz hybrydowy, kt\u00f3ry opisali\u015bmy w artykule <a href=\"https:\/\/jsoncrew.com\/wdrozenia-ai-w-firmach-porazka-jak-zrobic-skutecznie\/\">Wdro\u017cenia AI: dlaczego ko\u0144cz\u0105 si\u0119 pora\u017ck\u0105<\/a>. Publiczny model do 95% zada\u0144, prywatny LLM tylko do tych, kt\u00f3re faktycznie tego wymagaj\u0105.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Trzy typowe architektury wdro\u017ce\u0144, kt\u00f3re robimy<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Z wdro\u017ce\u0144, kt\u00f3re prowadzili\u015bmy w ramach <a href=\"https:\/\/jsoncrew.com\/oferta\/wdrozenia-ai\/\">wdro\u017ce\u0144 AI<\/a> u klient\u00f3w w Polsce, wy\u0142aniaj\u0105 si\u0119 trzy powtarzalne architektury:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Single node in a Polish data center.<\/strong> Jeden serwer z 1-2x A100 w OVH lub Beyond.pl. Model Llama 3.3 70B lub Bielik. RAG na PostgreSQL + pgvector. Obs\u0142uguje 5-20 tysi\u0119cy zapyta\u0144 dziennie. Dobre dla firm 50-500 os\u00f3b z jednym-dwoma procesami AI.<\/li>\n<li><strong>Cluster on-premise.<\/strong> 4-8x A100\/H100 we w\u0142asnej serwerowni. Load balancer, dwie repliki modelu, wysoka dost\u0119pno\u015b\u0107. Obs\u0142uguje 100k+ zapyta\u0144 dziennie. Dla du\u017cych organizacji z krytycznymi procesami AI (finansowe, medyczne).<\/li>\n<li><strong>Hybryda API + prywatny LLM.<\/strong> Warstwa orkiestracji (n8n albo custom backend) rozdziela zapytania. Dane oznaczone jako \u201ewra\u017cliwe&#8221; id\u0105 do prywatnego LLM, reszta do GPT-4o albo Claude. Ta architektura jest coraz cz\u0119stsza w firmach, kt\u00f3re wcze\u015bniej wybiera\u0142y tylko API.<\/li>\n<\/ul>\n\n\n\n<div class=\"pll-cta\" style=\"background:linear-gradient(135deg,#0c1322 0%,#1a2340 100%);color:#fff;padding:2.5rem;border-radius:12px;margin:2.5rem 0;text-align:center;\">\n<h3 style=\"color:#ffffff;margin:0 0 1rem;font-size:1.6rem;line-height:1.3;\">Chcesz sprawdzi\u0107, czy prywatny LLM si\u0119 u Ciebie op\u0142aca?<\/h3>\n<p style=\"color:#d1d5db;margin:0 0 1.5rem;font-size:1.05rem;line-height:1.6;\">Robimy audyt wolumenu, wymog\u00f3w compliance i bud\u017cetu. Wynikiem jest jednostronicowa rekomendacja: prywatny LLM, publiczne API albo hybryda, z konkretnymi liczbami TCO na 3 lata.<\/p>\n<a class=\"pll-btn\" href=\"https:\/\/jsoncrew.com\/oferta\/wdrozenia-ai\/\" style=\"display:inline-block;background:#d4b06b;color:#0c1322;padding:1rem 2rem;border-radius:8px;font-weight:600;text-decoration:none;\">Um\u00f3w audyt wdro\u017cenia AI<\/a>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Najcz\u0119\u015bciej zadawane pytania<\/h2>\n\n\n\n<div class=\"pll-faq\">\n<style>\n.pll-faq{margin:1.5rem 0;}\n.pll-faq-item{background:#f9fafb;border:1px solid #e5e7eb;border-radius:8px;padding:1rem 1.25rem;margin-bottom:0.75rem;}\n.pll-faq-item summary{cursor:pointer;font-weight:600;font-size:1.05rem;color:#0c1322;list-style:none;padding-right:1.5rem;position:relative;}\n.pll-faq-item summary::-webkit-details-marker{display:none;}\n.pll-faq-item summary::after{content:'+';position:absolute;right:0;top:0;font-size:1.4rem;line-height:1;color:#6b7280;}\n.pll-faq-item[open] summary::after{content:'-';}\n.pll-faq-item[open] summary{margin-bottom:0.75rem;}\n.pll-faq-item p{margin:0;color:#374151;line-height:1.65;}\n.pll-faq-item a{color:#0c1322;text-decoration:underline;}\n<\/style>\n<details class=\"pll-faq-item\">\n<summary>Is a private LLM GDPR compliant?<\/summary>\n<p>Sam fakt, \u017ce model dzia\u0142a u Ciebie, jest jednym z mocniejszych argument\u00f3w za zgodno\u015bci\u0105 z RODO, bo dane fizycznie nie opuszczaj\u0105 Twojej infrastruktury. Ale RODO to szerszy temat: musisz mie\u0107 podstaw\u0119 prawn\u0105 przetwarzania, ocen\u0119 skutk\u00f3w dla ochrony danych (DPIA), polityki retencji log\u00f3w i mechanizm realizacji praw os\u00f3b, kt\u00f3rych dane dotycz\u0105. Prywatny LLM u\u0142atwia compliance, ale go nie zast\u0119puje.<\/p>\n<\/details>\n<details class=\"pll-faq-item\">\n<summary>Kt\u00f3ry open source LLM jest najlepszy do polskiego?<\/summary>\n<p>Najlepszy jako\u015bciowo w polskim w\u015br\u00f3d open source to Bielik od SpeakLeash i Cyfronet AGH, mimo ma\u0142ego rozmiaru (11B). Z modeli w klasie 70B najlepiej radzi sobie Llama 3.3, ale r\u00f3\u017cnica wzgl\u0119dem GPT-4o w polskim wci\u0105\u017c jest zauwa\u017calna (o 5-10% ni\u017cszy score w naszych testach). Mistral Large 2 jest tu\u017c za Llam\u0105, Qwen 2.5 dalej, DeepSeek na ko\u0144cu w polskim (dobry w kodzie i angielskim).<\/p>\n<\/details>\n<details class=\"pll-faq-item\">\n<summary>Ile GPU potrzebuj\u0119 do uruchomienia Llama 3.3 70B?<\/summary>\n<p>W kwantyzacji INT8 wystarczy jedna karta A100 80GB (albo H100). Model wa\u017cy wtedy oko\u0142o 70 GB, plus KV cache dla kilku r\u00f3wnoleg\u0142ych zapyta\u0144 i bufor systemowy. Dla wi\u0119kszego batcha albo pe\u0142nej precyzji FP16 potrzebne s\u0105 2x A100 80GB. Kalkulator powy\u017cej pokazuje to na Twoich parametrach.<\/p>\n<\/details>\n<details class=\"pll-faq-item\">\n<summary>Jak d\u0142ugo trwa wdro\u017cenie prywatnego LLM w firmie?<\/summary>\n<p>Prosta instalacja modelu na serwerze GPU to kwestia 2-3 dni. Ale produkcyjne wdro\u017cenie z warstw\u0105 RAG, integracjami z istniej\u0105cymi narz\u0119dziami, autoryzacj\u0105, logowaniem i monitoringiem to typowo 6-10 tygodni. Kluczowy koszt to nie sam model, tylko wszystko wok\u00f3\u0142. Nasz typowy projekt ko\u0144czy si\u0119 deployem po 8 tygodniach od kick-offu.<\/p>\n<\/details>\n<details class=\"pll-faq-item\">\n<summary>Czy warto stawia\u0107 prywatny LLM w polskiej chmurze, czy we w\u0142asnej serwerowni?<\/summary>\n<p>Dla wi\u0119kszo\u015bci firm sensowniejszy jest wynajem serwera GPU w polskim data center (OVH, Beyond.pl, Netia). Wady w\u0142asnej serwerowni: koszt kapita\u0142u na start (100-500 tys. z\u0142 za sprz\u0119t), ryzyko awarii, konieczno\u015b\u0107 ch\u0142odzenia i zasilania rezerwowego, cykl amortyzacji sprz\u0119tu (GPU starzej\u0105 si\u0119 co 2 lata). Zalety chmury PL: brak CapEx, elastyczno\u015b\u0107 zmiany konfiguracji, wsparcie dostawcy. W\u0142asna serwerownia ma sens dla organizacji, kt\u00f3re i tak maj\u0105 zbudowan\u0105 infrastruktur\u0119 serwerow\u0105 (banki, telco, urz\u0119dy).<\/p>\n<\/details>\n<details class=\"pll-faq-item\">\n<summary>Is a private LLM slower than ChatGPT?<\/summary>\n<p>Zale\u017cy od sprz\u0119tu. Na dobrze dobranym GPU (A100 lub H100) prywatny Llama 3.3 70B generuje odpowiedzi w tempie 30-50 token\u00f3w na sekund\u0119, co jest por\u00f3wnywalne z ChatGPT. Na s\u0142abszym sprz\u0119cie (RTX 4090 z INT4) to raczej 15-25 token\u00f3w\/s, wci\u0105\u017c akceptowalne dla wi\u0119kszo\u015bci zastosowa\u0144, ale wolniejsze ni\u017c publiczne API. Batch processing na dobrym sprz\u0119cie potrafi obs\u0142u\u017cy\u0107 500+ token\u00f3w\/s \u0142\u0105cznej przepustowo\u015bci.<\/p>\n<\/details>\n<details class=\"pll-faq-item\">\n<summary>Czy mog\u0119 zmieni\u0107 model w prywatnym LLM po wdro\u017ceniu?<\/summary>\n<p>Tak, i to jedna z g\u0142\u00f3wnych zalet w\u0142asnej infrastruktury. Warstwa aplikacyjna (RAG, API, integracje) jest niezale\u017cna od konkretnego modelu, wi\u0119c podmiana Llama 3.3 na Mistral Large 2 albo Qwen 2.5 to zwykle 1-2 dni pracy. Dla por\u00f3wnania: migracja z ChatGPT na Claude wymaga przepisania wi\u0119kszo\u015bci prompt\u00f3w i logiki wywo\u0142a\u0144.<\/p>\n<\/details>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Wi\u0119cej materia\u0142\u00f3w o AI w firmie<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/jsoncrew.com\/automatyzacja-ai-chatgpt-alternatywy\/\">AI Automation with ChatGPT? 6 alternatives for companies in 2026<\/a><\/li>\n<li><a href=\"https:\/\/jsoncrew.com\/wdrozenia-ai-w-firmach-porazka-jak-zrobic-skutecznie\/\">Wdro\u017cenia AI w firmach: dlaczego ko\u0144cz\u0105 si\u0119 pora\u017ck\u0105<\/a><\/li>\n<li><a href=\"https:\/\/jsoncrew.com\/co-to-jest-rag\/\">Co to jest RAG i dlaczego bez niego AI zmy\u015bla<\/a><\/li>\n<li><a href=\"https:\/\/jsoncrew.com\/ai-dla-prawnikow-pulapki-kancelaria\/\">AI dla prawnik\u00f3w: 5 pu\u0142apek<\/a><\/li>\n<li><a href=\"https:\/\/jsoncrew.com\/jak-wybrac-wdrozeniowca-ai-branza-prawnicza-kancelaria\/\">Jak wybra\u0107 wykonawc\u0119 wdro\u017cenia AI<\/a><\/li>\n<li><a href=\"https:\/\/jsoncrew.com\/automatyzacja-dzialu-sprzedazy-b2b\/\">Automatyzacja dzia\u0142u sprzeda\u017cy B2B: 7 proces\u00f3w<\/a><\/li>\n<\/ul>\n\n\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Article\",\n  \"headline\": \"Private LLM in your company: kiedy warto postawi\u0107 model AI on-premise (2026)\",\n  \"description\": \"Prywatny LLM daje kontrol\u0119 nad danymi i RODO, ale kosztuje. 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TCO vs API calculator, GPU requirements for Llama\/Mistral\/Qwen, 4 deployment options.<\/p>","protected":false},"author":4,"featured_media":2352,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_jc_seo_i18n":"","jc_lang":"","jc_translation_group":"","footnotes":""},"categories":[353],"tags":[],"brevo_track":[],"class_list":["post-2350","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-dla-firm"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.8 (Yoast SEO v28.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Private LLM in your company: when on-premise makes sense (2026) - JSON Crew<\/title>\n<meta name=\"description\" content=\"Prywatny LLM daje kontrol\u0119 nad danymi i RODO, ale kosztuje. 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