{"product_id":"k-ai-48-rome-4090-1322tops-2x-rtx-4090-entry-ai-server","title":"K-AI 48 Rome 4090 1322TOPS — 2x RTX 4090 Entry AI Server","description":"\u003cdiv style=\"font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;line-height:1.7;color:#1a1a1a\"\u003e\n\n\u003cdiv style=\"background:linear-gradient(135deg,#0d0d0d 0%,#1a1a2e 100%);color:#fff;padding:32px;border-radius:12px;margin-bottom:32px\"\u003e\n\u003cp style=\"font-size:18px;margin:0 0 20px 0;color:#ccc\"\u003eK-AI 48 Rome 4090 1322TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003e48 GB VRAM Entry 2-GPU Server\u003cbr\u003e2x RTX 4090 | EPYC Rome | 1 322 TOPS INT8\u003c\/p\u003e\n\u003cdiv style=\"display:flex;gap:16px;flex-wrap:wrap;margin-top:24px\"\u003e\n\u003cdiv style=\"background:rgba(250,180,0,0.15);border:1px solid #fab400;border-radius:8px;padding:14px 16px;text-align:center;flex:1;min-width:100px\"\u003e\n\u003cdiv style=\"font-size:26px;font-weight:800;color:#fab400\"\u003e1 322\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eTOPS INT8\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"background:rgba(250,180,0,0.15);border:1px solid #fab400;border-radius:8px;padding:14px 16px;text-align:center;flex:1;min-width:100px\"\u003e\n\u003cdiv style=\"font-size:26px;font-weight:800;color:#fab400\"\u003e48 GB\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eVRAM pool\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"background:rgba(250,180,0,0.15);border:1px solid #fab400;border-radius:8px;padding:14px 16px;text-align:center;flex:1;min-width:100px\"\u003e\n\u003cdiv style=\"font-size:26px;font-weight:800;color:#fab400\"\u003e2 GPU\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003etensor parallel\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"background:rgba(250,180,0,0.15);border:1px solid #fab400;border-radius:8px;padding:14px 16px;text-align:center;flex:1;min-width:100px\"\u003e\n\u003cdiv style=\"font-size:26px;font-weight:800;color:#fab400\"\u003erack\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eready\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top:20px;font-size:15px;color:#aaa\"\u003e48 GB VRAM pool across two RTX 4090 — the cost-floor for 32B-class tensor-parallel inference.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cp style=\"font-size:17px;color:#333;margin-bottom:24px\"\u003eA two-GPU Ada workstation-class AI server built on ROMED8-2T \/ EPYC Rome. Two RTX 4090 give a 48 GB pooled VRAM envelope that comfortably runs 32B dense Q6-Q8, Hunyuan-A13B at Q6, Wan 2.1 14B video, and Pixtral 12B vision — the best all-round model selection per Euro the Kentino lineup offers, before stepping up to Blackwell.\u003c\/p\u003e\n\n\u003ch2 style=\"font-size:22px;font-weight:700;margin:40px 0 16px 0;padding-bottom:8px;border-bottom:3px solid #2563eb\"\u003eHardware\u003c\/h2\u003e\n\n\u003ctable style=\"width:100%;border-collapse:collapse;margin-bottom:24px;font-size:15px\"\u003e\n\u003cthead\u003e\u003ctr style=\"background:#0d0d0d;color:#fff\"\u003e\n\u003cth style=\"padding:12px 16px;text-align:left\"\u003eComponent\u003c\/th\u003e\n\u003cth style=\"padding:12px 16px;text-align:left\"\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"background:#f8f8f8\"\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eGPUs\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e2x NVIDIA GeForce RTX 4090 24 GB GDDR6X (450 W, PCIe 4.0 x16)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eVRAM pool\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e48 GB (no NVLink — tensor-parallel over PCIe)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background:#f8f8f8\"\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eCPU\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003eAMD EPYC 7542 Rome (32C\/64T, 225 W, 128x PCIe 4.0 lanes)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eMotherboard\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003eASRock Rack ROMED8-2T (SP3, 7x PCIe 4.0 x16, 8x DDR4 ECC, 2x 10 GbE, IPMI)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background:#f8f8f8\"\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eSystem RAM\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e128 GB DDR4-2666 ECC RDIMM (2x 64 GB)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eBoot \/ storage\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e1 TB NVMe M.2 (PCIe 4.0 x4)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background:#f8f8f8\"\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003ePower supply\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003eSingle 2 kW ATX PSU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eChassis\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e4U rack-mount, passive Gen4 x16 risers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background:#f8f8f8\"\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eCooling\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003eSP3 tower cooler, 3x 120 mm front intake + 1x 120 mm rear exhaust\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eNetwork\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003eOnboard dual 10 GbE (Intel X550) + IPMI\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003cdiv style=\"display:flex;gap:16px;flex-wrap:wrap;margin-bottom:32px\"\u003e\n\u003cdiv style=\"flex:1;min-width:250px;background:#f4f4f4;border-radius:8px;padding:20px\"\u003e\n\u003ch3 style=\"font-size:16px;font-weight:700;margin:0 0 12px 0\"\u003ePower envelope\u003c\/h3\u003e\n\u003cul style=\"margin:0;padding-left:18px;font-size:14px;color:#444\"\u003e\n\u003cli\u003eGPU draw: 2 x 450 W = 900 W\u003c\/li\u003e\n\u003cli\u003eSystem total at full load: ~1 225 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 2 000 W (single 2 kW ATX) — 38.75 % headroom\u003c\/li\u003e\n\u003cli\u003eComfortable single-PSU margin\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"flex:1;min-width:250px;background:#f4f4f4;border-radius:8px;padding:20px\"\u003e\n\u003ch3 style=\"font-size:16px;font-weight:700;margin:0 0 12px 0\"\u003eLane topology\u003c\/h3\u003e\n\u003cp style=\"margin:0;font-size:14px;color:#444\"\u003eROMED8-2T fans out 2x16 directly from CPU root complex — no PLX switch. Consumer 4090 has no NVLink; tensor-parallel communicates over PCIe. PCIe Gen4 x16 at both GPUs.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\u003ch2 style=\"font-size:22px;font-weight:700;margin:40px 0 16px 0;padding-bottom:8px;border-bottom:3px solid #2563eb\"\u003eWhat you can run\u003c\/h2\u003e\n\n\u003cdiv style=\"background:#fefaf0;border-left:4px solid #fab400;padding:16px 20px;margin-bottom:24px;border-radius:0 8px 8px 0\"\u003e\n\u003cp style=\"margin:0;font-size:15px;color:#333\"\u003eWith 48 GB of pooled VRAM across 2 cards, this server handles 32B-class dense LLMs at Q6-Q8, MoE flagships, image and video generation, speech AI, and multi-tenant serving.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0;color:#0d0d0d\"\u003eLLMs — text \/ reasoning \/ coding\u003c\/h3\u003e\n\n\u003cp style=\"font-size:14px;font-weight:700;color:#fab400;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px\"\u003eChinese frontier\u003c\/p\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-32B\u003c\/strong\u003e dense Q6-Q8 (~25-35 tok\/s single-stream on 2x 4090, published reference); \u003cstrong\u003eQwQ-32B\u003c\/strong\u003e Q6; \u003cstrong\u003eQwen3.5-27B\u003c\/strong\u003e Q6-Q8\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-30B-A3B\u003c\/strong\u003e \/ \u003cstrong\u003eQwen3-Coder-30B-A3B\u003c\/strong\u003e bf16 (~60 GB tight; use Q6)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHunyuan-A13B\u003c\/strong\u003e Q6 or fp8 (~48 GB) — 80B\/13B MoE, 256k ctx\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSeed-OSS-36B\u003c\/strong\u003e Q6 — 512k native ctx\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek-R2\u003c\/strong\u003e 32B sparse MoE bf16 (~64 GB tight — prefer Q6 ~45 GB) (~30-40 tok\/s single-stream at Q4, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eERNIE-4.5-47B-A3B\u003c\/strong\u003e Q4 (~28 GB with headroom) \/ Q6 (~42 GB)\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003cp style=\"font-size:14px;font-weight:700;color:#fab400;text-transform:uppercase;letter-spacing:1px;margin:20px 0 8px 0\"\u003eWestern frontier\u003c\/p\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama 3.3 70B\u003c\/strong\u003e Q4_K_M (~43 GB) tensor-parallel 2-way — the sweet spot of this class (~14-17 tok\/s single-stream on 2x 4090, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama 4 Scout\u003c\/strong\u003e 109B\/17B MoE Q3_K (~51 GB tight)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral Small 3 \/ Magistral Small \/ Devstral Small 2\u003c\/strong\u003e (24B) bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMixtral 8x7B\u003c\/strong\u003e Q6\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGemma 3 27B\u003c\/strong\u003e bf16; \u003cstrong\u003ePhi-4 14B\u003c\/strong\u003e bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNemotron-Super 49B\u003c\/strong\u003e Q4 (~28 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOthers:\u003c\/strong\u003e OLMo 2 32B; Reka Flash 3 21B bf16; Falcon H1R 7B\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0;color:#0d0d0d\"\u003eVision-Language\u003c\/h3\u003e\n\u003cp style=\"font-size:15px;color:#333\"\u003eQwen3-VL-32B \/ Qwen3-VL-30B-A3B MoE \/ Qwen3-Omni-30B-A3B; InternVL3-38B Q4-Q5; InternVL3.5-38B; DeepSeek-VL2; ERNIE-4.5-VL-28B-A3B-Thinking; Llama 3.2 11B Vision bf16; Pixtral 12B bf16; Gemma 3 27B multimodal; PaliGemma 2 28B Q4; MiniCPM-V 2.6 \/ MiniCPM-o 2.6.\u003c\/p\u003e\n\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0;color:#0d0d0d\"\u003eImage generation\u003c\/h3\u003e\n\u003cp style=\"font-size:15px;color:#333\"\u003eFLUX.1 [dev] \/ [schnell] fp16 (24 GB) or fp8 (~12 GB) with generous batch (~15-25 seconds per 1024x1024 image at fp8 per card, published reference); FLUX.1 Kontext [dev]; SD 3.5 Large (18 GB fp16); SDXL 1.0 + ControlNet + AnimateDiff; HunyuanImage-2.1 bf16 (~34 GB fits in pool); AuraFlow v0.3 \/ OmniGen v1 \/ Kolors 2.0.\u003c\/p\u003e\n\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0;color:#0d0d0d\"\u003eVideo generation\u003c\/h3\u003e\n\u003cp style=\"font-size:15px;color:#333\"\u003eWan 2.1 14B T2V\/I2V Q6\/fp8; Wan 2.2 TI2V-5B bf16 single-card; Wan 2.2 T2V-A14B \/ I2V-A14B Q4 (~32 GB); HunyuanVideo 13B Q4-Q5 (~30 GB); HunyuanVideo 1.5 (8.3B) bf16; Open-Sora 2.0 (11B) Q8; CogVideoX-5B \/ 1.5 bf16; Mochi-1 Q4-Q8; LTX-Video 2B; Pyramid Flow 2B.\u003c\/p\u003e\n\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0;color:#0d0d0d\"\u003eAudio \/ Speech \/ TTS\u003c\/h3\u003e\n\u003cp style=\"font-size:15px;color:#333\"\u003eFull 24 GB tier stack fits with room for concurrent use: Whisper v3 large + Parakeet-TDT + Canary 1B + Moshi + Step-Audio 2 mini + CosyVoice 3.0 + Kokoro 82M + Stable Audio Open all residable simultaneously. Whisper v3 turbo runs at ~50x realtime on a single card (published reference).\u003c\/p\u003e\n\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0;color:#0d0d0d\"\u003eMulti-model \/ multi-tenant\u003c\/h3\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e2-4 concurrent users on 32B Q6 class LLMs via vLLM tensor-parallel\u003c\/li\u003e\n\u003cli\u003eMixed workload: Qwen3-32B Q6 (~20 GB) + FLUX.1 fp8 (~12 GB) + Whisper-turbo (1.6 GB) + Moshi (8 GB) resident across 2 cards\u003c\/li\u003e\n\u003cli\u003eLoRA \/ QLoRA fine-tuning of 7-14B models comfortably, 24-32B tight\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2 style=\"font-size:22px;font-weight:700;margin:40px 0 16px 0;padding-bottom:8px;border-bottom:3px solid #2563eb\"\u003eTarget workloads\u003c\/h2\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003eTwo-operator AI workstation with mixed LLM + image + audio stacks\u003c\/li\u003e\n\u003cli\u003e32B-class serving endpoint for small-team developer environment (4-8 concurrent users on Qwen3-32B \/ Gemma 3 27B)\u003c\/li\u003e\n\u003cli\u003eImage generation pipeline (FLUX.1 + SD 3.5 + ControlNet) batch production\u003c\/li\u003e\n\u003cli\u003eVideo-gen development box (Wan 2.1 \/ Wan 2.2 TI2V \/ HunyuanVideo 1.5)\u003c\/li\u003e\n\u003cli\u003eLoRA \/ QLoRA fine-tuning research box for 7-34B Chinese + Western weights\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2 style=\"font-size:22px;font-weight:700;margin:40px 0 16px 0;padding-bottom:8px;border-bottom:3px solid #2563eb\"\u003ePublished performance references\u003c\/h2\u003e\n\n\u003cdiv style=\"background:#0d0d0d;color:#fff;border-radius:12px;padding:24px;margin-bottom:24px\"\u003e\n\u003cp style=\"margin:0 0 4px 0;font-size:13px;color:#888;text-transform:uppercase;letter-spacing:1px\"\u003ePublished reference | 2x RTX 4090 comparable hardware\u003c\/p\u003e\n\u003ctable style=\"width:100%;border-collapse:collapse;margin-top:16px;font-size:14px\"\u003e\n\u003cthead\u003e\u003ctr style=\"border-bottom:1px solid #333\"\u003e\n\u003cth style=\"padding:8px 12px;text-align:left;color:#888;font-weight:600\"\u003eBenchmark\u003c\/th\u003e\n\u003cth style=\"padding:8px 12px;text-align:left;color:#888;font-weight:600\"\u003eResult\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eLlama 3.3 70B Q4_K_M llama.cpp decode\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~14-17 tok\/s single-stream\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eQwen3-32B Q6 vLLM single-stream\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~35-45 tok\/s decode\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eFLUX.1 [dev] fp8\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~2.5-3.0 s per 1024x1024 at 20 steps\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003evLLM batch-32 aggregate (extrapolated from 4x4090)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~90 tok\/s aggregate\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp style=\"margin:12px 0 0 0;font-size:13px;color:#666\"\u003ePublished reference points from comparable 2x4090 hardware. Not measured on Kentino hardware.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003ch2 style=\"font-size:22px;font-weight:700;margin:40px 0 16px 0;padding-bottom:8px;border-bottom:3px solid #2563eb\"\u003eNot ideal for\u003c\/h2\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e70B dense at Q6+ (needs 96 GB pool — step up to 4x RTX 4090 or 4x RTX 5090)\u003c\/li\u003e\n\u003cli\u003eFrontier 100B+ MoE at bf16 (GLM-4.5, Kimi K2, Mistral Large 3)\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2 style=\"font-size:22px;font-weight:700;margin:40px 0 16px 0;padding-bottom:8px;border-bottom:3px solid #2563eb\"\u003eWarranty and lead time\u003c\/h2\u003e\n\u003cdiv style=\"display:flex;gap:16px;flex-wrap:wrap;margin-bottom:24px\"\u003e\n\u003cdiv style=\"flex:1;min-width:150px;background:#f4f4f4;border-radius:8px;padding:20px;text-align:center\"\u003e\n\u003cdiv style=\"font-size:24px;font-weight:800;color:#0d0d0d\"\u003e2 years\u003c\/div\u003e\n\u003cdiv style=\"font-size:13px;color:#666;text-transform:uppercase;letter-spacing:1px\"\u003eparts warranty\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"flex:1;min-width:150px;background:#f4f4f4;border-radius:8px;padding:20px;text-align:center\"\u003e\n\u003cdiv style=\"font-size:24px;font-weight:800;color:#0d0d0d\"\u003e1 year\u003c\/div\u003e\n\u003cdiv style=\"font-size:13px;color:#666;text-transform:uppercase;letter-spacing:1px\"\u003elabor warranty\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"flex:1;min-width:150px;background:#f4f4f4;border-radius:8px;padding:20px;text-align:center\"\u003e\n\u003cdiv style=\"font-size:24px;font-weight:800;color:#0d0d0d\"\u003e10-28 days\u003c\/div\u003e\n\u003cdiv style=\"font-size:13px;color:#666;text-transform:uppercase;letter-spacing:1px\"\u003elead time\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"font-size:14px;color:#666\"\u003eBuild includes assembly, BIOS configuration, driver install, burn-in testing, and functional verification. Lead time depends on component availability, confirmed at order.\u003c\/p\u003e\n\n\u003ch2 style=\"font-size:22px;font-weight:700;margin:40px 0 16px 0;padding-bottom:8px;border-bottom:3px solid #2563eb\"\u003eRecommended add-ons\u003c\/h2\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003eNVIDIA ConnectX-5 100 GbE MCX555A-ECAT\u003c\/li\u003e\n\u003cli\u003eUpgrade boot drive to 2 TB NVMe\u003c\/li\u003e\n\u003cli\u003eUpgrade RAM to 256 GB (4x 64 GB) — more KV cache headroom for long-ctx MoE\u003c\/li\u003e\n\u003cli\u003eRack PDU (C13\/C19 metered) and 2 kVA online UPS\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c\/div\u003e","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52927554584904,"sku":null,"price":11434.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0843\/5479\/3800\/files\/PXL_20260413_071103100.jpg?v=1776441356","url":"https:\/\/kentino.com\/ru\/products\/k-ai-48-rome-4090-1322tops-2x-rtx-4090-entry-ai-server","provider":"Kentino","version":"1.0","type":"link"}