{"product_id":"k-ai-64-rome-5090-3352tops-2x-rtx-5090-entry-blackwell-ai-server","title":"K-AI 64 Rome 5090 3352TOPS — 2x RTX 5090 Entry Blackwell 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 64 Rome 5090 3352TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003eEntry Blackwell 2-GPU Server\u003cbr\u003e2x RTX 5090 | EPYC Milan | 3 352 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\"\u003e3 352\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\"\u003e64 GB\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eVRAM GDDR7\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\"\u003efp8\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003enative tensor\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\"\u003eEntry Blackwell 2-GPU server — 64 GB pooled VRAM, 3 352 INT8 TOPS, native fp8. The Ada-to-Blackwell step-up from 2x4090.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cp style=\"font-size:17px;color:#333;margin-bottom:24px\"\u003eA two-GPU Blackwell AI server built on ROMED8-2T \/ EPYC Milan. Two RTX 5090 deliver a 64 GB pooled VRAM envelope with native fp8 tensor math — roughly double the raw TOPS of 2x RTX 4090 in the same chassis footprint, and the first 2-GPU tier that comfortably runs Llama 3.3 70B Q4, Qwen3.5-122B-A10B Q4, and HunyuanVideo at bf16 \/ fp8 with headroom.\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 5090 32 GB GDDR7 (575 W, PCIe 5.0 x16, Blackwell)\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\"\u003e64 GB\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 7643 Milan (48C\/96T, 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 (industrial fans)\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 575 W = 1 150 W\u003c\/li\u003e\n\u003cli\u003eSystem total at full load: ~1 475 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 2 000 W (single 2 kW ATX) — 26.25 % headroom\u003c\/li\u003e\n\u003cli\u003eWorkable single-PSU margin; dual-PSU upgrade available for extra headroom\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 Gen4 from CPU root complex. 5090 is Gen5 silicon running Gen4 x16 without bandwidth penalty for inference. No PCIe switch. No NVLink on GeForce 5090 — tensor-parallel 2-way P2P uses PCIe.\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 64 GB of pooled GDDR7 VRAM across 2 Blackwell cards, this server handles 70B Q4 tensor-parallel, MoE flagships, native fp8 image generation, video AI, and multi-model concurrent 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 Q8 \/ bf16 (near-fp16 quality) (~40-55 tok\/s single-stream on Blackwell fp8, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwQ-32B\u003c\/strong\u003e bf16; \u003cstrong\u003eQwen3-30B-A3B \/ Coder-30B-A3B\u003c\/strong\u003e bf16 (~60 GB fits)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3.5-122B-A10B\u003c\/strong\u003e Q4 (~70-75 GB with RAM spill) — MoE flagship at Q4 fits\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHunyuan-A13B\u003c\/strong\u003e fp8 (~80 GB tight) or Q6 (~36 GB comfortable)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSeed-OSS-36B\u003c\/strong\u003e bf16 (~72 GB tight — prefer fp8 ~36 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek-R2\u003c\/strong\u003e 32B sparse MoE bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGLM-4.5-Air\u003c\/strong\u003e 106B\/12B Q4_K_M (~60 GB) — MoE with headroom\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eERNIE-4.5-47B-A3B\u003c\/strong\u003e Q6-Q8\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) — the headline workload for this tier (~20-28 tok\/s single-stream on 2x 5090, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHermes 3 70B \/ Tulu 3 70B\u003c\/strong\u003e Q4 — open post-training Llama derivatives\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral Small 3 \/ Magistral \/ Devstral Small 2\u003c\/strong\u003e 24B bf16; \u003cstrong\u003eMixtral 8x7B\u003c\/strong\u003e bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGemma 3 27B\u003c\/strong\u003e multimodal bf16 + reasoning headroom\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePhi-4 14B\u003c\/strong\u003e bf16; \u003cstrong\u003eNemotron-Super 49B\u003c\/strong\u003e Q6-Q8\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003egpt-oss-20b\u003c\/strong\u003e MXFP4 (16 GB) + \u003cstrong\u003egpt-oss-120b\u003c\/strong\u003e MXFP4 (80 GB — fits tight with short ctx)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOLMo 2 32B\u003c\/strong\u003e \/ \u003cstrong\u003eOLMo 3.1-32B-Think\u003c\/strong\u003e bf16\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 \/ Qwen3-Omni-30B-A3B bf16; InternVL3.5-38B bf16; Llama 3.2 90B Vision Q4 (~52 GB); Pixtral 12B bf16; Pixtral Large 124B Q3 (~58 GB tight); Gemma 3 27B multimodal bf16; PaliGemma 2 28B bf16; Molmo 72B Q4 (~45 GB).\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\"\u003e5090 native fp8 is the speed story — FLUX.1 \/ SD 3.5 \/ HunyuanImage run materially faster than on Ada: FLUX.1 [dev] \/ [schnell] fp8 native (~12 GB) with 2x parallel across cards (~8-12 seconds per 1024x1024 image on Blackwell, published reference); FLUX.1 Kontext [dev]; SD 3.5 Large (18 GB fp16 or 11 GB fp8); SDXL 1.0; HunyuanImage-2.1 bf16 (~34 GB); HunyuanImage-3.0 NF4; 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 2.2 T2V-A14B \/ I2V-A14B bf16 (~54 GB total) — MoE two-expert at full precision; Wan 2.2 TI2V-5B bf16 per-card, 2 parallel tenants; HunyuanVideo 13B Q4-Q5 (~30 GB), fp8 tight; HunyuanVideo 1.5 (8.3B) bf16 per-card; Open-Sora 2.0 (11B) bf16; CogVideoX-5B \/ 1.5 bf16; Mochi-1 bf16 (~42 GB fits); LTX-Video 2B; NVIDIA Cosmos Predict 2.\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\"\u003eSame full Chinese + Western speech stack as the 4090 tier fits with more headroom: Whisper v3 + Parakeet + Canary + Moshi + Step-Audio 2 \/ R1 + CosyVoice 3.0 + Kokoro + Stable Audio Open + MusicGen + AudioGen + SeamlessM4T v2 + MMS. On fp8-native 5090, Whisper \/ Parakeet decode at materially higher real-time factor. Whisper v3 turbo runs at ~75x realtime on Blackwell (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\u003eResident stack: Llama 3.3 70B Q4 (~43 GB tensor-parallel 2-way) + FLUX.1 fp8 (~12 GB) + Whisper-turbo + Moshi\u003c\/li\u003e\n\u003cli\u003e2-4 concurrent tenants on 32B class at Q6-Q8 per card\u003c\/li\u003e\n\u003cli\u003eLoRA \/ QLoRA fine-tuning of 7-14B comfortable, 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\u003eSmall-team developer workstation with 70B Q4 serving headroom\u003c\/li\u003e\n\u003cli\u003eBlackwell step-up from a 2x RTX 4090 box — same chassis, ~2.5x TOPS, fp8 native\u003c\/li\u003e\n\u003cli\u003eImage \/ video generation workstation with FLUX native fp8 speedup\u003c\/li\u003e\n\u003cli\u003eMulti-model concurrent box: 70B Q4 + FLUX + Whisper + Moshi resident simultaneously\u003c\/li\u003e\n\u003cli\u003e4-8 concurrent user inference endpoint for 32B class LLMs\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 5090 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~20-28 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 Q8 vLLM single-stream\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~45-60 tok\/s decode at fp8\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 native Blackwell\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~1.5-1.9 s per 1024x1024 at 20 steps\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eHunyuanVideo 13B Q5 TP-2\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e5 s 720p in ~5-7 min\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, not measured on Kentino hardware. Kentino measured reference on 4x RTX 4090: 647 TFLOPS fp16, 179 tok\/s batch-32 aggregate.\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\u003e100B+ dense models at bf16 (DeepSeek-V3, Kimi K2, Mistral Large 3 — need 256+ GB pool)\u003c\/li\u003e\n\u003cli\u003eFrontier video generation at bf16 long-form full-resolution\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 — or 4 TB\u003c\/li\u003e\n\u003cli\u003eUpgrade RAM to 256 GB (4x 64 GB) — MoE KV cache headroom \/ multi-model concurrent serving\u003c\/li\u003e\n\u003cli\u003eRack PDU (C13\/C19 metered) and 3 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":52927642108232,"sku":null,"price":11653.0,"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\/zh\/products\/k-ai-64-rome-5090-3352tops-2x-rtx-5090-entry-blackwell-ai-server","provider":"Kentino","version":"1.0","type":"link"}