{"product_id":"k-ai-96-rome-4090-2644tops-4-rtx-4090-ai-inference-server","title":"K-AI 96 Rome 4090 2644TOPS — 4× RTX 4090 AI Inference Server","description":"\u003cdiv style=\"font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;line-height:1.7;color:#1a1a1a\"\u003e\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 96 Rome 4090 2644TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003e96 GB VRAM Inference Server\u003cbr\u003e4x RTX 4090 | EPYC Rome | 2 644 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\"\u003e647\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eTFLOPS fp16\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\"\u003e179\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003etok\/s batch-32\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\"\u003e96 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\"\u003e24\/7\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003erack-ready\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top:20px;font-size:15px;color:#aaa\"\u003eMeasured on Kentino hardware. Llama 3.3 70B AWQ INT4 via vLLM 0.19.0.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cp style=\"font-size:17px;color:#333;margin-bottom:24px\"\u003eA 4U rack-mount inference server with four GeForce RTX 4090 pooled to 96 GB VRAM, one AMD EPYC 7542 Rome CPU (32C\/64T), 256 GB DDR4 ECC, 2 TB NVMe boot, and dual synchronized 2 kW ATX PSU. Runs vLLM, SGLang, llama.cpp, ComfyUI and every major open-weight inference stack out of the box.\u003c\/p\u003e\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\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\"\u003e4x 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\"\u003e96 GB total across 4 cards\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\"\u003e256 GB DDR4-2666 ECC RDIMM (4x 64 GB)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eStorage\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e2 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\"\u003ePSU\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003eDual 2 kW ATX with sync cable\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, front-to-back directed airflow\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 front + 1x rear 120 mm 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)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\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: 4 x 450 W = 1 800 W\u003c\/li\u003e\n\u003cli\u003eSystem total: ~2 125 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 4 000 W (dual 2 kW) — 46.9% headroom\u003c\/li\u003e\n\u003cli\u003eSplit power delivery — single PSU failure = loss of 2 GPUs or 2 GPUs + motherboard\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\"\u003e128 PCIe Gen4 lanes from EPYC to seven x16 slots; four populated by GPUs at Gen4 x16. No PCIe switch. No NVLink — peer-to-peer at 19-22 GB\/s (Kentino measured).\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\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\u003cdiv style=\"background:#fefaf0;border-left:4px solid #fab400;padding:16px 20px;margin-bottom:24px;border-radius:0 8px 8px 0\"\u003e\u003cp style=\"margin:0;font-size:15px;color:#333\"\u003eWith 96 GB of pooled VRAM across 4 cards, this server handles open-weight LLMs, vision models, image and video generation, speech AI, and multi-tenant serving.\u003c\/p\u003e\u003c\/div\u003e\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0\"\u003eLLMs — text \/ reasoning \/ coding\u003c\/h3\u003e\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 \/ Qwen3.5:\u003c\/strong\u003e Qwen3-72B Q4 (~15-20 tok\/s); Qwen3-32B Q6; Qwen3-30B-A3B MoE Q4-Q6; Qwen3-Coder-30B-A3B at 256k; Qwen3.5-122B-A10B Q4; QwQ-32B\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek:\u003c\/strong\u003e DeepSeek-R2 32B Q4-Q6 (92.7% AIME 2025); DeepSeek-R1-Distill-Qwen-32B bf16; DeepSeek-V2-Lite 16B\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGLM \/ Z.ai:\u003c\/strong\u003e GLM-4.5-Air 106B\/12B Q4-Q5; GLM-4.6V-Flash; GLM-Zero 9B\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHunyuan:\u003c\/strong\u003e Hunyuan-A13B Q4-Q6 (~48 GB) 256k ctx dual-mode reasoning\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOthers:\u003c\/strong\u003e Seed-OSS-36B Q4 512k ctx; ERNIE-4.5-47B-A3B Q4; Yi-34B Q6; Baichuan-M2-32B; Step-3.5-Flash\u003c\/li\u003e\n\u003c\/ul\u003e\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\u003eMeta Llama:\u003c\/strong\u003e Llama 3.3 70B Q4_K_M (~20 tok\/s llama.cpp, ~179 tok\/s batch-32 vLLM — Kentino measured); Llama 3.1 8B bf16 (~80-120 tok\/s); Llama 4 Scout Q4\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral:\u003c\/strong\u003e Small 3 24B bf16; Magistral Small 24B reasoning; Devstral Small 2 24B 256k ctx; Mixtral 8x7B Q6\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOpenAI:\u003c\/strong\u003e gpt-oss-20b MXFP4 (16 GB); gpt-oss-120b MXFP4 (80 GB tight)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOthers:\u003c\/strong\u003e Gemma 3 27B Q6 128k; Phi-4 14B bf16; Nemotron-Super 49B Q4; Granite 4.0 H-Small; OLMo 2 32B; Reka Flash 3; Command R 35B\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0\"\u003eVision-Language Models\u003c\/h3\u003e\n\u003cp style=\"font-size:15px;color:#333\"\u003eQwen3-VL-8B\/32B, Qwen3-VL-30B-A3B, Qwen3-Omni-30B-A3B; InternVL3 up to 78B Q4; InternVL3.5-38B; DeepSeek-VL2; Llama 3.2 11B Vision; Pixtral 12B; Molmo 7B; Gemma 3 12B\/27B; PaliGemma 2; MiniCPM-V 2.6 \/ MiniCPM-o 2.6.\u003c\/p\u003e\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0\"\u003eImage generation\u003c\/h3\u003e\n\u003cp style=\"font-size:15px;color:#333\"\u003eFLUX.1 [dev]\/[schnell] fp8 (~15-25 s per 1024x1024); FLUX.1 Kontext; FLUX Tools; SD 3.5 Large; SDXL; HunyuanImage-2.1 bf16 (~34 GB) 2K native; Kolors 2.0; AuraFlow; OmniGen v1.\u003c\/p\u003e\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0\"\u003eVideo generation\u003c\/h3\u003e\n\u003cp style=\"font-size:15px;color:#333\"\u003eWan 2.2 T2V-A14B\/I2V-A14B MoE (~54 GB bf16); Wan 2.2 TI2V-5B 720p@24fps; HunyuanVideo 13B Q4-Q5; HunyuanVideo 1.5; CogVideoX-5B; Open-Sora 2.0; Mochi-1; LTX-Video; SVD\/SV3D\/SV4D; NVIDIA Cosmos Predict 2.\u003c\/p\u003e\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0\"\u003eAudio \/ Speech \/ TTS\u003c\/h3\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eASR:\u003c\/strong\u003e Whisper v3 turbo (~50x realtime); Parakeet-TDT 1.1B; Canary 1B; Qwen3-ASR; SenseVoice\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTTS:\u003c\/strong\u003e CosyVoice 3.0; Kokoro 82M; Stable Audio Open; Step-Audio-EditX\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRealtime:\u003c\/strong\u003e Kyutai Moshi (200 ms full-duplex); Step-Audio 2 mini; Qwen2.5-Omni-7B\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMusic:\u003c\/strong\u003e MusicGen; AudioGen; Suno Bark; SeamlessM4T v2\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0\"\u003eMulti-model serving\u003c\/h3\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e4-8 concurrent users on 32-72B LLMs via vLLM \/ SGLang tensor-parallel\u003c\/li\u003e\n\u003cli\u003eMixed: Qwen3-32B + FLUX.1 + Whisper-turbo + Moshi with partitioned VRAM\u003c\/li\u003e\n\u003cli\u003eLoRA\/QLoRA fine-tuning 32-72B; full-param 7-14B\u003c\/li\u003e\n\u003cli\u003eRAG with Command R+ or Qwen3 + BGE-M3\/E5\/Jina\u003c\/li\u003e\n\u003c\/ul\u003e\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\u003eInference gateway for 50-200 seat org (70B Q4-Q6, 4-8 concurrent sessions)\u003c\/li\u003e\n\u003cli\u003eBatch diffusion\/video pipeline (SDXL + FLUX.1 + Wan 2.2 overnight)\u003c\/li\u003e\n\u003cli\u003eLoRA\/QLoRA fine-tuning lab for 7-34B domain adaptations\u003c\/li\u003e\n\u003cli\u003eRAG document assistant (Qwen3-VL + BGE-M3 + Command R, 32k ctx)\u003c\/li\u003e\n\u003cli\u003eMixed single-box: chat + image + ASR + realtime voice on partitioned VRAM\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2 style=\"font-size:22px;font-weight:700;margin:40px 0 16px 0;padding-bottom:8px;border-bottom:3px solid #2563eb\"\u003eMeasured performance\u003c\/h2\u003e\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\"\u003eKentino bench | 2026-04-10 | 4x RTX 4090 + EPYC 7542 + ROMED8-2T\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\"\u003eBenchmark\u003c\/th\u003e\n\u003cth style=\"padding:8px 12px;text-align:left;color:#888\"\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\"\u003eSustained compute (fp16)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e647.7 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003evLLM Llama 3.3 70B AWQ INT4 (single)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e8.0 tok\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003evLLM Llama 3.3 70B AWQ INT4 (batch-32)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e179.3 tok\/s aggregate\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003ellama.cpp Llama 3.3 70B Q4_K_M (single)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e20.3 tok\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003ePrompt eval\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e1 568 tok\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eGPU memory bandwidth\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e920 GB\/s per card\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eNVMe read\/write\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e4 589 \/ 4 213 MB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003ePeak thermal (GPU+CPU burn)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e73 C, 0.6% drop\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\"\u003evLLM used awq kernel — 2-3x possible with awq_marlin.\u003c\/p\u003e\n\u003c\/div\u003e\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\u003eFrontier 100B+ dense at bf16 (DeepSeek V3\/R1, GLM-4.5+, Kimi-K2, Mistral Large 3 — require 256+ GB VRAM)\u003c\/li\u003e\n\u003cli\u003eTraining from scratch (consumer RTX 4090 lack NVLink)\u003c\/li\u003e\n\u003c\/ul\u003e\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\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\u003eUpgrade RAM to 512 GB (add 4x 64 GB DDR4 — four DIMM slots open)\u003c\/li\u003e\n\u003cli\u003e4 TB NVMe secondary drive for dataset\/model staging\u003c\/li\u003e\n\u003cli\u003e24U open cabinet for multi-server deployments\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52926141628744,"sku":null,"price":18491.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0843\/5479\/3800\/files\/PXL_20260413_071005153.jpg?v=1776441384","url":"https:\/\/kentino.com\/ar\/products\/k-ai-96-rome-4090-2644tops-4-rtx-4090-ai-inference-server","provider":"Kentino","version":"1.0","type":"link"}