{"product_id":"k-ai-32-rome-5090-1676tops-1x-rtx-5090-ai-workstation","title":"K-AI 32 Rome 5090 1676TOPS — 1x RTX 5090 AI Workstation","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 32 Rome 5090 1676TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003eSingle-GPU Blackwell Workstation\u003cbr\u003e1x RTX 5090 | EPYC Milan | 1 676 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 676\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\"\u003e32 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\"\u003eSingle Blackwell GPU, 32 GB GDDR7, fp8 native — the sharpest single-card AI workstation Kentino builds.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cp style=\"font-size:17px;color:#333;margin-bottom:24px\"\u003eA single-GPU, workstation-class AI server on the ROMED8-2T \/ EPYC Milan platform. One RTX 5090 delivers 32 GB of GDDR7 VRAM with native fp8 tensor math — the sweet spot for a developer box, a small-team inference endpoint, or an image\/video generation workstation where one strong GPU beats two weaker ones. 4U rack form factor, but drop-in for a quiet office under-desk deployment.\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\"\u003eGPU\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e1x 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\"\u003e32 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 riser\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 (Arctic Freezer 4U-M class), 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: 1 x 575 W = 575 W\u003c\/li\u003e\n\u003cli\u003eSystem total at full load: ~900 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 2 000 W (single 2 kW ATX) — 55 % headroom\u003c\/li\u003e\n\u003cli\u003eGenerous transient margin, silent operation at light load\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\"\u003ePCIe Gen4 x16 at the GPU (ROMED8-2T is Gen4; 5090 is Gen5 silicon running Gen4 without bandwidth penalty for inference). 16 lanes direct from CPU root complex. No PCIe switch. No NVLink on GeForce 5090.\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 32 GB of GDDR7 VRAM and native fp8 tensor math, this workstation handles open-weight LLMs up to 32B dense, image generation with FLUX.1, video generation, speech AI, and single-developer multi-model stacks.\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_K — 32k context, flagship general reasoning (~40-55 tok\/s single-stream on Blackwell fp8, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-30B-A3B\u003c\/strong\u003e MoE at Q4_K_M with long KV headroom (Qwen3-Coder-30B-A3B agentic, 256k ctx)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwQ-32B\u003c\/strong\u003e Q6 — reasoning preview\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek-R2\u003c\/strong\u003e 32B sparse MoE at Q4-Q6 — single-GPU reasoning that scores 92.7 % AIME-2025 (~45-60 tok\/s single-stream on Blackwell fp8, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3.5-27B\u003c\/strong\u003e dense Q6 (Feb 2026 release)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHunyuan-A13B\u003c\/strong\u003e at Q4_K_M (~28-30 GB) — 80B\/13B MoE, 256k ctx, dual-mode reasoning\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSeed-OSS-36B\u003c\/strong\u003e Q4_K_M — 512k native context for long-doc analysis\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 at Q2_K (~27 GB tight) or Q3_K (~34 GB with RAM spill) — usable for general chat\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral Small 3 \/ Magistral Small \/ Devstral Small 2\u003c\/strong\u003e (24B dense) at Q6-Q8 or bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGemma 3 27B\u003c\/strong\u003e multimodal at Q6 with 128k context\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePhi-4 14B\u003c\/strong\u003e \/ \u003cstrong\u003ePhi-4-reasoning\u003c\/strong\u003e bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReka Flash 3 (21B Apache 2.0)\u003c\/strong\u003e at bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003egpt-oss-20b\u003c\/strong\u003e native MXFP4 (~16 GB — fits with generous KV)\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-8B \/ -32B at Q4-Q6; Qwen3-VL-30B-A3B MoE; InternVL3.5-8B \/ -38B Q4; MiniCPM-V 2.6 \/ MiniCPM-o 2.6 (8B); Llama 3.2 11B Vision bf16; Pixtral 12B bf16 (24 GB — tight, use Q8); Gemma 3 12B \/ 27B multimodal; PaliGemma 2 (3\/10B); Phi-4-multimodal 5.6B; Aya Vision 8B.\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] fp8 (~12 GB) native Blackwell speedup (~8-12 seconds per 1024x1024 image at 20 steps on Blackwell, published reference); FLUX.1 Kontext [dev] — in-context editing, character consistency; SD 3.5 Large (18 GB fp16 \/ 11 GB fp8); SDXL 1.0 10-12 GB fp16; HunyuanImage-2.1 NF4 (~14 GB); Kolors 2.0 fp8; AuraFlow v0.3 \/ OmniGen v1 \/ PixArt-Sigma.\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 TI2V-5B at ~16 GB — 720p@24fps on a single 5090; Wan 2.1 T2V\/I2V 14B at Q4-Q6 (~16 GB); HunyuanVideo 1.5 (8.3B) — 14 GB minimum; CogVideoX-5B \/ 5B-I2V int8 (~12 GB); LTX-Video 2B realtime-class 30 fps; Mochi-1 Q4 (~17-18 GB).\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\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eASR:\u003c\/strong\u003e Whisper v3 large \/ turbo (~50x realtime on single GPU, published reference); NVIDIA Parakeet-TDT 1.1B; Canary 1B\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTTS:\u003c\/strong\u003e CosyVoice 2.0 \/ Fun-CosyVoice 3.0; Kokoro 82M; Stable Audio Open\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRealtime \/ S2S:\u003c\/strong\u003e Kyutai Moshi (7B) — only open realtime full-duplex voice; Step-Audio 2 mini \/ R1\u003c\/li\u003e\n\u003c\/ul\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 for a single developer: Qwen3-32B Q6 (~20 GB) + FLUX.1 fp8 (~12 GB fits tight) on swap, or Qwen3-14B Q6 (~9 GB) + FLUX.1 + Whisper-turbo + Kokoro simultaneously (~20-24 GB pinned)\u003c\/li\u003e\n\u003cli\u003e2-4 concurrent users on 14-32B class LLMs via vLLM \/ SGLang\u003c\/li\u003e\n\u003cli\u003eLoRA \/ QLoRA fine-tuning of 7-14B dense models\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\u003eDeveloper workstation for a single AI engineer running mixed inference + image gen\u003c\/li\u003e\n\u003cli\u003eSmall-team coding-agent endpoint (Qwen3-Coder-30B-A3B) with 1-4 concurrent users\u003c\/li\u003e\n\u003cli\u003eContent pipeline: FLUX.1 or SD 3.5 Large batch image gen + Wan 2.2 short-form video\u003c\/li\u003e\n\u003cli\u003eOn-premises ASR + TTS voice stack (Whisper + Kokoro + Moshi) for a branch office\u003c\/li\u003e\n\u003cli\u003eProsumer LLM + VLM research box — test Qwen3, Llama 3.3, Gemma 3, Phi-4 on real hardware\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 | single 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:#fff\"\u003e~18-22 tok\/s with CPU KV offload\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~45-55 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 on Blackwell\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~1.7-2.0 s per 1024x1024 image at 20 steps\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eWan 2.2 TI2V-5B 720p clip\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~3-4 minutes at fp16\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 single-5090 hardware. Kentino measured numbers will be posted once gf-logic extends bench to single-5090.\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 models at Q6+ (32 GB is insufficient — use 2x 5090 for proper 64 GB pool)\u003c\/li\u003e\n\u003cli\u003eMulti-user concurrent serving at scale (single tensor-parallel partition)\u003c\/li\u003e\n\u003cli\u003eFrontier 100B+ MoE (GLM-4.5, Kimi K2, Mistral Large 3 — out of reach on a single consumer card)\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 DDR4) for bigger KV cache \/ multi-model concurrent stacks\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":52927463620936,"sku":null,"price":8092.0,"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\/fr\/products\/k-ai-32-rome-5090-1676tops-1x-rtx-5090-ai-workstation","provider":"Kentino","version":"1.0","type":"link"}