{"product_id":"k-ai-48-rome-l4-484tops-2x-nvidia-l4-passive-edge-ai-server","title":"K-AI 48 Rome L4 484TOPS — 2x NVIDIA L4 Passive Edge 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 L4 484TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003eSilent 2x L4 Passive Edge Server\u003cbr\u003e48 GB ECC VRAM | EPYC Milan | 484 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\"\u003e484\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\"\u003eECC VRAM\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\"\u003e144 W\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eGPU total\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\"\u003edatacenter\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top:20px;font-size:15px;color:#aaa\"\u003eSilent 2x L4 passive inference box — datacenter-grade warranty path, 72 W per card, 48 GB ECC VRAM for always-on edge deployment.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cp style=\"font-size:17px;color:#333;margin-bottom:24px\"\u003eA 2-GPU edge inference server built around passive NVIDIA L4 cards — the datacenter-class silent option in the Kentino lineup. 48 GB total ECC VRAM, 144 W total GPU draw, single-slot card footprint, and airflow driven entirely by the chassis. For branch offices, broadcast facilities, always-on transcription, and any deployment where acoustic profile and a datacenter warranty path matter more than raw tensor throughput.\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 L4 24 GB GDDR6 passive (72 W, PCIe 4.0 x16, Ada Lovelace, ECC)\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 ECC\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 (low-RPM PWM)\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 72 W = 144 W\u003c\/li\u003e\n\u003cli\u003eSystem total at full load: ~469 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 2 000 W — 76.55 % headroom\u003c\/li\u003e\n\u003cli\u003eDrives fans at idle-low RPM (~35 dBA idle, \u0026lt;45 dBA sustained inference)\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 both GPUs. L4 is native Gen4 x16; ROMED8-2T fans out 2x16 directly from CPU. No switch, no NVLink. 55-65 C GPU temperature sustained — passive cards rely entirely on chassis airflow.\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 ECC VRAM across 2 passive L4 cards, this server handles always-on LLM inference, 24\/7 ASR + TTS pipelines, VLM document processing, and edge deployments where silence and datacenter warranty matter.\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 with 32k ctx (~15-20 tok\/s single-stream on L4, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-30B-A3B\u003c\/strong\u003e \/ \u003cstrong\u003eQwen3-Coder-30B-A3B\u003c\/strong\u003e Q4-Q6 (MoE, 256k ctx)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwQ-32B\u003c\/strong\u003e Q6; \u003cstrong\u003eDeepSeek-R2\u003c\/strong\u003e 32B sparse MoE Q4-Q6 (~18-24 tok\/s single-stream at Q4 on L4, published reference)\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 Q4-Q6 — 512k native ctx\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eERNIE-4.5-47B-A3B\u003c\/strong\u003e Q4-Q6 (~28-42 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 (~8-12 tok\/s single-stream on 2x L4, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral Small 3 \/ Magistral \/ Devstral Small 2\u003c\/strong\u003e (24B) bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGemma 3 27B\u003c\/strong\u003e multimodal bf16\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\u003eNemotron-Super 49B\u003c\/strong\u003e Q4 (~28 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOLMo 2 32B\u003c\/strong\u003e \/ \u003cstrong\u003eOLMo 3.1-32B-Think\u003c\/strong\u003e — fully open reasoning research\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 Q4-Q6; InternVL3.5-38B Q4; Pixtral 12B bf16 (24 GB); Llama 3.2 11B Vision bf16; Gemma 3 12B \/ 27B multimodal; MiniCPM-V 2.6 \/ MiniCPM-o 2.6; Aya Vision 8B \/ 32B for 23-language VLM.\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\"\u003eL4 is inference-tuned — usable for steady-state image pipelines, not batch generation: FLUX.1 [dev] fp8 \/ Q4 — single image in 8-12 s; SD 3.5 Large fp8 \/ SDXL 1.0 \/ SD 3.5 Medium; HunyuanImage-2.1 NF4 (~14 GB); Kolors 2.0 fp8.\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\"\u003eNot recommended for new video projects on L4 — prefer a 4090\/5090 build. For light T2V pipelines: Wan 2.2 TI2V-5B at bf16 — 5 s 720p in ~6-10 minutes; HunyuanVideo 1.5 (8.3B) Wan2GP optimization path.\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;font-weight:700;margin-bottom:8px\"\u003eThe L4's real strength — 24\/7 ASR + TTS + realtime voice stacks.\u003c\/p\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 (~30x realtime on L4, 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, 200 ms latency full-duplex); Step-Audio 2 mini \/ R1\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTranslation:\u003c\/strong\u003e Meta SeamlessM4T v2 (~100 languages)\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\u003eWhisper v3 + Kokoro + Moshi + Qwen3-14B Q6 all resident on card 1 (~18-20 GB); card 2 reserved for a second tenant or a VLM\u003c\/li\u003e\n\u003cli\u003e8-16 concurrent ASR sessions on a single L4 at Whisper-turbo real-time\u003c\/li\u003e\n\u003cli\u003eRAG endpoint: Qwen3-14B \/ Llama 3.1 8B (~48-72 tok\/s single-stream on L4, published reference) + BGE-M3 embeddings + reranker\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\u003eBranch office or broadcast facility silent inference box\u003c\/li\u003e\n\u003cli\u003eAlways-on ASR + translation pipeline (call centers, lecture transcription, media captioning)\u003c\/li\u003e\n\u003cli\u003eEdge RAG endpoint over corporate documents with datacenter warranty path\u003c\/li\u003e\n\u003cli\u003e24\/7 multimodal assistant (Qwen3-VL-8B + MiniCPM-o 2.6) for a small office\u003c\/li\u003e\n\u003cli\u003eDevelopment staging box for datacenter-class deployments — same L4 silicon as hyperscale edge\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 NVIDIA L4 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 3.1 8B Q4_K_M llama.cpp decode\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~30-40 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\"\u003eQwen3-14B Q6 vLLM decode\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~20-28 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\"\u003eWhisper v3 large realtime factor\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~15-20x per L4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eParakeet-TDT 1.1B English ASR\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~40-60x real-time\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eMoshi 7B full-duplex voice\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e200 ms latency, fits on single L4\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.\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+ (even 48 GB pool is tight — use 4x4090 or 2x5090)\u003c\/li\u003e\n\u003cli\u003eImage \/ video generation batch work at scale (L4 tensor throughput is inference-tuned)\u003c\/li\u003e\n\u003cli\u003eLoRA \/ fine-tuning workflows — use 4090\/5090 builds instead\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\"\u003eL4 carries NVIDIA datacenter warranty path — meaningful advantage over consumer cards for 24\/7 SLA deployment. Build includes assembly, BIOS configuration, driver install, burn-in testing, and functional verification.\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\u003eUpgrade to K-AI 96 Rome L4 968TOPS (4x L4, 96 GB pool) for doubled throughput\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) for multi-model concurrent serving\u003c\/li\u003e\n\u003cli\u003eRack PDU + 2 kVA online UPS for branch deployment\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c\/div\u003e","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52927599608136,"sku":null,"price":11374.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0843\/5479\/3800\/files\/kentino-ai-server-4u-rack-front-fans.jpg?v=1776848931","url":"https:\/\/kentino.com\/uk\/products\/k-ai-48-rome-l4-484tops-2x-nvidia-l4-passive-edge-ai-server","provider":"Kentino","version":"1.0","type":"link"}