{"product_id":"k-ai-144-rome-l4-1452tops-6-nvidia-l4-epyc-milan","title":"K-AI 144 Rome L4 1452TOPS — 6× NVIDIA L4 — EPYC Milan","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 144 Rome L4 1452TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003e144 GB VRAM Silent Edge Inference Server\u003cbr\u003e6x NVIDIA L4 Passive | EPYC Milan | 1 452 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 452\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eINT8 TOPS\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 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\"\u003e432 W\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eGPU envelope\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\"\u003esilent\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003epassive GPUs\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top:20px;font-size:15px;color:#aaa\"\u003eSix passive L4 datacenter cards. Quietest AI server in Kentino's lineup — acceptable for office-edge deployment.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cp style=\"font-size:17px;color:#333;margin-bottom:24px\"\u003eA 4U single-socket inference server with six passive NVIDIA L4 cards (24 GB each, 144 GB pool), one AMD EPYC 7643 Milan CPU (48C\/96T), 384 GB DDR4 ECC, 2 TB NVMe boot, and a single 2 kW ATX PSU with 62 % headroom. Dense-edge inference workhorse for embedding fleets, multi-tenant small\/mid-size LLM serving, and watts-per-query deployments near office space.\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\"\u003e6x NVIDIA L4 24 GB (Ada Lovelace, passive, 72 W, single-slot LP, PCIe Gen4 x8)\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\"\u003e144 GB aggregate across 6 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 7643 Milan (48C\/96T, 225 W, 128 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\"\u003e384 GB DDR4-2666 ECC RDIMM (6x 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\"\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\"\u003ePower supply\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e1x 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 (6-card layout)\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 + front-to-back directed airflow (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\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: 6 x 72 W = 432 W\u003c\/li\u003e\n\u003cli\u003eSystem total at full load: ~757 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 2 000 W — 62 % headroom\u003c\/li\u003e\n\u003cli\u003eSilent operation, massive thermal 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\"\u003eL4 is PCIe Gen4 x8 native — no bandwidth loss vs host. ROMED8-2T provides 7x x16 slots; one slot left free for NIC upsell. No PCIe switch required. No NVLink.\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\"\u003eAt 144 GB aggregate across 6 physical cards, the sweet spot is concurrent multi-model serving: run a 70B dense at Q4, a 30B MoE, a 14B coder, a VLM and an embedding model concurrently and still have KV headroom.\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 \/ Qwen3.5 (Alibaba):\u003c\/strong\u003e Qwen3-30B-A3B Q4-Q6; QwQ-32B Q6; Qwen3-32B dense Q6; Qwen3.5-122B-A10B Q4-Q5 (~75 GB comfortable); Qwen3-235B-A22B Q3 (~112 GB) tight, short ctx\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek:\u003c\/strong\u003e DeepSeek-R2 32B sparse MoE Q4-Q6 (single-card capable, 6x concurrent streams, ~15-20 tok\/s per stream); Seed-OSS-36B Q4-Q6 with 512k native context\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGLM \/ Z.ai:\u003c\/strong\u003e GLM-4.5-Air Q4-Q5 (60-70 GB comfortable); Hunyuan-A13B Q4-Q6 (~48 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBaidu ERNIE-4.5-47B-A3B\u003c\/strong\u003e Q4; Step-3.5-Flash Q3-Q4 with some RAM spill\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\u003eMeta Llama:\u003c\/strong\u003e Llama 3.3 70B Q4-Q6 (43-58 GB) with generous KV (~10-17 tok\/s single-stream across 6x L4 tensor-parallel); Llama 4 Scout 109B\/17B MoE Q4 (~63 GB) comfortable\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral:\u003c\/strong\u003e Mistral Small 3 \/ Magistral Small 1.2 \/ Devstral Small 2 (24B) at bf16 (~50-65 tok\/s per L4 card); Mixtral 8x22B Q4\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOpenAI (open weights):\u003c\/strong\u003e gpt-oss-120b MXFP4 native (~80 GB) with room to spare; gpt-oss-20b MXFP4\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGoogle Gemma 3:\u003c\/strong\u003e 27B bf16; Phi-4 14B bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNVIDIA Nemotron:\u003c\/strong\u003e Llama-3.1-Nemotron Super 49B Q4-Q6; Pixtral 12B \/ Pixtral Large Q4 (~72 GB)\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 Models\u003c\/h3\u003e\n\u003cp style=\"font-size:15px;color:#333\"\u003eQwen3-VL-8B\/32B, Qwen3-VL-30B-A3B MoE, InternVL3 up to 78B Q4 (~48 GB), InternVL3.5-38B, DeepSeek-VL2, Llama 3.2 11B Vision bf16, Llama 3.2 90B Vision Q4 (~52 GB), Molmo 72B Q4, Gemma 3 12B\/27B multimodal, MiniCPM-V 2.6 \/ MiniCPM-o 2.6, GLM-4.6V-Flash.\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 (~20-35 s\/image on single L4 at fp8); FLUX.1 Kontext [dev]; FLUX Tools; SD 3.5 Large (18 GB fp16 \/ 11 GB fp8); SDXL 1.0; HunyuanImage-2.1 (~34 GB bf16); HunyuanDiT; Kolors 2.0; 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 T2V-A14B \/ I2V-A14B MoE (tight at bf16 ~54 GB); Wan 2.2 TI2V-5B fast path; HunyuanVideo 13B Q4-Q8 (~30 GB); HunyuanVideo 1.5 (8.3B); CogVideoX-5B; Open-Sora 2.0 Q8 (~16 GB); Mochi-1 Q4 (~18 GB); LTX-Video; Pyramid Flow; SVD \/ SV3D \/ SV4D; NVIDIA Cosmos.\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); Parakeet-TDT; Canary 1B; Qwen3-ASR; SenseVoice\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTTS:\u003c\/strong\u003e CosyVoice 2 \/ 3; Kokoro 82M; Stable Audio Open; XTTS v2; StyleTTS 2; Step-Audio-EditX\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRealtime \/ S2S:\u003c\/strong\u003e Kyutai Moshi 7B; Step-Audio 2 mini\/R1; Qwen2.5-Omni-7B\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMusic \/ SFX:\u003c\/strong\u003e MusicGen \/ AudioGen \/ Bark; SeamlessM4T v2\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 serving\u003c\/h3\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e6 concurrent streams of a 24 GB Q4 model (one per card): e.g. 6x Qwen3-14B Q4 agents\u003c\/li\u003e\n\u003cli\u003eMixed fleet: Llama 3.3 70B Q4 (tensor-parallel over 2 cards) + FLUX.1 (1 card) + Whisper-turbo (1 card) + Moshi (1 card) + BGE-M3 embedder (1 card)\u003c\/li\u003e\n\u003cli\u003eEmbedding service at high QPS — 6x parallel embed streams of BGE-M3 \/ E5 \/ Nomic \/ Cohere Embed\u003c\/li\u003e\n\u003cli\u003eVideo transcode farm — 6x parallel NVENC\/NVDEC streams\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\u003eSaaS multi-tenant LLM API — serve 20-40 concurrent users across a 24B\/32B model with room for image and ASR alongside\u003c\/li\u003e\n\u003cli\u003eRAG backend — query-side embedder + 70B Q4 reader + reranker, sub-second latency, 50 QPS\u003c\/li\u003e\n\u003cli\u003eVideo-AI pipeline — live transcode + caption + moderation on 6 parallel streams\u003c\/li\u003e\n\u003cli\u003eEdge AI appliance near the office — low acoustic profile, zero datacenter dependency\u003c\/li\u003e\n\u003cli\u003eMid-tier model R\u0026amp;D bench — rapid iteration on 30-70B fine-tunes, one card per experiment\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\"\u003eMeasured performance\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 references | NVIDIA L4 datasheet + community benchmarks\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\"\u003ePer-card INT8 TOPS (NVIDIA datasheet)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e242 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eAggregate INT8 TOPS (6 cards)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e1 452 TOPS\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 3.1 8B Q4 on single L4 (community)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~35-45 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\"\u003eBGE-M3 embedding QPS on L4 (community)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~800 QPS at 512-token input\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eWhisper v3 turbo realtime factor\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~1.5-2x realtime per card\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 external references, not measured on Kentino hardware. Kentino will publish first-party numbers after the first customer build.\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\u003eFrontier 200B+ MoE at Q4+ with long context — 4x L40 or 8x RTX 4090 (192 GB pool, contiguous TP) is the right fit\u003c\/li\u003e\n\u003cli\u003eTraining workloads — L4 lacks FP8 and bandwidth for efficient training\u003c\/li\u003e\n\u003cli\u003eSingle-workload peak throughput — per-card compute is modest vs L40 \/ RTX Pro 6000\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\"\u003eNVIDIA OEM 3-year warranty on L4 + Kentino integration warranty. Build 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\u003e4 TB NVMe upgrade for model library staging\u003c\/li\u003e\n\u003cli\u003e24U open rack cabinet with managed PDU\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c\/div\u003e","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52940172656968,"sku":null,"price":28681.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0843\/5479\/3800\/files\/kentino-ai-server-4-gpu-topdown_6b2c51b2-25c1-479d-929a-29eebe60e5ef.jpg?v=1776940959","url":"https:\/\/kentino.com\/nl\/products\/k-ai-144-rome-l4-1452tops-6-nvidia-l4-epyc-milan","provider":"Kentino","version":"1.0","type":"link"}