{"product_id":"k-ai-288-rome-l40-6-nvidia-l40-passive-enterprise-288-gb-ecc-vram","title":"K-AI 288 Rome L40 — 6× NVIDIA L40 Passive Enterprise (288 GB ECC VRAM)","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 288 Rome L40 2172TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003e288 GB ECC VRAM Enterprise Server\u003cbr\u003e6x NVIDIA L40 Passive | EPYC Milan | 2 172 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\"\u003e2 172\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\"\u003e288 GB\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eECC VRAM 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\"\u003eECC\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eend-to-end\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\"\u003eproduction-rated\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top:20px;font-size:15px;color:#aaa\"\u003ePublished external references. Not measured on Kentino hardware.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cp style=\"font-size:17px;color:#333;margin-bottom:24px\"\u003eA 4U rack-mount enterprise inference server with six NVIDIA L40 Ada Lovelace passive datacenter cards (48 GB ECC each) pooled to 288 GB ECC VRAM, one AMD EPYC 7643 Milan CPU (48C\/96T), 384 GB DDR4-2666 ECC, 2 TB NVMe boot, and dual synchronized 2.5 kW ATX PSU. ECC end-to-end, purpose-built for 24\/7 enterprise production where bit-level integrity and serviceable failure domains matter.\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 L40 48 GB ECC (Ada Lovelace, passive datacenter, 300 W, PCIe 4.0 x16, dual-slot, 362 INT8 TOPS\/card)\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\"\u003e288 GB aggregate ECC across 6 cards (no NVLink on L40 PCIe SKU)\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\"\u003e384 GB DDR4-2666 ECC RDIMM (6x 64 GB — 2 DIMM slots open for upgrade to 512 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\"\u003e2x 2.5 kW ATX with dual-PSU sync cable (5 kW aggregate)\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-slot 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 (Arctic Freezer 4U-M class) + 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 300 W = 1 800 W\u003c\/li\u003e\n\u003cli\u003eSystem total under full load: ~2 175 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 5 000 W (dual 2.5 kW synced) — 56.5% headroom\u003c\/li\u003e\n\u003cli\u003eDual PSU for split 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\"\u003eROMED8-2T exposes 7x PCIe 4.0 x16 direct from EPYC Milan. Six slots populated with passive Gen4 x16 risers — one free slot for NIC \/ storage. No PCIe switch required. L40 native link is PCIe 4.0 x16 — no bandwidth loss. No NVLink; inter-GPU traffic runs PCIe peer-to-peer.\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 288 GB of pooled ECC VRAM across 6 passive L40 cards, this server handles frontier open-weight LLMs at Q4, multi-model concurrent serving, video\/media pipelines, and 24\/7 enterprise production inference. Note: L40 is Ada Lovelace, not Blackwell — fp8 upcasts to bf16. Use GGUF Q4\/Q5 or AWQ\/GPTQ int4 for maximum VRAM efficiency.\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\u003eQwen3-235B-A22B\u003c\/strong\u003e Q4 (~132 GB) with very long context + generous KV budget (~15-20 tok\/s single, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGLM-4.5 \/ 4.6 \/ 4.7\u003c\/strong\u003e Q4 (~177 GB) comfortable on 6-way TP (~12-18 tok\/s single, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHunyuan-Large\u003c\/strong\u003e 389B\/52B Q3 (~160 GB); \u003cstrong\u003eERNIE-4.5-424B-A47B\u003c\/strong\u003e Q3 (~180 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-Coder-480B-A35B\u003c\/strong\u003e Q2 (~160 GB) flagship coding agent\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMiniMax-M1 \/ Text-01\u003c\/strong\u003e Q3 (~180 GB) 1M-ctx Lightning Attention\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-30B-A3B \/ QwQ-32B \/ Qwen3-32B\u003c\/strong\u003e — single-card with 6 parallel streams\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek-R2\u003c\/strong\u003e 32B sparse MoE — single card per stream, 6 concurrent sessions\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 bf16 (~142 GB) multi-tenant serving (~17 tok\/s single, published reference), or Q4 (~43 GB) with 6 concurrent copies\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama 4 Scout\u003c\/strong\u003e 109B\/17B bf16 (~218 GB tight) or Q4 (~63 GB) comfortable\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral Small 3 \/ Magistral \/ Devstral Small\u003c\/strong\u003e (24B) bf16 (~40-50 tok\/s single, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePixtral Large \/ Mistral Large 2\u003c\/strong\u003e Q6-Q8 (~90-140 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama-3.1-Nemotron Ultra 253B\u003c\/strong\u003e Q4 (~119 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003egpt-oss-120b\u003c\/strong\u003e MXFP4 (~80 GB via GGUF on Ada — note Ada upcast caveat)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCohere Command R+\u003c\/strong\u003e 104B Q4 RAG stack\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-235B-A22B Q4; Qwen3-VL-32B; InternVL3.5-78B \/ 241B-A28B Q4 (~135 GB); Llama 3.2 90B Vision bf16 (~180 GB); Pixtral 12B; Molmo 72B; Gemma 3 12B\/27B multimodal; GLM-4.6V full (106B bf16); MiniCPM-o 2.6. L40's NVENC\/NVDEC is particularly useful for high-throughput VLM document \/ video pipelines.\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] \/ Kontext \/ Tools across multiple workers concurrently (~3.5 s per 1024x1024 image on single L40 fp8, published reference) — 6x ComfyUI worker farm possible; SD 3.5 Large; HunyuanImage-2.1 (17B) bf16; HunyuanDiT; Kolors 2.0; AuraFlow; OmniGen.\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 dual-expert bf16 (~54 GB, ~20-30 s per 4s clip at 720p, published reference); HunyuanVideo 13B bf16 both experts; Open-Sora 2.0 bf16; CogVideoX-5B; Mochi-1; LTX-Video; Pyramid Flow; NVIDIA Cosmos Predict 2. L40's hardware NVENC\/NVDEC handles caption \/ moderation \/ transcode at scale alongside generation.\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; Parakeet-TDT 1.1B; 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; Step-Audio-EditX\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRealtime \/ S2S:\u003c\/strong\u003e Kyutai Moshi; Step-Audio 2 mini \/ R1; Qwen2.5-Omni-7B\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\u003eMulti-model residency — Qwen3-235B Q4 + FLUX.1 + HunyuanVideo + Whisper-turbo + Moshi + embedder, all resident\u003c\/li\u003e\n\u003cli\u003e6 concurrent 48 GB-class workloads (one per card): 6x Qwen3-VL-32B, or 6x FLUX.1 workers, or 6x ASR streams\u003c\/li\u003e\n\u003cli\u003e6-way tensor-parallel for 200B+ MoE at Q4 with real context\u003c\/li\u003e\n\u003cli\u003eRAG pipelines — Command R+ \/ Qwen3 + reranker + embedder + image analysis on same host\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\u003e24\/7 production LLM inference backend — 100+ concurrent users on 200B+ MoE at Q4, ECC-protected\u003c\/li\u003e\n\u003cli\u003eMedia-AI pipeline at enterprise scale — caption + moderation + thumbnail + transcode on 6 parallel streams via NVENC\/NVDEC\u003c\/li\u003e\n\u003cli\u003eMulti-tenant SaaS where per-tenant isolation across physical cards matters\u003c\/li\u003e\n\u003cli\u003eRAG backend with Command R+ reader + reranker + embedder + vision fully resident\u003c\/li\u003e\n\u003cli\u003eReliability-first pair replacing the 12x L40 Legacy — two K-AI 288 servers = 576 GB aggregate with independent failure domains\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\"\u003eExternal references | Not measured on Kentino 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\"\u003eL40 per-card INT8 TOPS\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e362 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\"\u003eL40 memory bandwidth\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e864 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\"\u003evLLM — Llama 3.3 70B AWQ INT4 on 2x L40 TP (single)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~25-35 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 on 2x L40 TP (batch-16)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~150-200 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 — GLM-4.6 Q4 on 6x L40 (single)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~12-18 tok\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eFLUX.1 [dev] on single L40 fp8\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~3.5 s per 1024x1024 image\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\"\u003eKentino will publish first-party numbers after the initial 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\u003efp8-native inference at full speed — Ada upcasts to bf16; use GGUF Q4\/Q5 or AWQ\/GPTQ int4 instead. For fp8 native see K-AI 384 Rome RTXPro6000 (Blackwell)\u003c\/li\u003e\n\u003cli\u003eTraining large models from scratch (no NVLink)\u003c\/li\u003e\n\u003cli\u003eBudget single-user inference — 4x L4 or 4x 5080 is materially cheaper for small workloads\u003c\/li\u003e\n\u003cli\u003eFrontier 600B+ dense at Q4+ (require 576 GB+ pool — see 6x 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\"\u003e3 years\u003c\/div\u003e\n\u003cdiv style=\"font-size:13px;color:#666;text-transform:uppercase;letter-spacing:1px\"\u003eNVIDIA OEM GPU 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\"\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, memtest, 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\u003eUpgrade RAM to 512 GB DDR4 (add 2x 64 GB — 2 DIMM slots open) for heavier KV budget\u003c\/li\u003e\n\u003cli\u003e4 TB NVMe Gen4 x4 for model library staging\u003c\/li\u003e\n\u003cli\u003eFull 24U rack cabinet with managed PDU + online UPS (critical for 24\/7 ECC workloads)\u003c\/li\u003e\n\u003cli\u003ePaired second K-AI 288 unit — replaces the 12x L40 Legacy envelope with two independent failure domains\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c\/div\u003e\n","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52940296782152,"sku":null,"price":59490.0,"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\/zh\/products\/k-ai-288-rome-l40-6-nvidia-l40-passive-enterprise-288-gb-ecc-vram","provider":"Kentino","version":"1.0","type":"link"}