{"product_id":"k-ai-384-rome-rtxpro6000mq-4-rtx-pro-6000-blackwell-max-q-turbofan-384-gb-ecc-vram","title":"K-AI 384 Rome RTXPro6000MQ — 4× RTX Pro 6000 Blackwell Max-Q Turbofan (384 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 384 Rome RTXPro6000MQ 8000TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003e384 GB ECC VRAM Lab Server\u003cbr\u003e4x RTX Pro 6000 Max-Q Turbofan | EPYC Milan | 8 000 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\"\u003e8 000\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\"\u003e384 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\"\u003efp8\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eBlackwell native\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\"\u003eQuiet\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003eturbofan cooling\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 inference server with four NVIDIA RTX Pro 6000 Blackwell Max-Q turbofan (blower) cards (96 GB ECC each) pooled to 384 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. Same Blackwell silicon as the Server Edition — identical inference envelope, identical throughput — with a quieter blower cooler suited to lab, R\u0026amp;D, and office-adjacent environments.\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\"\u003e4x NVIDIA RTX Pro 6000 Blackwell Max-Q 96 GB ECC (turbofan \/ blower cooler, 600 W TGP, PCIe 5.0 x16, 2000 INT8 TOPS\/card, fp8 native)\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\"\u003e384 GB aggregate ECC 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 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\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 (3x 120 mm front intake + 1x 120 mm rear exhaust). GPU cards self-cooled via turbofan blower (rear exhaust) — quieter for lab environments.\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: 4 x 600 W = 2 400 W\u003c\/li\u003e\n\u003cli\u003eSystem total under full load: ~2 775 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 5 000 W (dual 2.5 kW synced) — 44.5% 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\"\u003eThermal profile (Max-Q)\u003c\/h3\u003e\n\u003cp style=\"margin:0;font-size:14px;color:#444\"\u003eMax-Q uses a turbofan (blower) cooler with directional rear-of-card exhaust. Expected GPU hotspot 72-80 C under continuous load. Materially quieter than passive cards in a high-static-pressure chassis. Better suited to non-datacenter airflow, open-rack, or lab \/ office-adjacent placement. Silicon, TDP, ECC, and performance are identical to the Server Edition.\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\"\u003eIdentical to the Server Edition (K-AI 384 Rome RTXPro6000) — same Blackwell silicon, same 384 GB ECC pool, same fp8 native, same model compatibility. The difference is acoustic, not computational.\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\u003eDeepSeek V3 \/ V3-0324 \/ V3.1 \/ V3.2 \/ R1 \/ R1-0528\u003c\/strong\u003e Q3 (~290 GB) comfortably on-card (~30-40 tok\/s single, published reference); fp8 native (~670 GB) with RAM spill\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-Coder-480B-A35B\u003c\/strong\u003e Q3 (~350 GB tight with RAM spill) — SOTA open coding agent (~18-25 tok\/s single, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-235B-A22B\u003c\/strong\u003e Q6\/Q8 (~200-280 GB) with long ctx and multi-user batching\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGLM-5 \/ GLM-5.1\u003c\/strong\u003e Q3 (~317 GB) — Chinese frontier, close to Claude Opus 4.6 on coding\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eKimi-K2\u003c\/strong\u003e 1.58-bit UD (~240 GB) — trillion-param agent at real throughput\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHunyuan-Large\u003c\/strong\u003e 389B\/52B Q4 (~220 GB), fp8 native (~390 GB spill)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eERNIE-4.5-424B-A47B\u003c\/strong\u003e Q4 (~240 GB); \u003cstrong\u003eMiniMax-M1\u003c\/strong\u003e Q4 (~260 GB) 1M-ctx\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama 3.3 70B\u003c\/strong\u003e bf16 resident on a single card (96 GB\/card)\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\u003eMistral Large 3\u003c\/strong\u003e (675B\/41B MoE, Apache 2.0) Q3 (~317 GB) — frontier Western open weights (~20-30 tok\/s single, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama 4 Maverick\u003c\/strong\u003e (400B\/17B) Q4 (~232 GB) with generous KV budget (~45-55 tok\/s single, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama-3.1-Nemotron Ultra 253B\u003c\/strong\u003e Q4-Q6 (~119-207 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003egpt-oss-120b\u003c\/strong\u003e MXFP4 native (80 GB) with concurrent fleet headroom\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePixtral Large \/ Mistral Large 2\u003c\/strong\u003e bf16 (~248 GB); \u003cstrong\u003eDevstral 2\u003c\/strong\u003e 123B bf16 — 256k top open coding\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama 3.3 70B\u003c\/strong\u003e bf16 on a single card; 4x concurrent 70B deployments possible\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 bf16 (~240 GB); InternVL3.5-241B-A28B Q4 (~135 GB); Llama 3.2 90B Vision bf16; Pixtral Large 124B bf16; Qwen3-Omni-30B-A3B; Molmo 72B; ERNIE-4.5-VL; GLM-4.6V 106B bf16 on TP. Blackwell fp8 delivers ~2x throughput on vision-tower inference vs Ada.\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 at fp8 native (~15-20 s per 1024x1024 image on single RTX Pro 6000, published reference); SD 3.5 Large; HunyuanImage-2.1 (17B native 2K); HunyuanImage-3.0 80B\/13B MoE; AuraFlow; OmniGen; 4x concurrent ComfyUI workers.\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; HunyuanVideo 13B bf16 both experts; Open-Sora 2.0 (11B) bf16; CogVideoX-5B; Mochi-1; LTX-Video; Pyramid Flow; SVD \/ SV3D \/ SV4D; NVIDIA Cosmos Predict 2.\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; Canary; Qwen3-ASR; SenseVoice\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTTS:\u003c\/strong\u003e CosyVoice 2\/3; Kokoro; 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\u003cli\u003e\n\u003cstrong\u003eMusic \/ SFX:\u003c\/strong\u003e MusicGen \/ AudioGen \/ Bark \/ SeamlessM4T\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\u003eDeepSeek V3 Q3 + concurrent 70B + FLUX.1 + Whisper all resident\u003c\/li\u003e\n\u003cli\u003e4-way tensor-parallel on 350-400B class at Q4\u003c\/li\u003e\n\u003cli\u003ePer-card tenant isolation — one 96 GB Llama 3.3 70B bf16 per card, 4 independent inference silos\u003c\/li\u003e\n\u003cli\u003eMulti-model RAG: reader + reranker + vision + embedder all on one 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\u003eFrontier open-weight inference for a lab \/ R\u0026amp;D team where acoustic budget matters\u003c\/li\u003e\n\u003cli\u003eSmall-team server room without dedicated datacenter airflow — Max-Q self-cooling tolerates open-rack placement\u003c\/li\u003e\n\u003cli\u003eOffice-adjacent AI workstation for a specialist team (ML research, agentic tools)\u003c\/li\u003e\n\u003cli\u003efp8-native serving (DeepSeek \/ R1 \/ Hunyuan) in lab settings\u003c\/li\u003e\n\u003cli\u003e4-tenant per-card isolation workload with noise budget\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 | Same silicon as Server Edition | 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\"\u003eRTX Pro 6000 per-card INT8 TOPS\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e2 000 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\"\u003eRTX Pro 6000 memory bandwidth\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~1 800 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 — DeepSeek V3 Q3 on 4x Blackwell PCIe (single)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~30-40 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 — DeepSeek V3 Q3 on 4x Blackwell PCIe (batch-8)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~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\"\u003eSGLang — Llama 4 Maverick Q4 on 4x Blackwell (single)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~45-55 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\"\u003ellama.cpp — Qwen3-Coder-480B Q3 on 4x Blackwell (single)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~18-25 tok\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eFLUX.1 [dev] fp8 on single RTX Pro 6000\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~1.8 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 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\u003eProper datacenter rack deployments with established hot-aisle airflow — choose the passive Server Edition (K-AI 384 Rome RTXPro6000) instead: same silicon, simpler mechanically\u003c\/li\u003e\n\u003cli\u003eSingle-user workloads up to 70B (4x RTX 5090 is materially cheaper for 128 GB pool)\u003c\/li\u003e\n\u003cli\u003eFrontier training from scratch (no NVLink)\u003c\/li\u003e\n\u003cli\u003eFull DeepSeek V3 Q4 on-card (~404 GB) — upgrade to 6x RTX Pro 6000 \/ 576 GB\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 RAM-spill headroom on Q3 frontier quants\u003c\/li\u003e\n\u003cli\u003e4 TB NVMe Gen4 x4 for frontier-model library (DeepSeek V3 Q3 alone is ~290 GB on disk)\u003c\/li\u003e\n\u003cli\u003eFull 24U rack cabinet with managed PDU + online UPS\u003c\/li\u003e\n\u003cli\u003eAlternative silhouette: passive Server Edition (K-AI 384 Rome RTXPro6000) — same silicon, for datacenter airflow deployments\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c\/div\u003e\n","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52940336922952,"sku":null,"price":46583.0,"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-384-rome-rtxpro6000mq-4-rtx-pro-6000-blackwell-max-q-turbofan-384-gb-ecc-vram","provider":"Kentino","version":"1.0","type":"link"}