{"product_id":"k-ai-768-turindual-rtxpro6000mq-16000tops-8-rtx-pro-6000-blackwell-max-q-ai-frontier-server-dual-turin","title":"K-AI 768 TurinDual RTXPro6000MQ 16000TOPS — 8× RTX Pro 6000 Blackwell Max-Q AI Frontier Server (Dual Turin)","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 768 TurinDual RTXPro6000MQ 16000TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003e768 GB ECC VRAM Frontier Flagship\u003cbr\u003e8x RTX Pro 6000 Max-Q | Dual EPYC Turin | 16 000 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\"\u003e16 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\"\u003e768 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\"\u003eGen5\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003ePCIe end-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\"\u003eFlagship\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003efrontier multi-tenant\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top:16px;font-size:13px;color:#777\"\u003eCPU pricing finalized at order — Turin 9005-series market moves weekly in Q2 2026.\u003c\/p\u003e\n\u003cp style=\"margin-top:12px;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\"\u003eTop of the Kentino AI server lineup. A 7U rack-mount flagship frontier-tier inference platform with eight NVIDIA RTX Pro 6000 Blackwell Max-Q turbofan cards pooled to 768 GB ECC VRAM, two AMD EPYC Turin 9005-series CPUs (Zen5c, SP5), 1.5 TB DDR5-4800 ECC (all 24 channels populated), 4 TB NVMe boot, and 5x 1200 W server PSU. PCIe Gen5 end-to-end. DeepSeek V3 fp8 native (~670 GB) on-card. Kimi-K2 Q4-Q5. 4 frontier-class models resident simultaneously.\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\"\u003e8x NVIDIA RTX Pro 6000 Blackwell Max-Q 96 GB ECC (turbofan, 600 W TDP spec, 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\"\u003e768 GB total across 8 cards (no NVLink — P2P over PCIe Gen5 at ~55-60 GB\/s within socket, cross-socket via CPU interconnect)\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\"\u003e2x AMD EPYC Turin 9005-series (Zen5c, SP5, PCIe 5.0) — quote-pending, exact SKU confirmed at order\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 TURIN2D24XGM\/500W (dual SP5 Turin, PCIe 5.0, 24x DDR5, 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\"\u003e1.5 TB DDR5-4800 ECC RDIMM (24x 64 GB — all 24 channels populated, ~920 GB\/s aggregate)\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\"\u003e4 TB NVMe M.2 (PCIe 4.0 x4) — sized for frontier checkpoints\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\"\u003e5x 1200 W server PSU set (6 kW total)\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\"\u003e7U 8-GPU rack-mount, 10 PCIe slot capacity, active Gen5 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\"\u003e2x SP5 Turin tower coolers + 8x 120 mm Martech chassis fans. Per-GPU turbofan blowers self-contained.\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 (spec): 8 x 600 W = 4 800 W\u003c\/li\u003e\n\u003cli\u003eCPU draw: 2 x 360 W = 720 W (Turin mid-tier estimate)\u003c\/li\u003e\n\u003cli\u003eSystem total at spec full load: ~5 720 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 6 000 W — ~4.7% raw headroom at spec\u003c\/li\u003e\n\u003cli\u003eReal-world: Max-Q sustains 520-550 W in inference, lifting sustained headroom to ~20%+\u003c\/li\u003e\n\u003cli\u003eFirmware power-cap at 520 W available for guaranteed headroom\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\"\u003eDual Turin provides 2x 128 PCIe Gen5 lanes. TURIN2D24XGM\/500W routes 8 GPU slots direct-attached to the CPUs at Gen5 x16 via active risers — 4 slots per CPU root. No PCIe switch in the GPU path — clean dual-root topology. NUMA tuning required for optimal cross-socket peer-to-peer. No NVLink; P2P at ~55-60 GB\/s per direction within socket.\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 768 GB of pooled ECC VRAM — the top of the Kentino envelope — this server runs DeepSeek V3 fp8 native (~670 GB) on-card, Kimi-K2 Q4-Q5 (~630 GB) comfortable, and the defining use case: 4 frontier-class models resident simultaneously for multi-tenant production serving.\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 at production quants\u003c\/p\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eKimi-K2\u003c\/strong\u003e (Base \/ Instruct \/ Thinking) at Q4_K_M \/ Q5_K_M (~630 GB) comfortable (~15-25 tok\/s single, published reference) — flagship Chinese frontier on a single box at production quants\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek V3 \/ R1 \/ V3.1 \/ V3.2\u003c\/strong\u003e at fp8 native (~670 GB) on-card (~30-50 tok\/s single, published reference) — Blackwell fp8 tensor cores run this natively at speed\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek V3\u003c\/strong\u003e at Q4_K_M (~404 GB) with multiple concurrent large-batch serving instances\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGLM-5 \/ GLM-5.1\u003c\/strong\u003e (~745B\/44B) at Q3-Q4 (~420-560 GB) comfortable on-card\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIntern-S1-Pro\u003c\/strong\u003e (1T\/22B active, SAGE) at Q3-Q4 (~440-580 GB) comfortable\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-Coder-480B-A35B\u003c\/strong\u003e at Q5-Q6 (~340-400 GB) with 1M ctx\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3-235B-A22B\u003c\/strong\u003e at bf16 (~470 GB) with generous KV for long context\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eERNIE-4.5-424B-A47B\u003c\/strong\u003e at Q6 (~360 GB); \u003cstrong\u003eHunyuan-Large\u003c\/strong\u003e at fp8 (~390 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMiniMax-Text-01 \/ M1\u003c\/strong\u003e at Q5-Q6 (~325-390 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 at production quants\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) at Q3-Q4 (~317-404 GB) comfortable (~20-30 tok\/s single, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama 4 Maverick\u003c\/strong\u003e (400B\/17B, 128 experts) at Q5-Q6 (~290-350 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLlama-3.1-Nemotron Ultra 253B\u003c\/strong\u003e at bf16 (~506 GB) on-card\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSnowflake Arctic\u003c\/strong\u003e at Q5-Q6 (~350-420 GB); \u003cstrong\u003eGrok-1\u003c\/strong\u003e at Q5-Q6 (~225-270 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDBRX Instruct\u003c\/strong\u003e 132B\/36B at bf16 (~264 GB) multi-instance\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 flagship VLM with long context; InternVL3.5-241B-A28B at bf16 (~482 GB); GLM-4.5V \/ 4.6V 106B bf16 multi-instance; Llama 3.2 90B Vision bf16 multi-instance; Pixtral Large 124B bf16; Molmo 72B bf16 multi-instance.\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\"\u003eHunyuanImage-3.0 Instruct concurrent instances; FLUX.1 multi-instance (~15-20 s per 1024x1024 image, published reference); SD 3.5 Large; SDXL; AuraFlow; OmniGen; HunyuanImage-2.1; Kolors 2.0 — full Chinese + Western image stack resident concurrent.\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 — many concurrent streams; HunyuanVideo 13B bf16 multiple concurrent streams; Open-Sora 2.0 (11B) multi-instance; Mochi-1 (10B) multi-instance; NVIDIA Cosmos Predict 2 up to 14B.\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\"\u003eFull stack resident at batch: Whisper v3 large, Parakeet-TDT, Canary 1B, Moshi 7B realtime, Qwen3-Omni, Step-Audio R1, CosyVoice 3.0, Kokoro, Stable Audio Open.\u003c\/p\u003e\n\n\u003ch3 style=\"font-size:18px;font-weight:700;margin:28px 0 12px 0;color:#0d0d0d\"\u003eMulti-model \/ multi-tenant serving (the defining use case)\u003c\/h3\u003e\n\u003cul style=\"font-size:15px;color:#333;line-height:1.8\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-tenant frontier production:\u003c\/strong\u003e 4 frontier-class models resident simultaneously — e.g. DeepSeek V3 fp8 + Kimi-K2 Q4 + Mistral Large 3 Q3 + Qwen3-Coder-480B Q5 — with partitioned VRAM and per-tenant SLOs\u003c\/li\u003e\n\u003cli\u003eConcurrent fp8-native Blackwell inference (DeepSeek V3 \/ R1 family, Hunyuan fp8) + quantized serving on separate PCIe domains\u003c\/li\u003e\n\u003cli\u003eResearch A\/B across 4-5 frontier open-weight models at research-grade quants\u003c\/li\u003e\n\u003cli\u003eAgentic platform with a 400B+ primary + multiple 30-70B specialists resident\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\u003eMulti-tenant frontier open-weight production — multiple frontier models resident concurrently with per-tenant isolation\u003c\/li\u003e\n\u003cli\u003eSovereign frontier AI deployment — on-prem DeepSeek V3 fp8 \/ Kimi-K2 \/ Mistral Large 3 access, EU data residency\u003c\/li\u003e\n\u003cli\u003eFrontier research lab with A\/B evaluation across 4+ frontier open-weight models at research-grade quants\u003c\/li\u003e\n\u003cli\u003eEnterprise agentic platform where 400B+ MoE drives tools + multiple specialist models\u003c\/li\u003e\n\u003cli\u003eAir-gapped regulated-industry inference at frontier scale with ECC + PCIe Gen5\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\"\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\"\u003evLLM — DeepSeek V3 fp8 on 8x RTX Pro 6000 (single)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~30-50 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 fp8 on 8x RTX Pro 6000 (batch-32)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e300-500 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\"\u003eKimi-K2 Q4 serving on 8x RTX Pro 6000 (single)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~15-25 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~15-20 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\"\u003eExact figures confirmed at PoC stage. Kentino 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\u003eBudget-conscious deployments — flagship SKU at flagship price\u003c\/li\u003e\n\u003cli\u003eTraining from scratch on frontier-class models — no NVLink, PCIe P2P only (for training at this scale H100\/H200 SXM or GB200 NVLink fabric is the right tool)\u003c\/li\u003e\n\u003cli\u003ePlug-and-play deployment — frontier multi-tenant MoE serving requires a skilled MLOps team\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 config, driver install, burn-in, memtest, functional verification, NUMA tuning, and LLM environment setup (vLLM \/ SGLang \/ llama.cpp \/ CUDA 13 stack with fp8 Blackwell kernels). 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 MCX555A-ECAT or ConnectX-7 Gen5 100 GbE NIC for multi-node scale-out\u003c\/li\u003e\n\u003cli\u003eMellanox ConnectX-6 25 GbE SFP28 for datacenter fabric\u003c\/li\u003e\n\u003cli\u003eSecond 4 TB NVMe for dataset \/ model library (frontier checkpoints are large — Kimi-K2 bf16 alone is ~1 TB)\u003c\/li\u003e\n\u003cli\u003eFull 24U rack cabinet with front perforated door and managed PDU\u003c\/li\u003e\n\u003cli\u003eOnline UPS 10 kVA (graceful shutdown on power event)\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c\/div\u003e\n","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52942290059592,"sku":null,"price":0.0,"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\/it\/products\/k-ai-768-turindual-rtxpro6000mq-16000tops-8-rtx-pro-6000-blackwell-max-q-ai-frontier-server-dual-turin","provider":"Kentino","version":"1.0","type":"link"}