{"product_id":"k-ai-192-rome-l40-1448tops-4-nvidia-l40-epyc-milan","title":"K-AI 192 Rome L40 1448TOPS — 4× NVIDIA L40 — 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 192 Rome L40 1448TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003e192 GB ECC Enterprise Inference Server\u003cbr\u003e4x NVIDIA L40 Passive | EPYC Milan | 1 448 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 448\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\"\u003e192 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\"\u003eECC\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003edatacenter grade\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\"\u003epassive cooled\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top:20px;font-size:15px;color:#aaa\"\u003eFour passive L40 datacenter cards with ECC memory. Same 192 GB pool as 8x RTX 4090 — but datacenter-grade, ECC-protected, and OEM-warrantied.\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 passive NVIDIA L40 cards pooled to 192 GB ECC VRAM, one AMD EPYC 7643 Milan CPU (48C\/96T), 256 GB DDR4 ECC, 2 TB NVMe boot, and dual synchronized 2 kW ATX PSU. The L40 is the datacenter sibling of the RTX 4090 — passive-cooled, ECC-equipped, NVENC\/NVDEC hardware encoders on-die, and NVIDIA OEM 3-year warranty. Runs vLLM, SGLang, llama.cpp, Triton, TensorRT-LLM out of the box.\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 L40 48 GB ECC GDDR6 (Ada Lovelace, passive, 300 W, dual-slot, PCIe 4.0 x16)\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\"\u003e192 GB ECC across 4 cards (no NVLink on L40)\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\"\u003e256 GB DDR4-2666 ECC RDIMM (4x 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\"\u003eDual 2 kW ATX PSU with sync cable\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 with front-to-back directed airflow\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\"\u003eArctic Freezer 4U-M SP3 tower + 3x 120 mm front intake + 1x 120 mm rear exhaust\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 300 W = 1 200 W\u003c\/li\u003e\n\u003cli\u003eSystem total at full load: ~1 525 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 4 000 W (dual 2 kW synced) — 61.9 % headroom\u003c\/li\u003e\n\u003cli\u003eDual PSU for split power delivery and N+1 capability\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 per card (L40 is Gen4 native). Direct root-complex connection from single EPYC — no PCIe switch. No NVLink — inter-GPU traffic runs PCIe peer-to-peer. Three x16 slots remain for NIC \/ storage expansion.\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 192 GB of ECC VRAM across 4 datacenter cards, this server handles 200B+ frontier MoE at Q4, enterprise multi-tenant serving with strict SLA, and 24\/7 production inference without ECC-related bit-flip drift.\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 Qwen3-235B-A22B Q4 (~132 GB) with long context — the hero config (~12-18 tok\/s single-stream across 4x L40); Qwen3-Coder-480B-A35B Q2 (~160 GB, tight); Qwen3.5-122B-A10B fp8 (~75 GB) with huge KV; Qwen3-32B dense bf16 multiple concurrent streams\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek:\u003c\/strong\u003e DeepSeek-V3\/R1\/V3.1\/V3.2 Q2 (~215 GB with minor RAM spill); DeepSeek-R2 32B — 4x concurrent streams, one per card\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGLM \/ Z.ai:\u003c\/strong\u003e GLM-4.5 \/ 4.6 \/ 4.7 Q4 (~177 GB) — sweet spot for this tier; GLM-4.5-Air 106B\/12B fp8 or bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTencent Hunyuan:\u003c\/strong\u003e Hunyuan-Large Q3 (~160 GB) — 389B MoE with 256k ctx; Hunyuan-A13B fp8 (~80 GB) with huge KV\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBaidu ERNIE-4.5-424B Q3 (~180 GB);\u003c\/strong\u003e InternVL3.5-241B-A28B Q4 (~135 GB); Qwen3.5-397B Q3 (~170 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\u003eMeta Llama:\u003c\/strong\u003e Llama 3.3 70B bf16 with massive KV (~15-18 tok\/s single-stream on 4x L40); Llama 4 Scout bf16 (~218 GB) tight; Llama 4 Maverick 400B\/17B Q3 (~188 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral:\u003c\/strong\u003e Mistral Large 2 \/ Pixtral Large \/ Devstral 2 123B Q6 (~102 GB) comfortable; Mistral Small 3 multi-stream\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOpenAI (open weights):\u003c\/strong\u003e gpt-oss-120b MXFP4 (80 GB) with generous KV\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNVIDIA Nemotron:\u003c\/strong\u003e Llama-3.1-Nemotron Ultra 253B Q4 (~147 GB); Super 49B bf16 multiple streams\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGoogle Gemma 3:\u003c\/strong\u003e 27B multimodal bf16 — multiple resident streams\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOthers:\u003c\/strong\u003e Cohere Command R+ 104B Q6 (~85 GB); OLMo 3.1 32B; Reka Flash 3 21B; IBM Granite 4.0 H-Small\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\"\u003eInternVL3.5-241B-A28B Q4 (~135 GB); Qwen3-VL-235B-A22B Q4; Qwen3-VL-32B bf16; Llama 3.2 90B Vision bf16 (~180 GB); Pixtral Large 124B Q6-bf16; Molmo 72B bf16; GLM-4.6V 106B fp8; Gemma 3 27B multimodal multiple streams; InternVL3 78B bf16; DeepSeek-VL2 full range.\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] bf16 with concurrent generation (~3-4 s per 1024x1024 image on L40); FLUX.1 Kontext [dev]; FLUX Tools; SD 3.5 Large bf16 x 2-3 concurrent; HunyuanImage-2.1 bf16 (~34 GB) multi-stream; HunyuanImage-3.0 base (80B MoE, 13B active) bf16 (~80 GB); HunyuanDiT; Kolors \/ Kolors 2.0; AuraFlow; 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 bf16 dual-expert full-context; Wan 2.2 TI2V-5B fast path; HunyuanVideo 13B bf16 both experts; HunyuanVideo 1.5; CogVideoX-5B bf16; Open-Sora 2.0 11B bf16; Mochi-1 bf16 (~42 GB) multi-stream; 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 (~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; XTTS v2; Stable Audio Open; 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\u003eEnterprise production LLM gateway — Qwen3-235B Q4 or GLM-4.5\/4.6 Q4 serving 16-32 concurrent users with strict SLA\u003c\/li\u003e\n\u003cli\u003eMixed resident stack: 235B MoE + FLUX.1 + Whisper-turbo + Moshi with partitioned VRAM and ECC protection\u003c\/li\u003e\n\u003cli\u003eLive video + AI pipeline — NVENC\/NVDEC hardware encoders stream 6-8 parallel captioning + moderation pipelines\u003c\/li\u003e\n\u003cli\u003eMulti-tenant RAG — query-side embedder + 70B reader + reranker at sub-second P99 latency\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 at 192 GB pool (Qwen3-235B Q4, GLM-4.5\/4.6\/4.7 Q4, Llama 4 Scout bf16)\u003c\/li\u003e\n\u003cli\u003eEnterprise multi-tenant serving with strict SLA — ECC reliability over long runs\u003c\/li\u003e\n\u003cli\u003eRAG + vector DB serving with high-quality retrieval models concurrent\u003c\/li\u003e\n\u003cli\u003eMedia \/ video AI pipelines — NVENC \/ NVDEC hardware path, VFX rendering, transcribe\/translate\u003c\/li\u003e\n\u003cli\u003eDatacenter silent-operation deployments — passive cards, low acoustic profile near office space\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 L40 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\"\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\"\u003eAggregate INT8 TOPS (4 cards)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e1 448 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\"\u003ePer-card VRAM\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e48 GB ECC GDDR6, 864 GB\/s bandwidth\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 3.3 70B Q6 via vLLM (community)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e30-50 tok\/s single-stream, 150+ tok\/s batch-16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eFLUX.1 [dev] bf16 on L40 (community)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~3-4 s per 1024x1024 image\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eNVENC \/ NVDEC\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003eGen-8 hardware encoders on-die (video AI pipeline)\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\u003eTraining large models from scratch (no NVLink, limited FP8 tensor compute)\u003c\/li\u003e\n\u003cli\u003eSingle-user budget inference (4x L4 or 2x L40 is materially cheaper)\u003c\/li\u003e\n\u003cli\u003eDense bf16 70B at very long context on one model — prefer 2x RTX Pro 6000 Server Edition (same 192 GB pool, less TP overhead)\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 L40 + 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\u003eUpgrade RAM to 512 GB (add 4x 64 GB DDR4 — four DIMM slots still open)\u003c\/li\u003e\n\u003cli\u003e4 TB NVMe for model library staging\u003c\/li\u003e\n\u003cli\u003eFull 24U rack cabinet with managed PDU + online UPS 5 kVA\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c\/div\u003e","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52940180193608,"sku":null,"price":40798.0,"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-192-rome-l40-1448tops-4-nvidia-l40-epyc-milan","provider":"Kentino","version":"1.0","type":"link"}