{"product_id":"k-ai-96-rome-l40-724tops-2x-nvidia-l40-ecc-production-inference-server","title":"K-AI 96 Rome L40 724TOPS — 2x NVIDIA L40 ECC Production Inference Server","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 96 Rome L40 724TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003e2x L40 ECC Production Server\u003cbr\u003e96 GB ECC VRAM | EPYC Milan | 724 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\"\u003e724\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\"\u003e96 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\"\u003eproduction\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top:20px;font-size:15px;color:#aaa\"\u003eEntry enterprise ECC 24\/7 box — 2x L40 passive, 96 GB ECC VRAM pool, datacenter-grade alternative to the 4090 tier for regulated deployments.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cp style=\"font-size:17px;color:#333;margin-bottom:24px\"\u003eA two-GPU production-class inference server built on ROMED8-2T \/ EPYC Milan with two passive NVIDIA L40 cards. 96 GB ECC GDDR6 pool at the same VRAM envelope as the 4x RTX 4090 workhorse, but with full datacenter certification, ECC memory on every card, and a thermal design built for 24\/7 duty cycle. The right call where RTX 4090 would raise warranty, reliability or compliance concerns — finance, healthcare, formal verification, and any sustained-production LLM \/ VLM serving.\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\"\u003e2x NVIDIA L40 48 GB GDDR6 ECC (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\"\u003e96 GB ECC (no NVLink)\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\"\u003e1 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\"\u003eSingle 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, passive Gen4 x16 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\"\u003eSP3 tower cooler (Arctic Freezer 4U-M), 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) + IPMI\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: 2 x 300 W = 600 W\u003c\/li\u003e\n\u003cli\u003eSystem total at full load: ~925 W\u003c\/li\u003e\n\u003cli\u003ePSU total: 2 000 W — 53.8 % headroom\u003c\/li\u003e\n\u003cli\u003eComfortable single-PSU margin, quiet operation\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 at both GPUs (L40 is native Gen4 x16). 16 lanes direct from CPU root complex — no PCIe switch. NVLink not present on L40 — inter-GPU comms via PCIe P2P. 864 GB\/s memory bandwidth per card.\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 96 GB of ECC VRAM across 2 passive L40 cards, this server handles enterprise 24\/7 LLM serving, regulated deployments, image and video generation, and multi-tenant inference where ECC reliability and datacenter warranty matter.\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\u003eQwen3-32B\u003c\/strong\u003e bf16 single-GPU on one L40 with 32k ctx headroom (~18-22 tok\/s single-stream on L40, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3.5-27B\u003c\/strong\u003e bf16; \u003cstrong\u003eQwen3-30B-A3B\u003c\/strong\u003e \/ \u003cstrong\u003eQwen3-Coder-30B-A3B\u003c\/strong\u003e bf16 (~60 GB) 256k ctx\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwen3.5-122B-A10B\u003c\/strong\u003e Q4 (~70 GB) — MoE flagship, long ctx\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQwQ-32B\u003c\/strong\u003e bf16; \u003cstrong\u003eHunyuan-A13B\u003c\/strong\u003e Q6 (~48 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek-R2\u003c\/strong\u003e 32B sparse MoE bf16 — single-GPU capable, two parallel streams\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGLM-4.5-Air\u003c\/strong\u003e 106B\/12B Q4-Q5 (60-70 GB comfortable)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSeed-OSS-36B\u003c\/strong\u003e bf16 — 512k native ctx; \u003cstrong\u003eERNIE-4.5-47B-A3B\u003c\/strong\u003e Q6-Q8\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBaichuan-M2-32B\u003c\/strong\u003e bf16 (medical reasoning — ECC advantage here)\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 Q6 (~58 GB) with KV headroom; Q4_K_M (~43 GB) very long ctx (~15-18 tok\/s single-stream on 2x L40, published reference)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHermes 3 70B \/ Tulu 3 70B\u003c\/strong\u003e Q4-Q6; \u003cstrong\u003eLlama 4 Scout\u003c\/strong\u003e 109B\/17B MoE Q4 (~63 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral Small 3 \/ Magistral Small 1.2 \/ Devstral Small 2\u003c\/strong\u003e (24B) bf16; \u003cstrong\u003eMixtral 8x22B\u003c\/strong\u003e Q3-Q4\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003egpt-oss-120b\u003c\/strong\u003e MXFP4 (~80 GB) with KV room\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGemma 3 27B\u003c\/strong\u003e multimodal bf16 with 128k ctx\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePhi-4 14B\u003c\/strong\u003e \/ \u003cstrong\u003ePhi-4-reasoning\u003c\/strong\u003e \/ \u003cstrong\u003ePhi-4-multimodal\u003c\/strong\u003e bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNemotron-Super 49B\u003c\/strong\u003e Q6-Q8; \u003cstrong\u003eIBM Granite 4.0 H-Small\u003c\/strong\u003e 32B\/9B — enterprise compliance\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReka Flash 3\u003c\/strong\u003e 21B bf16; \u003cstrong\u003eOLMo 2 32B\u003c\/strong\u003e \/ \u003cstrong\u003eOLMo 3.1-32B-Think\u003c\/strong\u003e bf16\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, Qwen3-Omni-30B-A3B; InternVL3 up to 78B Q4 (~48 GB); InternVL3.5-38B bf16; DeepSeek-VL2; ERNIE-4.5-VL-28B-A3B-Thinking; Llama 3.2 11B Vision bf16; Pixtral 12B bf16; Gemma 3 12B \/ 27B multimodal; PaliGemma 2 (3\/10\/28B); MiniCPM-V 2.6 \/ MiniCPM-o 2.6; GLM-4.6V-Flash; Molmo 72B Q4; Aya Vision 32B.\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\"\u003eL40 has Ada tensor cores and 864 GB\/s memory bandwidth per card — solid for production image pipelines: FLUX.1 [dev] \/ [schnell] fp16 (~24 GB) or fp8 (~12 GB) (~15-25 seconds per 1024x1024 image at fp8, published reference); FLUX.1 Kontext [dev]; FLUX Tools (Fill \/ Depth \/ Canny \/ Redux); SD 3.5 Large (18 GB fp16 \/ 11 GB fp8); SDXL 1.0 + ControlNet + AnimateDiff; HunyuanImage-2.1 bf16 (~34 GB); 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\"\u003eHunyuanVideo 13B bf16 fits on one L40 at 720p short clip; Wan 2.2 T2V-A14B \/ I2V-A14B bf16 (~54 GB) tensor-parallel 2-way; Wan 2.2 TI2V-5B bf16 per card; Wan 2.1 14B fp8 \/ bf16; HunyuanVideo 1.5 (8.3B) bf16; Open-Sora 2.0 (11B) bf16; CogVideoX-5B \/ 1.5 bf16; Mochi-1 bf16 (~42 GB); LTX-Video 2B; 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 on single GPU, published reference); Parakeet-TDT 1.1B; Canary 1B; Qwen3-ASR; SenseVoice\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTTS:\u003c\/strong\u003e CosyVoice 2 \/ Fun-CosyVoice 3.0; Kokoro 82M; Stable Audio Open; Coqui XTTS v2; StyleTTS 2; Step-Audio-EditX\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRealtime \/ S2S:\u003c\/strong\u003e Kyutai Moshi (200 ms latency full-duplex); Step-Audio 2 mini \/ R1 \/ R1.1; Qwen2.5-Omni-7B\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMusic \/ SFX \/ translation:\u003c\/strong\u003e MusicGen; AudioGen; Suno Bark; SeamlessM4T v2; MMS\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\u003e4-8 concurrent users on 32-70B class LLMs via vLLM tensor-parallel or per-card partition\u003c\/li\u003e\n\u003cli\u003eMixed stack: Qwen3-32B + FLUX.1 + Whisper-turbo + Moshi resident with partitioned VRAM\u003c\/li\u003e\n\u003cli\u003eLoRA inference + light fine-tuning of 7-14B; full-param possible on smaller models\u003c\/li\u003e\n\u003cli\u003eRAG pipelines with Command R \/ Qwen3 + BGE-M3 \/ E5 \/ Jina embeddings\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\u003eEnterprise 24\/7 LLM serving — 70B Q4-Q6, Qwen3-32B bf16, Mistral Small 3 bf16\u003c\/li\u003e\n\u003cli\u003eRegulated deployment requiring ECC memory (finance, healthcare, formal verification)\u003c\/li\u003e\n\u003cli\u003eLong-context serving — Seed-OSS-36B 512k ctx fits comfortably on the 96 GB pool\u003c\/li\u003e\n\u003cli\u003eMid-tier MoE serving — Hunyuan-A13B Q6, GLM-4.5-Air Q4, Qwen3-30B-A3B bf16\u003c\/li\u003e\n\u003cli\u003eVLM document processing — InternVL3.5-38B, Pixtral 12B bf16, Qwen3-VL-32B\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\"\u003ePublished reference | 2x NVIDIA L40 comparable 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\"\u003eLlama 3.3 70B Q4_K_M across 2x L40 tensor-split\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~15-18 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\"\u003eQwen3-32B bf16 single-GPU on one L40\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~18-22 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\"\u003evLLM Hunyuan-A13B Q6 on 2x L40 pool\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e~28-34 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\"\u003eHunyuanVideo 13B bf16 on one L40\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e720p short clip — fits in 48 GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003ePer-card metrics\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e362 TOPS INT8, 864 GB\/s, 300 W TDP\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, not measured on Kentino hardware.\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\u003eCost-per-TFLOPS optimization — 4x RTX 4090 gives 2 644 aggregate TOPS at ~40 % of the component cost (without ECC \/ datacenter warranty)\u003c\/li\u003e\n\u003cli\u003eFrontier 200B+ dense models — 96 GB pool ceiling applies (need 192+ GB SKU)\u003c\/li\u003e\n\u003cli\u003eVideo generation at bf16 long-form full-resolution (Wan 2.2 MoE two-expert wants more VRAM)\u003c\/li\u003e\n\u003cli\u003eTraining from scratch — L40 is inference-certified; use RTX Pro 6000 \/ workstation Blackwell for training\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 datacenter warranty on L40 + Kentino integration warranty (2 years parts, 1 year labor). Build includes assembly, BIOS configuration, driver install, burn-in testing, and functional verification.\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 to 4x L40 (K-AI 192 Rome L40 1448TOPS) for 192 GB ECC pool and frontier-tier serving\u003c\/li\u003e\n\u003cli\u003eUpgrade RAM to 512 GB (add 4x 64 GB DDR4) for larger embedding \/ reranker stacks\u003c\/li\u003e\n\u003cli\u003eUpgrade NVMe to 4 TB for model library + dataset staging\u003c\/li\u003e\n\u003cli\u003eRedundant PSU upsell (dual 2 kW synced) available on request\u003c\/li\u003e\n\u003cli\u003eRack PDU + 3 kVA online UPS for production colo\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c\/div\u003e","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52928513704264,"sku":null,"price":23144.0,"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\/uk\/products\/k-ai-96-rome-l40-724tops-2x-nvidia-l40-ecc-production-inference-server","provider":"Kentino","version":"1.0","type":"link"}