{"product_id":"k-ai-96-rome-rtxpro6000-2000tops-single-card-96-gb-blackwell-workstation-server","title":"K-AI 96 Rome RTXPro6000 2000TOPS — Single-Card 96 GB Blackwell Workstation 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 RTXPro6000 2000TOPS\u003c\/p\u003e\n\u003cp style=\"font-size:28px;font-weight:700;margin:0 0 16px 0;line-height:1.3\"\u003e96 GB ECC Single-Card Workstation Server\u003cbr\u003e1x RTX Pro 6000 Blackwell | EPYC Milan | 2 000 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\"\u003e2 000\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\"\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\"\u003esingle\u003c\/div\u003e\n\u003cdiv style=\"font-size:11px;color:#ccc;text-transform:uppercase;letter-spacing:1px\"\u003ecard design\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\"\u003enative Blackwell\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top:20px;font-size:15px;color:#aaa\"\u003eOne card, 96 GB ECC VRAM, the entire Blackwell tensor pipeline. 70B dense bf16 on a single GPU — no tensor-parallel overhead.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cp style=\"font-size:17px;color:#333;margin-bottom:24px\"\u003eA 4U rack-mount workstation server with a single NVIDIA RTX Pro 6000 Blackwell Workstation card (96 GB ECC GDDR7), one AMD EPYC 7643 Milan CPU (48C\/96T), 256 GB DDR4 ECC, 2 TB NVMe boot, and one 2 kW ATX PSU with 54 % headroom. The simplest software path Kentino ships — no tensor-parallel config, no multi-GPU debugging. vLLM, SGLang, llama.cpp, ComfyUI run single-device and just work.\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\"\u003eGPU\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e1x NVIDIA RTX Pro 6000 Blackwell Workstation 96 GB ECC GDDR7 (600 W, PCIe 5.0 x16)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 16px;font-weight:600\"\u003eVRAM\u003c\/td\u003e\n\u003ctd style=\"padding:10px 16px\"\u003e96 GB ECC on a single card — no pooling, no tensor-parallel overhead\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\"\u003e1x 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 (4-slot capacity, 1 populated — room to expand)\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: 1 x 600 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\u003eSingle PSU, simple cabling — generous margin for single-card build\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 the GPU (card is Gen5 native; Rome board caps at Gen4). Direct root-complex connection — no PCIe switch. No NVLink required — single card, no inter-GPU link at all. Six x16 slots remain open 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 96 GB of ECC VRAM on a single Blackwell card, this server handles 70B dense bf16 on one GPU, open-weight LLMs, vision models, image and video generation, speech AI, and production inference — no tensor-parallel coordination needed.\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 Qwen3-32B dense bf16 (~65 GB) with generous KV; Qwen3-72B Q6 (~58 GB, ~25-35 tok\/s single-stream); Qwen3-30B-A3B MoE bf16; Qwen3-Coder-30B-A3B agentic at 256k ctx; Qwen3.5-122B-A10B Q4 (~70 GB) with tight KV; QwQ-32B bf16 reasoning\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeepSeek:\u003c\/strong\u003e DeepSeek-R2 32B sparse MoE bf16 (~64 GB, 92.7 % AIME 2025 single-card); DeepSeek-R1-Distill-Qwen-32B bf16; DeepSeek-V2-Lite 16B full precision\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGLM \/ Z.ai:\u003c\/strong\u003e GLM-4.5-Air 106B\/12B Q4-Q5 (60-70 GB); GLM-4.6V 106B Q4\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTencent Hunyuan:\u003c\/strong\u003e Hunyuan-A13B 80B\/13B MoE Q4-fp8 (~48-80 GB) with 256k ctx and dual-mode reasoning\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eByteDance Seed-OSS-36B\u003c\/strong\u003e bf16 (~72 GB tight) or fp8 (~36 GB) with full 512k native context\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBaidu ERNIE-4.5-47B-A3B\u003c\/strong\u003e Q4-fp8 with long context\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 at bf16 (~70 GB) on a single card with 8-16k ctx — the hero config; Llama 3.3 70B Q6 (~58 GB, ~35-50 tok\/s single-stream); Llama 3.1 8B bf16 (~80-120 tok\/s); Llama 3.2 90B Vision Q4 (~52 GB); Llama 4 Scout 109B\/17B MoE Q4 (~63 GB)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMistral:\u003c\/strong\u003e Mistral Small 3 \/ Magistral Small 1.2 \/ Devstral Small 2 (24B) all at bf16 with 256k ctx; Mixtral 8x7B Q6; Codestral Mamba 7B; Pixtral 12B bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOpenAI (open weights):\u003c\/strong\u003e gpt-oss-20b MXFP4 native (16 GB); gpt-oss-120b MXFP4 native (80 GB) — single-card single-stream\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGoogle Gemma 3:\u003c\/strong\u003e 27B multimodal bf16 (~54 GB) with 128k ctx; 12B \/ 4B bf16\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMicrosoft Phi-4\u003c\/strong\u003e 14B dense bf16; Phi-4-reasoning; Phi-4-multimodal\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNVIDIA Nemotron:\u003c\/strong\u003e Llama-3.1-Nemotron-Super 49B Q6 (~40 GB); Nemotron-Nano 8B\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOthers:\u003c\/strong\u003e IBM Granite 4.0 H-Small 32B\/9B; OLMo 2 32B; Reka Flash 3 21B; Falcon H1R 7B; Command R 35B\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 bf16, Qwen3-VL-30B-A3B MoE bf16, Qwen3-Omni-30B-A3B; InternVL3 up to 78B Q4 (~48 GB); InternVL3.5-38B bf16; DeepSeek-VL2 full range; Llama 3.2 11B Vision bf16; Llama 3.2 90B Vision Q4 (~52 GB); Pixtral 12B bf16; Molmo 72B Q4; Molmo 7B bf16; Gemma 3 12B \/ 27B multimodal; PaliGemma 2 28B; Phi-3.5-Vision; Aya Vision 8B \/ 32B; MiniCPM-V 2.6 \/ MiniCPM-o 2.6; GLM-4.6V.\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 (~24 GB) and quantized (~15-25 s\/image at fp8); FLUX.1 Kontext [dev] in-context editing; FLUX Tools (Fill \/ Depth \/ Canny \/ Redux); SD 3.5 Large bf16 (~18 GB); SDXL 1.0; HunyuanImage-2.1 bf16 (~34 GB) at 2K native; HunyuanDiT 1.5B; Kolors \/ 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\"\u003eWan 2.2 T2V-A14B \/ I2V-A14B MoE bf16 (~54 GB, both experts resident); Wan 2.2 TI2V-5B fast path; HunyuanVideo 13B bf16 (~60-80 GB, tight at 720p); HunyuanVideo 1.5 (8.3B); CogVideoX-5B; Open-Sora 2.0 (11B) bf16; Genmo Mochi-1 bf16 (~42 GB); 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); NVIDIA 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 full-duplex); Step-Audio 2 mini; Step-Audio-R1 \/ R1.1; Qwen2.5-Omni-7B\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMusic \/ SFX:\u003c\/strong\u003e Meta MusicGen; AudioGen; Suno 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\u003eSingle-tenant streaming coding assistant — 70B dense bf16, low latency, no TP penalty\u003c\/li\u003e\n\u003cli\u003eMixed resident stack: Qwen3-32B bf16 + FLUX.1 fp8 + Whisper-turbo + Moshi on one card with partitioned VRAM\u003c\/li\u003e\n\u003cli\u003eFine-tuning: LoRA \/ QLoRA on 13-34B models; full-param on 7B\u003c\/li\u003e\n\u003cli\u003eEmbedding service: BGE-M3 \/ E5 \/ Jina resident alongside a generator LLM\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\u003eSingle-tenant streaming coding assistant running Llama 3.3 70B bf16 or Qwen3-Coder-30B-A3B — no TP coordination overhead\u003c\/li\u003e\n\u003cli\u003eDeveloper workstation for a single engineer or tight team needing a 70B-class model with 32-128k context\u003c\/li\u003e\n\u003cli\u003eVideo or image generation lab — HunyuanVideo 13B, Wan 2.2 dual-expert, HunyuanImage-2.1 all at bf16 resident\u003c\/li\u003e\n\u003cli\u003eVLM \/ OCR bench — Qwen3-VL-32B bf16 or InternVL3.5-38B with long-document pipelines\u003c\/li\u003e\n\u003cli\u003eClean appliance for a small LLM API gateway — one model, one card, easy ops\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 RTX Pro 6000 Blackwell 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\"\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\"\u003eVRAM per card\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fab400;font-weight:700\"\u003e96 GB ECC GDDR7\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"border-bottom:1px solid #222\"\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e~1 800 GB\/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 3.3 70B Q6 single-GPU (community)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e40-55 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\"\u003eLlama 3.3 70B bf16 single-GPU (community)\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003e15-25 tok\/s single-stream\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:10px 12px;color:#ccc\"\u003eBlackwell fp8 native\u003c\/td\u003e\n\u003ctd style=\"padding:10px 12px;color:#fff\"\u003eDeepSeek-V3 fp8, Hunyuan-A13B fp8 run without bf16 upcast\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 (single GPU — no tensor\/pipeline parallelism)\u003c\/li\u003e\n\u003cli\u003eFrontier 200B+ MoE at real quantizations (Qwen3-235B Q4, GLM-4.5\/4.6 — use K-AI 192 RTXPro6000 or larger)\u003c\/li\u003e\n\u003cli\u003eHigh-concurrency multi-tenant inference (single card caps aggregate throughput; 4x RTX 4090 or 4x L40 scale better)\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 RTX Pro 6000 + 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 secondary drive for model library \/ dataset staging\u003c\/li\u003e\n\u003cli\u003e24U open cabinet for production rack-mount\u003c\/li\u003e\n\u003cli\u003eFor Gen5 x16 link speed consider the Genoa-platform variant on request\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c\/div\u003e","brand":"Kentino s.r.o.","offers":[{"title":"Default Title","offer_id":52940156698952,"sku":null,"price":15847.0,"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\/no\/products\/k-ai-96-rome-rtxpro6000-2000tops-single-card-96-gb-blackwell-workstation-server","provider":"Kentino","version":"1.0","type":"link"}