Kentino AI 96 Rome RTXPro6000 2000TOPS — Single-Card 96 GB Blackwell Workstation Server
Enterprise-grade platform, assembled and tested in the EU.
Kentino AI 96 Rome RTXPro6000 2000TOPS
96 GB ECC Single-Card Workstation Server
1x RTX Pro 6000 Blackwell | EPYC Milan | 2 000 TOPS INT8
One card, 96 GB ECC VRAM, the entire Blackwell tensor pipeline. 70B dense bf16 on a single GPU — no tensor-parallel overhead.
A 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.
Hardware
| Component | Detail |
|---|---|
| GPU | 1x NVIDIA RTX Pro 6000 Blackwell Workstation 96 GB ECC GDDR7 (600 W, PCIe 5.0 x16) |
| VRAM | 96 GB ECC on a single card — no pooling, no tensor-parallel overhead |
| CPU | AMD EPYC 7643 Milan (48C/96T, 225 W, 128x PCIe 4.0 lanes) |
| Motherboard | ASRock Rack ROMED8-2T (SP3, 7x PCIe 4.0 x16, 8x DDR4 ECC, 2x 10 GbE, IPMI) |
| System RAM | 256 GB DDR4-2666 ECC RDIMM (4x 64 GB) |
| Boot / storage | 2 TB NVMe M.2 (PCIe 4.0 x4) |
| Power supply | 1x 2 kW ATX PSU |
| Chassis | 4U rack-mount (4-slot capacity, 1 populated — room to expand) |
| Cooling | Arctic Freezer 4U-M SP3 tower + 3x 120 mm front intake + 1x 120 mm rear exhaust |
| Network | Onboard dual 10 GbE (Intel X550) |
Power envelope
- GPU draw: 1 x 600 W = 600 W
- System total at full load: ~925 W
- PSU total: 2 000 W — 53.8 % headroom
- Single PSU, simple cabling — generous margin for single-card build
Lane topology
PCIe 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.
What you can run
With 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.
LLMs — text / reasoning / coding
Chinese frontier
- Qwen3 / Qwen3.5 (Alibaba): 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
- DeepSeek: 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
- GLM / Z.ai: GLM-4.5-Air 106B/12B Q4-Q5 (60-70 GB); GLM-4.6V 106B Q4
- Tencent Hunyuan: Hunyuan-A13B 80B/13B MoE Q4-fp8 (~48-80 GB) with 256k ctx and dual-mode reasoning
- ByteDance Seed-OSS-36B bf16 (~72 GB tight) or fp8 (~36 GB) with full 512k native context
- Baidu ERNIE-4.5-47B-A3B Q4-fp8 with long context
Western frontier
- Meta Llama: 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)
- Mistral: 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
- OpenAI (open weights): gpt-oss-20b MXFP4 native (16 GB); gpt-oss-120b MXFP4 native (80 GB) — single-card single-stream
- Google Gemma 3: 27B multimodal bf16 (~54 GB) with 128k ctx; 12B / 4B bf16
- Microsoft Phi-4 14B dense bf16; Phi-4-reasoning; Phi-4-multimodal
- NVIDIA Nemotron: Llama-3.1-Nemotron-Super 49B Q6 (~40 GB); Nemotron-Nano 8B
- Others: IBM Granite 4.0 H-Small 32B/9B; OLMo 2 32B; Reka Flash 3 21B; Falcon H1R 7B; Command R 35B
Vision-Language Models
Qwen3-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.
Image generation
FLUX.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.
Video generation
Wan 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.
Audio / Speech / TTS
- ASR: Whisper v3 large / turbo (~50x realtime); NVIDIA Parakeet-TDT 1.1B; Canary 1B; Qwen3-ASR; SenseVoice
- TTS: CosyVoice 2 / Fun-CosyVoice 3.0; Kokoro 82M; Stable Audio Open; Coqui XTTS v2; StyleTTS 2; Step-Audio-EditX
- Realtime / S2S: Kyutai Moshi (200 ms full-duplex); Step-Audio 2 mini; Step-Audio-R1 / R1.1; Qwen2.5-Omni-7B
- Music / SFX: Meta MusicGen; AudioGen; Suno Bark; SeamlessM4T v2
Multi-model / multi-tenant serving
- Single-tenant streaming coding assistant — 70B dense bf16, low latency, no TP penalty
- Mixed resident stack: Qwen3-32B bf16 + FLUX.1 fp8 + Whisper-turbo + Moshi on one card with partitioned VRAM
- Fine-tuning: LoRA / QLoRA on 13-34B models; full-param on 7B
- Embedding service: BGE-M3 / E5 / Jina resident alongside a generator LLM
Target workloads
- Single-tenant streaming coding assistant running Llama 3.3 70B bf16 or Qwen3-Coder-30B-A3B — no TP coordination overhead
- Developer workstation for a single engineer or tight team needing a 70B-class model with 32-128k context
- Video or image generation lab — HunyuanVideo 13B, Wan 2.2 dual-expert, HunyuanImage-2.1 all at bf16 resident
- VLM / OCR bench — Qwen3-VL-32B bf16 or InternVL3.5-38B with long-document pipelines
- Clean appliance for a small LLM API gateway — one model, one card, easy ops
Measured performance
Published references | NVIDIA RTX Pro 6000 Blackwell datasheet + community benchmarks
| Benchmark | Result |
|---|---|
| Per-card INT8 TOPS (NVIDIA datasheet) | 2 000 TOPS |
| VRAM per card | 96 GB ECC GDDR7 |
| Memory bandwidth | ~1 800 GB/s |
| Llama 3.3 70B Q6 single-GPU (community) | 40-55 tok/s single-stream |
| Llama 3.3 70B bf16 single-GPU (community) | 15-25 tok/s single-stream |
| Blackwell fp8 native | DeepSeek-V3 fp8, Hunyuan-A13B fp8 run without bf16 upcast |
Published external references, not measured on Kentino hardware. Kentino will publish first-party numbers after the first customer build.
Not ideal for
- Training large models from scratch (single GPU — no tensor/pipeline parallelism)
- Frontier 200B+ MoE at real quantizations (Qwen3-235B Q4, GLM-4.5/4.6 — use Kentino AI 192 RTXPro6000 or larger)
- High-concurrency multi-tenant inference (single card caps aggregate throughput; 4x RTX 4090 or 4x L40 scale better)
Warranty and lead time
NVIDIA 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.
Recommended add-ons
- Upgrade RAM to 512 GB (add 4x 64 GB DDR4 — four DIMM slots still open)
- 4 TB NVMe secondary drive for model library / dataset staging
- 24U open cabinet for production rack-mount
- For Gen5 x16 link speed consider the Genoa-platform variant on request
The questions buyers ask us most often before ordering a server.
How long does it take?
Machines built from components we hold ship quickly; anything requiring a specific GPU generation depends on supply. We give you a date before you pay, and if it moves we tell you rather than letting you find out.
Can the configuration be changed before you build it?
Almost always. GPUs, memory, storage and cooling are chosen per order, and the listed configuration is a starting point rather than a fixed package. If you need more VRAM, faster storage or a different cooling approach, say so before you order and we will quote the change.
Can I collect the server in person?
You can. Our warehouse is in Prague, and collection in person is welcome — most people who come use the visit to go through the machine with our engineer and ask the questions that are awkward over email. For orders within the Czech Republic we also try to deliver personally and walk you through the setup on site.
Can I talk to someone who actually understands the workload?
Yes. We have an engineer who works on AI systems specifically, not a general sales desk. If your question is about batch sizes, quantisation, interconnect or where your bottleneck will be, ask it — that conversation usually changes the configuration for the better.
Which model can I run on this configuration?
Yes. Every machine is assembled, burn-in tested and benchmarked on real AI workloads before it ships, and it leaves us with an LLM already installed and running. You plug it in, connect it to your network and start work — the only decision left is which project it runs first.
How do you test a server before shipping?
We run it against actual AI workloads rather than synthetic scores: inference throughput, sustained load behaviour and thermals under continuous operation. You get the benchmark results with the machine, so the performance you were promised is the performance you can verify on day one.
Is the server ready to run when it arrives?
Yes. Every machine is assembled, burn-in tested and benchmarked on real AI workloads before it ships, and it leaves us with an LLM already installed and running. You plug it in, connect it to your network and start work — the only decision left is which project it runs first.
Ships from our EU warehouse. Heavy items may require freight arrangement — contact us for a shipping quote and lead time. 2-year limited warranty with advanced RMA support; extended warranty available.
Not exactly what you need?
Tell us your workload and we'll spec this platform around it — GPUs, memory, storage and cooling matched to what you actually run.