Serveur Frontier IA Kentino AI 576 Genoa RTXPro6000MQ 12000TOPS — 6× RTX Pro 6000 Max-Q Blackwell
Enterprise-grade platform, assembled and tested in the EU.
Kentino AI 576 Genoa RTXPro6000MQ 12000TOPS
576 GB ECC VRAM Frontier Server
6x RTX Pro 6000 Max-Q Turbofan | EPYC Genoa | 12 000 TOPS INT8
Published external references. Not measured on Kentino hardware.
A 7U rack-mount frontier-tier inference platform with six NVIDIA RTX Pro 6000 Blackwell Max-Q turbofan cards pooled to 576 GB ECC VRAM, one AMD EPYC 9354 Genoa CPU (32C/64T), 768 GB DDR5-4800 ECC (all 12 channels populated), 4 TB NVMe boot, and 5x 1200 W server PSU. Same silicon and memory pool as the passive Server Edition build — different cooler. The Max-Q turbofan is self-contained per card, runs quieter, and tolerates less strict chassis airflow. Identical model envelope to its passive sibling.
Hardware
| Component | Detail |
|---|---|
| GPUs | 6x NVIDIA RTX Pro 6000 Blackwell Max-Q 96 GB ECC (turbofan blower, 600 W TDP spec, PCIe 5.0 x16, 2000 INT8 TOPS per card) |
| VRAM pool | 576 GB total across 6 cards (no NVLink — P2P over PCIe Gen5 at ~55-60 GB/s per direction) |
| CPU | AMD EPYC 9354 Genoa (32C/64T, 280 W, 128x PCIe 5.0 lanes, 12-channel DDR5) |
| Motherboard | ASRock Rack GENOAD8X-2T/BCM (SP5 Genoa, integrated Broadcom PEX PCIe Gen5 switch, 12x DDR5, 2x 10 GbE, IPMI) |
| System RAM | 768 GB DDR5-4800 ECC RDIMM (12x 64 GB — all channels populated, ~460 GB/s aggregate) |
| Boot / storage | 4 TB NVMe M.2 (PCIe 4.0 x4) — sized for frontier checkpoint staging |
| Power supply | 5x 1200 W server PSU set (HP-compatible, 6 kW total) |
| Chassis | 7U 8-GPU rack-mount, 10 PCIe slot capacity, active Gen5 risers |
| Cooling | SP5 Genoa tower cooler + 8x 120 mm chassis fans. Per-GPU turbofan blowers are self-contained — datacenter airflow recommended but not strictly required. Quieter for lab environments. |
| Network | Onboard dual 10 GbE (Intel X550) |
Power envelope
- GPU draw (spec): 6 x 600 W = 3 600 W
- System total at spec full load: ~4 080 W
- PSU total: 6 000 W (5x 1200 W) — 32% headroom
- Max-Q cards typically run 520-550 W sustained — real-world headroom above 20%
Cooling (Max-Q differentiator)
Each card pulls air front-to-back via its own blower — self-contained per card. Tolerates mixed-rack / open-cabinet deployment. Quieter than an equivalent axial-fan stack. Max-Q firmware profile favours lower sustained power (520-550 W typical in inference). Recommended: cabinet with front perforated door and clear rear exhaust path.
What you can run
Identical to the Server Edition sibling — same silicon, same 576 GB pool. DeepSeek V3 Q4 (~404 GB) with long context, Kimi-K2 Q2, Mistral Large 3 Q2-Q3, GLM-5 Q2, Qwen3-Coder-480B Q4.
LLMs — text / reasoning / coding
Chinese frontier
- DeepSeek V3 / R1 / V3.1 / V3.2 at Q4_K_M (~404 GB) comfortable with long context (~5-8 tok/s single vLLM TP-6, published reference); fp8 native (~670 GB) with RAM spill
- Kimi-K2 (Base / Instruct / Thinking) at Q2_K (~375 GB) comfortable (~5-8 tok/s single, published reference)
- GLM-5 / GLM-5.1 (~745B/44B) at Q2_K (~260 GB); Q3 (~420 GB) with RAM spill
- Qwen3-Coder-480B-A35B at Q4_K_M (~270 GB) with long context
- Qwen3-235B-A22B at bf16 (~470 GB) or fp8 (~240 GB)
- ERNIE-4.5-424B-A47B at Q4 (~240 GB) with 128k ctx
- Intern-S1-Pro at Q2_K (~325 GB); Hunyuan-Large at Q4 (~220 GB)
- MiniMax-Text-01 / M1 at Q4 (~260 GB)
Western frontier
- Mistral Large 3 at Q2-Q3 (~243-317 GB) comfortable (~20-30 tok/s single, published reference)
- Llama 4 Maverick at Q4_K_M (~232 GB) with long ctx (~45-55 tok/s single, published reference)
- Llama-3.1-Nemotron Ultra 253B at fp8 (~253 GB)
- Grok-1 314B at Q4 (~182 GB); Snowflake Arctic at Q4 (~278 GB)
- DBRX Instruct 132B/36B at bf16 (~264 GB) or fp8
Vision-Language Models
Qwen3-VL-235B-A22B; InternVL3.5-241B-A28B Q4; GLM-4.5V / 4.6V 106B bf16; Llama 3.2 90B Vision bf16; Pixtral Large 124B fp8; Molmo 72B bf16.
Image generation
HunyuanImage-3.0 Instruct; FLUX.1 [dev] / [schnell] / Kontext multi-instance (~15-20 s per 1024x1024 image, published reference); SD 3.5 Large; SDXL; AuraFlow; OmniGen; HunyuanImage-2.1; Kolors 2.0.
Video generation
Wan 2.2 T2V-A14B dual-expert MoE bf16; HunyuanVideo 13B bf16; Open-Sora 2.0 (11B); Mochi-1 (10B); NVIDIA Cosmos Predict 2 up to 14B; CogVideoX-5B; LTX-Video; Pyramid Flow.
Audio / Speech / TTS
Full stack resident: Whisper v3 large, Parakeet-TDT 1.1B, Canary 1B, Moshi 7B realtime, Qwen3-Omni, Step-Audio R1, CosyVoice 3.0, Kokoro, Stable Audio Open.
Multi-model / multi-tenant serving
- DeepSeek V3 Q4 + FLUX + HunyuanVideo + Whisper/Moshi realtime all resident
- Concurrent 70B tensor-parallel + 235B-MoE on separate PCIe domains
- 3 frontier models resident for A/B evaluation
Target workloads
- Frontier open-weight research lab with mixed / non-ideal airflow infra
- Colocation / private-datacenter where per-card turbofan is operationally simpler than full passive airflow
- Sovereign AI deployment with Apache 2.0 / MIT model stack
- Enterprise multi-model RAG + agentic platform
- Lab environments with open racks
Published performance references
External references | Same silicon as Server Edition | Not measured on Kentino hardware
| Benchmark | Result |
|---|---|
| RTX Pro 6000 per-card INT8 TOPS | 2 000 TOPS |
| vLLM — DeepSeek V3 Q4 on 6x RTX Pro 6000 (single) | ~25-40 tok/s |
| vLLM — DeepSeek V3 Q4 on 6x RTX Pro 6000 (batch-32) | 200-400 tok/s aggregate |
| FLUX.1 [dev] fp8 on single RTX Pro 6000 | ~15-20 s per 1024x1024 image |
Exact figures confirmed at PoC stage. Kentino will publish first-party numbers after initial customer build.
Not ideal for
- Kimi-K2 / DeepSeek V3 at Q4 real-speed production serving — step up to Kentino AI 768 TurinDual RTXPro6000MQ
- Training from scratch on frontier-class models — no NVLink
- Plug-and-play deployment — frontier MoE serving needs a skilled MLOps team
Warranty and lead time
Build includes assembly, BIOS config, driver install, burn-in, memtest, functional verification, and LLM environment setup. Lead time depends on component availability, confirmed at order.
Recommended add-ons
- NVIDIA ConnectX-5 MCX555A-ECAT 100 GbE NIC for multi-node scale-out
- Second 4 TB NVMe for dataset / model library
- Full 24U rack cabinet with front perforated door
- Online UPS 10 kVA
- Managed PDU
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.