Kentino AI 768 TurinDual RTXPro6000MQ 16000TOPS — 8× RTX Pro 6000 Blackwell Max-Q AI Frontier-Server (Dual Turin)
Enterprise-grade platform, assembled and tested in the EU.
Kentino AI 768 TurinDual RTXPro6000MQ 16000TOPS
768 GB ECC VRAM Frontier Flagship
8x RTX Pro 6000 Max-Q | Dual EPYC Turin | 16 000 TOPS INT8
CPU pricing finalized at order — Turin 9005-series market moves weekly in Q2 2026.
Published external references. Not measured on Kentino hardware.
Top of the Kentino AI server lineup. A 7U rack-mount flagship frontier-tier inference platform with eight NVIDIA RTX Pro 6000 Blackwell Max-Q turbofan cards pooled to 768 GB ECC VRAM, two AMD EPYC Turin 9005-series CPUs (Zen5c, SP5), 1.5 TB DDR5-4800 ECC (all 24 channels populated), 4 TB NVMe boot, and 5x 1200 W server PSU. PCIe Gen5 end-to-end. DeepSeek V3 fp8 native (~670 GB) on-card. Kimi-K2 Q4-Q5. 4 frontier-class models resident simultaneously.
Hardware
| Component | Detail |
|---|---|
| GPUs | 8x NVIDIA RTX Pro 6000 Blackwell Max-Q 96 GB ECC (turbofan, 600 W TDP spec, PCIe 5.0 x16, 2000 INT8 TOPS/card, fp8 native) |
| VRAM pool | 768 GB total across 8 cards (no NVLink — P2P over PCIe Gen5 at ~55-60 GB/s within socket, cross-socket via CPU interconnect) |
| CPU | 2x AMD EPYC Turin 9005-series (Zen5c, SP5, PCIe 5.0) — quote-pending, exact SKU confirmed at order |
| Motherboard | ASRock Rack TURIN2D24XGM/500W (dual SP5 Turin, PCIe 5.0, 24x DDR5, 2x 10 GbE, IPMI) |
| System RAM | 1.5 TB DDR5-4800 ECC RDIMM (24x 64 GB — all 24 channels populated, ~920 GB/s aggregate) |
| Boot / storage | 4 TB NVMe M.2 (PCIe 4.0 x4) — sized for frontier checkpoints |
| Power supply | 5x 1200 W server PSU set (6 kW total) |
| Chassis | 7U 8-GPU rack-mount, 10 PCIe slot capacity, active Gen5 risers |
| Cooling | 2x SP5 Turin tower coolers + 8x 120 mm Martech chassis fans. Per-GPU turbofan blowers self-contained. |
| Network | Onboard dual 10 GbE (Intel X550) |
Power envelope
- GPU draw (spec): 8 x 600 W = 4 800 W
- CPU draw: 2 x 360 W = 720 W (Turin mid-tier estimate)
- System total at spec full load: ~5 720 W
- PSU total: 6 000 W — ~4.7% raw headroom at spec
- Real-world: Max-Q sustains 520-550 W in inference, lifting sustained headroom to ~20%+
- Firmware power-cap at 520 W available for guaranteed headroom
Lane topology
Dual Turin provides 2x 128 PCIe Gen5 lanes. TURIN2D24XGM/500W routes 8 GPU slots direct-attached to the CPUs at Gen5 x16 via active risers — 4 slots per CPU root. No PCIe switch in the GPU path — clean dual-root topology. NUMA tuning required for optimal cross-socket peer-to-peer. No NVLink; P2P at ~55-60 GB/s per direction within socket.
What you can run
With 768 GB of pooled ECC VRAM — the top of the Kentino envelope — this server runs DeepSeek V3 fp8 native (~670 GB) on-card, Kimi-K2 Q4-Q5 (~630 GB) comfortable, and the defining use case: 4 frontier-class models resident simultaneously for multi-tenant production serving.
LLMs — text / reasoning / coding
Chinese frontier at production quants
- Kimi-K2 (Base / Instruct / Thinking) at Q4_K_M / Q5_K_M (~630 GB) comfortable (~15-25 tok/s single, published reference) — flagship Chinese frontier on a single box at production quants
- DeepSeek V3 / R1 / V3.1 / V3.2 at fp8 native (~670 GB) on-card (~30-50 tok/s single, published reference) — Blackwell fp8 tensor cores run this natively at speed
- DeepSeek V3 at Q4_K_M (~404 GB) with multiple concurrent large-batch serving instances
- GLM-5 / GLM-5.1 (~745B/44B) at Q3-Q4 (~420-560 GB) comfortable on-card
- Intern-S1-Pro (1T/22B active, SAGE) at Q3-Q4 (~440-580 GB) comfortable
- Qwen3-Coder-480B-A35B at Q5-Q6 (~340-400 GB) with 1M ctx
- Qwen3-235B-A22B at bf16 (~470 GB) with generous KV for long context
- ERNIE-4.5-424B-A47B at Q6 (~360 GB); Hunyuan-Large at fp8 (~390 GB)
- MiniMax-Text-01 / M1 at Q5-Q6 (~325-390 GB)
Western frontier at production quants
- Mistral Large 3 (675B/41B MoE, Apache 2.0) at Q3-Q4 (~317-404 GB) comfortable (~20-30 tok/s single, published reference)
- Llama 4 Maverick (400B/17B, 128 experts) at Q5-Q6 (~290-350 GB)
- Llama-3.1-Nemotron Ultra 253B at bf16 (~506 GB) on-card
- Snowflake Arctic at Q5-Q6 (~350-420 GB); Grok-1 at Q5-Q6 (~225-270 GB)
- DBRX Instruct 132B/36B at bf16 (~264 GB) multi-instance
Vision-Language Models
Qwen3-VL-235B-A22B flagship VLM with long context; InternVL3.5-241B-A28B at bf16 (~482 GB); GLM-4.5V / 4.6V 106B bf16 multi-instance; Llama 3.2 90B Vision bf16 multi-instance; Pixtral Large 124B bf16; Molmo 72B bf16 multi-instance.
Image generation
HunyuanImage-3.0 Instruct concurrent instances; FLUX.1 multi-instance (~15-20 s per 1024x1024 image, published reference); SD 3.5 Large; SDXL; AuraFlow; OmniGen; HunyuanImage-2.1; Kolors 2.0 — full Chinese + Western image stack resident concurrent.
Video generation
Wan 2.2 T2V-A14B / I2V-A14B — many concurrent streams; HunyuanVideo 13B bf16 multiple concurrent streams; Open-Sora 2.0 (11B) multi-instance; Mochi-1 (10B) multi-instance; NVIDIA Cosmos Predict 2 up to 14B.
Audio / Speech / TTS
Full stack resident at batch: Whisper v3 large, Parakeet-TDT, Canary 1B, Moshi 7B realtime, Qwen3-Omni, Step-Audio R1, CosyVoice 3.0, Kokoro, Stable Audio Open.
Multi-model / multi-tenant serving (the defining use case)
- Multi-tenant frontier production: 4 frontier-class models resident simultaneously — e.g. DeepSeek V3 fp8 + Kimi-K2 Q4 + Mistral Large 3 Q3 + Qwen3-Coder-480B Q5 — with partitioned VRAM and per-tenant SLOs
- Concurrent fp8-native Blackwell inference (DeepSeek V3 / R1 family, Hunyuan fp8) + quantized serving on separate PCIe domains
- Research A/B across 4-5 frontier open-weight models at research-grade quants
- Agentic platform with a 400B+ primary + multiple 30-70B specialists resident
Target workloads
- Multi-tenant frontier open-weight production — multiple frontier models resident concurrently with per-tenant isolation
- Sovereign frontier AI deployment — on-prem DeepSeek V3 fp8 / Kimi-K2 / Mistral Large 3 access, EU data residency
- Frontier research lab with A/B evaluation across 4+ frontier open-weight models at research-grade quants
- Enterprise agentic platform where 400B+ MoE drives tools + multiple specialist models
- Air-gapped regulated-industry inference at frontier scale with ECC + PCIe Gen5
Published performance references
External references | Not measured on Kentino hardware
| Benchmark | Result |
|---|---|
| RTX Pro 6000 per-card INT8 TOPS | 2 000 TOPS |
| vLLM — DeepSeek V3 fp8 on 8x RTX Pro 6000 (single) | ~30-50 tok/s |
| vLLM — DeepSeek V3 fp8 on 8x RTX Pro 6000 (batch-32) | 300-500 tok/s aggregate |
| Kimi-K2 Q4 serving on 8x RTX Pro 6000 (single) | ~15-25 tok/s |
| 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
- Budget-conscious deployments — flagship SKU at flagship price
- Training from scratch on frontier-class models — no NVLink, PCIe P2P only (for training at this scale H100/H200 SXM or GB200 NVLink fabric is the right tool)
- Plug-and-play deployment — frontier multi-tenant MoE serving requires a skilled MLOps team
Warranty and lead time
Build includes assembly, BIOS config, driver install, burn-in, memtest, functional verification, NUMA tuning, and LLM environment setup (vLLM / SGLang / llama.cpp / CUDA 13 stack with fp8 Blackwell kernels). Lead time depends on component availability, confirmed at order.
Recommended add-ons
- NVIDIA ConnectX-5 MCX555A-ECAT or ConnectX-7 Gen5 100 GbE NIC for multi-node scale-out
- Mellanox ConnectX-6 25 GbE SFP28 for datacenter fabric
- Second 4 TB NVMe for dataset / model library (frontier checkpoints are large — Kimi-K2 bf16 alone is ~1 TB)
- Full 24U rack cabinet with front perforated door and managed PDU
- Online UPS 10 kVA (graceful shutdown on power event)
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.