Kentino AI 192 Turin2U RTXPro6000 4000TOPS — 2× RTX Pro 6000 Blackwell Server Edition — 2U Turin SP5
Enterprise-grade platform, assembled and tested in the EU.
Kentino AI 192 Turin2U RTXPro6000 4000TOPS
192 GB ECC Blackwell Flagship Pair
2x RTX Pro 6000 Server Edition | EPYC Turin SP5 | 4 000 TOPS INT8
Two passive RTX Pro 6000 Blackwell Server Edition cards -- 96 GB ECC each. Less tensor-parallel overhead than 4- or 8-card builds. Datacenter flagship pair on a Gen5/DDR5 2U platform with genuine 1+1 redundant power.
A 2U rack-mount inference server with two passive RTX Pro 6000 Blackwell Server Edition cards (96 GB ECC GDDR7 per card), one AMD EPYC 9335 Turin CPU (32C/64T, 3.0/4.4 GHz), 512 GB DDR5-4800 ECC, 5.76 TB datacenter Gen5 NVMe, and a 1+1 redundant 2.7 kW 80+ Platinum CRPS power supply. Starting from €56 600 ex VAT. For 70B dense bf16 and mid-size MoE, fewer big cards beat more small cards -- two-card tensor parallelism has minimal communication overhead, and each 96 GB card carries a complete copy of most models.
The same 192 GB Blackwell pair as our 4U Rome build, in a 2U rack-dense ASRock chassis with full Gen5 host-side, DDR5-4800 memory, and a genuine 1+1 redundant 2.7 kW Platinum CRPS power supply. Pick this build when rack density matters, when your grant or procurement spec mandates a modern PCIe 5.0 / DDR5 platform, or when redundant power is a requirement rather than an upsell.
Hardware
| Component | Detail |
|---|---|
| GPUs | 2x NVIDIA RTX Pro 6000 Blackwell Server Edition 96 GB ECC GDDR7 (passive, 600 W, PCIe 5.0 x16, dual-slot) |
| VRAM pool | 192 GB ECC (96 GB x 2) -- each card holds a 70B bf16 model standalone |
| CPU | AMD EPYC 9335 Turin (32C/64T, 3.0/4.4 GHz, 210 W, SP5, 128x PCIe 5.0 lanes, Zen5c, 256 MB L3) |
| Motherboard | ASRock Rack 2U4G-GENOA/M3 (SP5, 4x PCIe 5.0 x16 dual-slot GPU, 8x DDR5 1DPC, OCP 3.0, IPMI AST2600) |
| System RAM | 512 GB DDR5-4800 ECC RDIMM (8x 64 GB, 1DPC fully populated -- max bandwidth configuration) |
| Boot / storage | Kioxia CD8-P 3.84 TB Gen5 U.3 (hot-tier, 1 DWPD, ~12 GB/s read) + Kioxia CD8-P 1.92 TB Gen5 U.3 (boot OS tier) -- 5.76 TB total datacenter Gen5 NVMe |
| Power supply | 1+1 redundant 2.7 kW 80+ Platinum CRPS (2x 1350 W at 230 V) -- genuine N+1 redundancy; one PSU sustains full inference load |
| Chassis | 2U rack-mount with front-to-back directed airflow (80 mm high-static-pressure fans). 24/7-capable. |
| Cooling | SP5 active CPU heatsink + 3x 80x38 mm front intake + 1x 80x80 mm rear exhaust (designed for 4x passive GPU thermal load; 2-card layout provides ample thermal headroom) |
| Network | Intel X710-T2L PCIe dual 10GBASE-T + OCP 3.0 slot available for 25/100 GbE upgrade |
Power envelope
- GPU draw: 2x 600 W = 1 200 W
- System total at full load: ~1 510 W
- PSU config: 1+1 redundant CRPS, 2x 1350 W at 230 V (2 700 W total)
- Headroom: 44.1 % under typical inference load
- Genuine N+1 redundancy -- one PSU sustains full inference load; no single-PSU failure risk
Lane topology
PCIe Gen5 x16 end-to-end -- both host and card native Gen5. Direct root-complex connection, no PCIe switch. One PCIe 5.0 x16 single-slot + one PCIe 5.0 x8 slot remain available (NIC occupies the x8 slot). No NVLink -- inter-GPU peer-to-peer via PCIe. Gen5 bandwidth eliminates the Gen4 host-cap present in the 4U Rome sibling.
What you can run
With 192 GB ECC VRAM on just two Blackwell cards with native fp8/fp4, this is the cleanest path to dense 70B at bf16 and mid-size MoE. Two independent 70B streams -- one per card -- or 200B MoE across both with minimal 2-way TP overhead.
LLMs -- text / reasoning / coding
Chinese frontier
- Qwen3 / Qwen3.5 (Alibaba): Qwen3-235B-A22B Q4 (~132 GB) comfortable with long ctx (~15-25 tok/s single-stream across 2 cards); Qwen3-Coder-480B-A35B Q2 (~160 GB); Qwen3.5-122B-A10B fp8 (~75 GB); Qwen3-32B dense bf16 with huge KV; QwQ-32B bf16
- DeepSeek: DeepSeek-V3/R1 Q2 (~215 GB with small RAM spill) -- Blackwell runs fp8 natively; DeepSeek-R2 32B bf16 two concurrent streams (one per card)
- GLM / Z.ai: GLM-4.5 / 4.6 / 4.7 Q4 (~177 GB) -- hero config at this tier; GLM-4.5-Air fp8 or bf16 with huge KV
- Tencent Hunyuan: Hunyuan-Large Q3 (~160 GB) -- 389B MoE with 256k ctx; Hunyuan-A13B fp8 native (~80 GB) with huge KV
- Others: Baidu ERNIE-4.5-424B Q3 (~180 GB); InternVL3.5-241B-A28B Q4 (~135 GB); MiniMax-M1 Q3 (~180 GB)
Western frontier
- Meta Llama: Llama 3.3 70B bf16 on one card -- two independent concurrent 70B streams (~20-30 tok/s per stream); Llama 4 Scout bf16 (~218 GB, tight); Llama 4 Maverick Q3 (~188 GB)
- Mistral: Mistral Large 2 / Pixtral Large / Devstral 2 123B Q6 (~88 GB) single-card or bf16 across both; Mistral Small 3 multi-stream
- OpenAI (open weights): gpt-oss-120b MXFP4 native (80 GB) -- fits on ONE card, two independent concurrent streams
- NVIDIA Nemotron: Llama-3.1-Nemotron Ultra 253B Q4 (~147 GB); Super 49B bf16 on single card
- Others: Cohere Command R+ 104B Q6 (~85 GB) on one card; Google Gemma 3 27B bf16 multiple concurrent streams
Vision-Language Models
InternVL3.5-241B-A28B Q4 (~135 GB); Qwen3-VL-235B-A22B Q4; Qwen3-VL-32B bf16 single-card; Pixtral Large 124B bf16 or Q6; Llama 3.2 90B Vision bf16 (~180 GB); Molmo 72B bf16 (~144 GB); GLM-4.6V 106B fp8; Gemma 3 27B multimodal x 2-3 concurrent streams.
Image generation
FLUX.1 [dev] bf16 multiple concurrent streams; FLUX.1 Kontext [dev]; FLUX Tools; SD 3.5 Large bf16 concurrent; HunyuanImage-2.1 bf16 (~34 GB) x 2-4 concurrent; HunyuanImage-3.0 base (80B MoE, 13B active) bf16 -- fits on one card; HunyuanDiT; Kolors / Kolors 2.0; AuraFlow; OmniGen v1; PixArt-Sigma.
Video generation
Wan 2.2 MoE dual-expert bf16 full context -- fits on one card, two concurrent generation streams; Wan 2.2 TI2V-5B; HunyuanVideo 13B bf16 both experts; HunyuanVideo 1.5; CogVideoX-5B bf16; Open-Sora 2.0 11B bf16; 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); Parakeet-TDT; Canary 1B; Qwen3-ASR; SenseVoice
- TTS: CosyVoice 2/3; Kokoro 82M; XTTS v2; Stable Audio Open; Step-Audio-EditX
- Realtime / S2S: Kyutai Moshi 7B; Step-Audio 2 mini/R1; Qwen2.5-Omni-7B
- Music / SFX: MusicGen / AudioGen / Bark; SeamlessM4T v2
Multi-model / multi-tenant serving
- Two independent 70B streams -- one per card, simplest form of tenant isolation
- Dense 70B bf16 + supporting stack -- LLM on card 1, image/video/audio on card 2
- 200B MoE across both cards -- minimal tensor-parallel overhead (2-way split)
- fp8-native frontier -- DeepSeek V3 family, Hunyuan-Large fp8 with Blackwell native paths
Target workloads
- Dense 70B bf16 inference -- two cards tensor-parallel with minimal overhead, or one model per card for streaming
- 100-150B MoE at Q4-Q6 (GLM-4.5-Air, Qwen3.5-122B-A10B, Hunyuan-A13B, Llama 4 Scout)
- FP8-native frontier inference (DeepSeek V3 family, Hunyuan, Llama 4) -- Blackwell runs fp8 natively
- Scientific computation requiring datacenter-grade Gen5 NVMe throughput and ECC memory
- Image + video generation studio at bf16 (Wan 2.2 T2V-A14B, HunyuanVideo 13B, FLUX.1 [dev])
- Rack-density-constrained deployments -- 2U form factor vs the 4U Rome equivalent at same VRAM
- Procurement specs mandating PCIe 5.0 / DDR5 platform or redundant PSU
Measured performance
Published references | NVIDIA RTX Pro 6000 Blackwell Server Edition datasheet + community benchmarks
| Benchmark | Result |
|---|---|
| Per-card INT8 TOPS (NVIDIA datasheet) | 2 000 TOPS |
| Aggregate INT8 TOPS (2 cards) | 4 000 TOPS |
| Memory bandwidth per card | ~1 800 GB/s, 96 GB ECC GDDR7 |
| Llama 3.3 70B bf16 per-card (community) | 15-25 tok/s single-stream, 60-90 tok/s batch -- expected improvement from Gen5 host-side memory path in streaming batch workloads vs Gen4 host |
| Gen5 host-side advantage (single-card same silicon) | PCIe 5.0 x16 end-to-end reduces host-device transfer latency for streaming batch workloads; on-card compute-bound tasks see identical throughput to Gen4-hosted builds |
| Dual-card tensor-parallel 70B (community) | ~30-45 tok/s single-stream expected |
| 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
- Very high concurrency multi-tenant serving -- 4x L40 or 6x L4 distributes better across more cards
- Heavy KV cache at very long context -- step up to Kentino AI 576 Genoa RTXPro6000 12000TOPS
- Training -- Kentino does not sell H-class NVLink fabrics
- Budget inference at this VRAM pool -- the 4U Rome Kentino AI 192 RTXPro6000 4000TOPS build is lower-cost if Gen4 host-side is acceptable and PSU redundancy is not required
Warranty and lead time
NVIDIA OEM 3-year warranty on RTX Pro 6000 Server Edition + 36-month chassis warranty + Kentino integration warranty. Build includes assembly, BIOS/firmware configuration, IPMI setup, driver install, burn-in testing, and functional verification. Lead time of 14-21 business days reflects reseller order for Turin-class components; confirmed at order placement.
Recommended add-ons
- Expand to 4-card configuration -- chassis has 4 GPU bays natively (current build uses 2 of 4), upgrade path to Kentino AI 384 Turin2U RTXPro6000 8000TOPS
- Add 25 GbE or 100 GbE via OCP 3.0 slot (Mellanox ConnectX-5/6 OCP variant)
- Additional Kioxia CD8-P NVMe in the 2 remaining U.2 bays for RAID or scratch storage
- Upgrade storage tier to Samsung PM1743 or Kioxia CM7-V for higher endurance (3 DWPD)
- 24U rack cabinet + online UPS 5 kVA
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.