Kentino AI 288 Rome L40 — 6× NVIDIA L40 Serwer Przedsiębiorstw Pasywny (288 GB ECC VRAM)
Enterprise-grade platform, assembled and tested in the EU.
Kentino AI 288 Rome L40 2172TOPS
288 GB ECC VRAM Enterprise Server
6x NVIDIA L40 Passive | EPYC Milan | 2 172 TOPS INT8
Published external references. Not measured on Kentino hardware.
A 4U rack-mount enterprise inference server with six NVIDIA L40 Ada Lovelace passive datacenter cards (48 GB ECC each) pooled to 288 GB ECC VRAM, one AMD EPYC 7643 Milan CPU (48C/96T), 384 GB DDR4-2666 ECC, 2 TB NVMe boot, and dual synchronized 2.5 kW ATX PSU. ECC end-to-end, purpose-built for 24/7 enterprise production where bit-level integrity and serviceable failure domains matter.
Hardware
| Component | Detail |
|---|---|
| GPUs | 6x NVIDIA L40 48 GB ECC (Ada Lovelace, passive datacenter, 300 W, PCIe 4.0 x16, dual-slot, 362 INT8 TOPS/card) |
| VRAM pool | 288 GB aggregate ECC across 6 cards (no NVLink on L40 PCIe SKU) |
| 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 | 384 GB DDR4-2666 ECC RDIMM (6x 64 GB — 2 DIMM slots open for upgrade to 512 GB) |
| Boot / storage | 2 TB NVMe M.2 (PCIe 4.0 x4) |
| Power supply | 2x 2.5 kW ATX with dual-PSU sync cable (5 kW aggregate) |
| Chassis | 4U rack-mount (6-slot layout) |
| Cooling | SP3 tower cooler (Arctic Freezer 4U-M class) + front-to-back directed airflow (industrial fans) |
| Network | Onboard dual 10 GbE (Intel X550) |
Power envelope
- GPU draw: 6 x 300 W = 1 800 W
- System total under full load: ~2 175 W
- PSU total: 5 000 W (dual 2.5 kW synced) — 56.5% headroom
- Dual PSU for split power delivery — single PSU failure = loss of 2 GPUs or 2 GPUs + motherboard
Lane topology
ROMED8-2T exposes 7x PCIe 4.0 x16 direct from EPYC Milan. Six slots populated with passive Gen4 x16 risers — one free slot for NIC / storage. No PCIe switch required. L40 native link is PCIe 4.0 x16 — no bandwidth loss. No NVLink; inter-GPU traffic runs PCIe peer-to-peer.
What you can run
With 288 GB of pooled ECC VRAM across 6 passive L40 cards, this server handles frontier open-weight LLMs at Q4, multi-model concurrent serving, video/media pipelines, and 24/7 enterprise production inference. Note: L40 is Ada Lovelace, not Blackwell — fp8 upcasts to bf16. Use GGUF Q4/Q5 or AWQ/GPTQ int4 for maximum VRAM efficiency.
LLMs — text / reasoning / coding
Chinese frontier
- Qwen3-235B-A22B Q4 (~132 GB) with very long context + generous KV budget (~15-20 tok/s single, published reference)
- GLM-4.5 / 4.6 / 4.7 Q4 (~177 GB) comfortable on 6-way TP (~12-18 tok/s single, published reference)
- Hunyuan-Large 389B/52B Q3 (~160 GB); ERNIE-4.5-424B-A47B Q3 (~180 GB)
- Qwen3-Coder-480B-A35B Q2 (~160 GB) flagship coding agent
- MiniMax-M1 / Text-01 Q3 (~180 GB) 1M-ctx Lightning Attention
- Qwen3-30B-A3B / QwQ-32B / Qwen3-32B — single-card with 6 parallel streams
- DeepSeek-R2 32B sparse MoE — single card per stream, 6 concurrent sessions
Western frontier
- Llama 3.3 70B bf16 (~142 GB) multi-tenant serving (~17 tok/s single, published reference), or Q4 (~43 GB) with 6 concurrent copies
- Llama 4 Scout 109B/17B bf16 (~218 GB tight) or Q4 (~63 GB) comfortable
- Mistral Small 3 / Magistral / Devstral Small (24B) bf16 (~40-50 tok/s single, published reference)
- Pixtral Large / Mistral Large 2 Q6-Q8 (~90-140 GB)
- Llama-3.1-Nemotron Ultra 253B Q4 (~119 GB)
- gpt-oss-120b MXFP4 (~80 GB via GGUF on Ada — note Ada upcast caveat)
- Cohere Command R+ 104B Q4 RAG stack
Vision-Language Models
Qwen3-VL-235B-A22B Q4; Qwen3-VL-32B; InternVL3.5-78B / 241B-A28B Q4 (~135 GB); Llama 3.2 90B Vision bf16 (~180 GB); Pixtral 12B; Molmo 72B; Gemma 3 12B/27B multimodal; GLM-4.6V full (106B bf16); MiniCPM-o 2.6. L40's NVENC/NVDEC is particularly useful for high-throughput VLM document / video pipelines.
Image generation
FLUX.1 [dev] / Kontext / Tools across multiple workers concurrently (~3.5 s per 1024x1024 image on single L40 fp8, published reference) — 6x ComfyUI worker farm possible; SD 3.5 Large; HunyuanImage-2.1 (17B) bf16; HunyuanDiT; Kolors 2.0; AuraFlow; OmniGen.
Video generation
Wan 2.2 T2V-A14B / I2V-A14B dual-expert bf16 (~54 GB, ~20-30 s per 4s clip at 720p, published reference); HunyuanVideo 13B bf16 both experts; Open-Sora 2.0 bf16; CogVideoX-5B; Mochi-1; LTX-Video; Pyramid Flow; NVIDIA Cosmos Predict 2. L40's hardware NVENC/NVDEC handles caption / moderation / transcode at scale alongside generation.
Audio / Speech / TTS
- ASR: Whisper v3 large / turbo; Parakeet-TDT 1.1B; Canary 1B; Qwen3-ASR; SenseVoice
- TTS: CosyVoice 2/3; Kokoro 82M; Stable Audio Open; XTTS v2; Step-Audio-EditX
- Realtime / S2S: Kyutai Moshi; Step-Audio 2 mini / R1; Qwen2.5-Omni-7B
Multi-model / multi-tenant serving
- Multi-model residency — Qwen3-235B Q4 + FLUX.1 + HunyuanVideo + Whisper-turbo + Moshi + embedder, all resident
- 6 concurrent 48 GB-class workloads (one per card): 6x Qwen3-VL-32B, or 6x FLUX.1 workers, or 6x ASR streams
- 6-way tensor-parallel for 200B+ MoE at Q4 with real context
- RAG pipelines — Command R+ / Qwen3 + reranker + embedder + image analysis on same host
Target workloads
- 24/7 production LLM inference backend — 100+ concurrent users on 200B+ MoE at Q4, ECC-protected
- Media-AI pipeline at enterprise scale — caption + moderation + thumbnail + transcode on 6 parallel streams via NVENC/NVDEC
- Multi-tenant SaaS where per-tenant isolation across physical cards matters
- RAG backend with Command R+ reader + reranker + embedder + vision fully resident
- Reliability-first pair replacing the 12x L40 Legacy — two Kentino AI 288 servers = 576 GB aggregate with independent failure domains
Published performance references
External references | Not measured on Kentino hardware
| Benchmark | Result |
|---|---|
| L40 per-card INT8 TOPS | 362 TOPS |
| L40 memory bandwidth | 864 GB/s per card |
| vLLM — Llama 3.3 70B AWQ INT4 on 2x L40 TP (single) | ~25-35 tok/s |
| vLLM — Llama 3.3 70B AWQ INT4 on 2x L40 TP (batch-16) | ~150-200 tok/s aggregate |
| llama.cpp — GLM-4.6 Q4 on 6x L40 (single) | ~12-18 tok/s |
| FLUX.1 [dev] on single L40 fp8 | ~3.5 s per 1024x1024 image |
Kentino will publish first-party numbers after the initial customer build.
Not ideal for
- fp8-native inference at full speed — Ada upcasts to bf16; use GGUF Q4/Q5 or AWQ/GPTQ int4 instead. For fp8 native see Kentino AI 384 Rome RTXPro6000 (Blackwell)
- Training large models from scratch (no NVLink)
- Budget single-user inference — 4x L4 or 4x 5080 is materially cheaper for small workloads
- Frontier 600B+ dense at Q4+ (require 576 GB+ pool — see 6x RTX Pro 6000)
Warranty and lead time
Build includes assembly, BIOS configuration, driver install, burn-in, memtest, and functional verification. Lead time depends on component availability, confirmed at order.
Recommended add-ons
- Upgrade RAM to 512 GB DDR4 (add 2x 64 GB — 2 DIMM slots open) for heavier KV budget
- 4 TB NVMe Gen4 x4 for model library staging
- Full 24U rack cabinet with managed PDU + online UPS (critical for 24/7 ECC workloads)
- Paired second Kentino AI 288 unit — replaces the 12x L40 Legacy envelope with two independent failure domains
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.