Kentino AI 192 Rome L40 1448TOPS — 4× NVIDIA L40 — EPYC Milan
Enterprise-grade platform, assembled and tested in the EU.
Kentino AI 192 Rome L40 1448TOPS
192 GB ECC Enterprise Inference Server
4x NVIDIA L40 Passive | EPYC Milan | 1 448 TOPS INT8
Four passive L40 datacenter cards with ECC memory. Same 192 GB pool as 8x RTX 4090 — but datacenter-grade, ECC-protected, and OEM-warrantied.
A 4U rack-mount inference server with four passive NVIDIA L40 cards pooled to 192 GB ECC VRAM, one AMD EPYC 7643 Milan CPU (48C/96T), 256 GB DDR4 ECC, 2 TB NVMe boot, and dual synchronized 2 kW ATX PSU. The L40 is the datacenter sibling of the RTX 4090 — passive-cooled, ECC-equipped, NVENC/NVDEC hardware encoders on-die, and NVIDIA OEM 3-year warranty. Runs vLLM, SGLang, llama.cpp, Triton, TensorRT-LLM out of the box.
Hardware
| Component | Detail |
|---|---|
| GPUs | 4x NVIDIA L40 48 GB ECC GDDR6 (Ada Lovelace, passive, 300 W, dual-slot, PCIe 4.0 x16) |
| VRAM pool | 192 GB ECC across 4 cards (no NVLink on L40) |
| 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 | Dual 2 kW ATX PSU with sync cable |
| Chassis | 4U rack-mount with front-to-back directed airflow |
| 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: 4 x 300 W = 1 200 W
- System total at full load: ~1 525 W
- PSU total: 4 000 W (dual 2 kW synced) — 61.9 % headroom
- Dual PSU for split power delivery and N+1 capability
Lane topology
PCIe Gen4 x16 per card (L40 is Gen4 native). Direct root-complex connection from single EPYC — no PCIe switch. No NVLink — inter-GPU traffic runs PCIe peer-to-peer. Three x16 slots remain for NIC / storage expansion.
What you can run
With 192 GB of ECC VRAM across 4 datacenter cards, this server handles 200B+ frontier MoE at Q4, enterprise multi-tenant serving with strict SLA, and 24/7 production inference without ECC-related bit-flip drift.
LLMs — text / reasoning / coding
Chinese frontier
- Qwen3 / Qwen3.5 (Alibaba): Qwen3-235B-A22B Q4 (~132 GB) with long context — the hero config (~12-18 tok/s single-stream across 4x L40); Qwen3-Coder-480B-A35B Q2 (~160 GB, tight); Qwen3.5-122B-A10B fp8 (~75 GB) with huge KV; Qwen3-32B dense bf16 multiple concurrent streams
- DeepSeek: DeepSeek-V3/R1/V3.1/V3.2 Q2 (~215 GB with minor RAM spill); DeepSeek-R2 32B — 4x concurrent streams, one per card
- GLM / Z.ai: GLM-4.5 / 4.6 / 4.7 Q4 (~177 GB) — sweet spot for this tier; GLM-4.5-Air 106B/12B fp8 or bf16
- Tencent Hunyuan: Hunyuan-Large Q3 (~160 GB) — 389B MoE with 256k ctx; Hunyuan-A13B fp8 (~80 GB) with huge KV
- Baidu ERNIE-4.5-424B Q3 (~180 GB); InternVL3.5-241B-A28B Q4 (~135 GB); Qwen3.5-397B Q3 (~170 GB)
Western frontier
- Meta Llama: Llama 3.3 70B bf16 with massive KV (~15-18 tok/s single-stream on 4x L40); Llama 4 Scout bf16 (~218 GB) tight; Llama 4 Maverick 400B/17B Q3 (~188 GB)
- Mistral: Mistral Large 2 / Pixtral Large / Devstral 2 123B Q6 (~102 GB) comfortable; Mistral Small 3 multi-stream
- OpenAI (open weights): gpt-oss-120b MXFP4 (80 GB) with generous KV
- NVIDIA Nemotron: Llama-3.1-Nemotron Ultra 253B Q4 (~147 GB); Super 49B bf16 multiple streams
- Google Gemma 3: 27B multimodal bf16 — multiple resident streams
- Others: Cohere Command R+ 104B Q6 (~85 GB); OLMo 3.1 32B; Reka Flash 3 21B; IBM Granite 4.0 H-Small
Vision-Language Models
InternVL3.5-241B-A28B Q4 (~135 GB); Qwen3-VL-235B-A22B Q4; Qwen3-VL-32B bf16; Llama 3.2 90B Vision bf16 (~180 GB); Pixtral Large 124B Q6-bf16; Molmo 72B bf16; GLM-4.6V 106B fp8; Gemma 3 27B multimodal multiple streams; InternVL3 78B bf16; DeepSeek-VL2 full range.
Image generation
FLUX.1 [dev] / [schnell] bf16 with concurrent generation (~3-4 s per 1024x1024 image on L40); FLUX.1 Kontext [dev]; FLUX Tools; SD 3.5 Large bf16 x 2-3 concurrent; HunyuanImage-2.1 bf16 (~34 GB) multi-stream; HunyuanImage-3.0 base (80B MoE, 13B active) bf16 (~80 GB); HunyuanDiT; Kolors / Kolors 2.0; AuraFlow; OmniGen v1; PixArt-Sigma.
Video generation
Wan 2.2 T2V-A14B / I2V-A14B MoE bf16 dual-expert full-context; Wan 2.2 TI2V-5B fast path; HunyuanVideo 13B bf16 both experts; HunyuanVideo 1.5; CogVideoX-5B bf16; Open-Sora 2.0 11B bf16; Mochi-1 bf16 (~42 GB) multi-stream; 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
- Enterprise production LLM gateway — Qwen3-235B Q4 or GLM-4.5/4.6 Q4 serving 16-32 concurrent users with strict SLA
- Mixed resident stack: 235B MoE + FLUX.1 + Whisper-turbo + Moshi with partitioned VRAM and ECC protection
- Live video + AI pipeline — NVENC/NVDEC hardware encoders stream 6-8 parallel captioning + moderation pipelines
- Multi-tenant RAG — query-side embedder + 70B reader + reranker at sub-second P99 latency
Target workloads
- 24/7 production LLM inference at 192 GB pool (Qwen3-235B Q4, GLM-4.5/4.6/4.7 Q4, Llama 4 Scout bf16)
- Enterprise multi-tenant serving with strict SLA — ECC reliability over long runs
- RAG + vector DB serving with high-quality retrieval models concurrent
- Media / video AI pipelines — NVENC / NVDEC hardware path, VFX rendering, transcribe/translate
- Datacenter silent-operation deployments — passive cards, low acoustic profile near office space
Measured performance
Published references | NVIDIA L40 datasheet + community benchmarks
| Benchmark | Result |
|---|---|
| Per-card INT8 TOPS (NVIDIA datasheet) | 362 TOPS |
| Aggregate INT8 TOPS (4 cards) | 1 448 TOPS |
| Per-card VRAM | 48 GB ECC GDDR6, 864 GB/s bandwidth |
| Llama 3.3 70B Q6 via vLLM (community) | 30-50 tok/s single-stream, 150+ tok/s batch-16 |
| FLUX.1 [dev] bf16 on L40 (community) | ~3-4 s per 1024x1024 image |
| NVENC / NVDEC | Gen-8 hardware encoders on-die (video AI pipeline) |
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 (no NVLink, limited FP8 tensor compute)
- Single-user budget inference (4x L4 or 2x L40 is materially cheaper)
- Dense bf16 70B at very long context on one model — prefer 2x RTX Pro 6000 Server Edition (same 192 GB pool, less TP overhead)
Warranty and lead time
NVIDIA OEM 3-year warranty on L40 + 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 for model library staging
- Full 24U rack cabinet with managed PDU + 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.