Kentino AI 64 Rome 5080 3600TOPS — 4x RTX 5080 Budżetowy Serwer AI
Enterprise-grade platform, assembled and tested in the EU.
Kentino AI 64 Rome 5080 3600TOPS
Budget 4-GPU Blackwell Server
4x RTX 5080 | EPYC Milan | 3 600 TOPS INT8
Kentino's budget 4-GPU Blackwell server — 64 GB VRAM pool, 3 600 aggregate TOPS INT8, lowest CZK-per-TOPS in the lineup.
A 4-GPU Blackwell inference server built around the RTX 5080 — 360 W per card, PCIe 5 silicon, 16 GB GDDR7 each. Four cards deliver a 64 GB pooled VRAM envelope and 3 600 INT8 TOPS aggregate at the best CZK-per-TOPS point Kentino offers. The entry into multi-GPU Blackwell inference: ideal for embedding clusters, 7-13B model serving at scale, image / video batch generation, and 70B Q4 tensor-parallel.
Hardware
| Component | Detail |
|---|---|
| GPUs | 4x NVIDIA GeForce RTX 5080 16 GB GDDR7 (360 W, PCIe 5.0 x16) |
| VRAM pool | 64 GB |
| 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 | Single 2 kW ATX PSU |
| Chassis | 4U rack-mount, 4x GPU, passive Gen4 x16 risers, front-to-back directed airflow |
| Cooling | SP3 tower cooler, 3x 120 mm front intake + 1x 120 mm rear exhaust (industrial fans) |
| Network | Onboard dual 10 GbE (Intel X550) + IPMI |
Power envelope
- GPU draw: 4 x 360 W = 1 440 W
- System total at full load: ~1 765 W
- PSU total: 2 000 W (single 2 kW ATX) — 11.75 % headroom
- Above the 10 % floor but tighter than other 4-GPU builds; dual-PSU upgrade recommended for high-duty workloads
Lane topology
ROMED8-2T fans out 4x16 Gen4 from CPU root complex. 5080 is PCIe Gen5 silicon running Gen4 x16 without bandwidth bottleneck for inference. No PCIe switch. No NVLink — tensor parallel over PCIe.
What you can run
With 64 GB of pooled VRAM across 4 Blackwell cards, this server handles 70B Q4 tensor-parallel, embedding clusters at scale, image and video batch pipelines, and 7-13B multi-tenant serving for 64-128 concurrent users.
LLMs — text / reasoning / coding
Chinese frontier
- Qwen3-32B Q8 (dense at near-fp16 quality); Qwen3.5-27B bf16
- Qwen3-30B-A3B / Qwen3-Coder-30B-A3B bf16 (~60 GB fits tight)
- Qwen3.5-122B-A10B Q4 (~70-75 GB — tight, spill to DDR4 RAM)
- Hunyuan-A13B fp8 (~80 GB native — tight, prefer Q6)
- Seed-OSS-36B bf16 (~72 GB tight)
- DeepSeek-R2 32B sparse MoE bf16 (~64 GB) (~45-60 tok/s single-stream at Q4 on Blackwell, published reference)
- GLM-4.5-Air 106B/12B Q3_K (~55 GB) — tight KV headroom
- ERNIE-4.5-47B-A3B Q4 (~28 GB with headroom for second model)
Western frontier
- Llama 3.3 70B Q4_K_M (~43 GB) — the sweet spot for this pool (~30-36 tok/s single-stream on 4x 5080, published reference)
- Hermes 3 70B / Tulu 3 70B Q4 — open Llama derivatives with full post-training transparency
- Mistral Small 3 / Magistral / Devstral Small 2 24B bf16
- Gemma 3 27B bf16 multimodal
- Phi-4 14B / Nemotron-Super 49B Q6-Q8
- gpt-oss-20b MXFP4 (16 GB — 4 instances on 4 cards for parallel tenants); gpt-oss-120b MXFP4 (80 GB — tight; spill manageable)
Vision-Language
Qwen3-VL-32B / Qwen3-VL-30B-A3B / Qwen3-Omni-30B-A3B; InternVL3.5-38B Q6-Q8; Llama 3.2 90B Vision Q4 (~52 GB tight); Pixtral 12B / Pixtral Large 124B Q2-Q3; Gemma 3 27B multimodal bf16; PaliGemma 2 28B bf16; Molmo 72B Q4 (~45 GB); Aya Vision 32B bf16.
Image generation
FLUX.1 [dev] / [schnell] fp16 — batch-4 parallel (~10-15 seconds per 1024x1024 image at fp8 on Blackwell, published reference); FLUX.1 Kontext [dev] — in-context editing across 4 tenants; SD 3.5 Large (18 GB fp16) — 4 parallel generators; SDXL 1.0 + ControlNet + AnimateDiff stacks x 4; HunyuanImage-2.1 bf16 per-card; AuraFlow v0.3 / OmniGen v1 / Kolors 2.0 / PixArt-Sigma.
Video generation
Wan 2.2 TI2V-5B bf16 on a single card — 4 parallel tenants; Wan 2.1 14B T2V/I2V Q4-Q6 per card; HunyuanVideo 13B Q4 (~30 GB) tensor-parallel 2-way; HunyuanVideo 1.5 (8.3B) bf16 per card; Open-Sora 2.0 (11B) Q8 per card — 4 parallel generations; CogVideoX-5B int8; Mochi-1 Q4 per card.
Audio / Speech / TTS
Full Western and Chinese audio stack fits per card: Whisper v3 + Parakeet + Canary + Moshi + Step-Audio 2 / R1 + CosyVoice 3.0 + Kokoro + Stable Audio Open + MusicGen + AudioGen + SeamlessM4T v2. With 4 cards, each card can host a dedicated speech tenant. Whisper v3 turbo runs at ~50x realtime per card (published reference).
Multi-model / multi-tenant
The target use case. 16 GB per card rewards partitioned workloads:
- Embedding cluster: BGE-M3 / Nomic / Jina-embed / E5 / Cohere Embed v3 — 4 tenants at high RPS
- 7-13B serving at scale: 16-32 concurrent users per card via vLLM / SGLang; 64-128 concurrent total
- Mixed pipeline: Card 1 = Qwen3-14B + reranker; Card 2 = Whisper + Moshi; Card 3 = FLUX.1; Card 4 = Wan 2.2 TI2V
- 4-way tensor-parallel for 70B Q4 — Llama 3.3 70B AWQ INT4 across 4 cards, ~90-130 tok/s batch aggregate (extrapolated from gf-logic 4x4090 bench)
Target workloads
- Budget multi-GPU AI serving platform for a startup or lab on a capex floor
- Embedding + RAG infrastructure at 4-way horizontal scale
- Image / video generation batch farm (Stable Diffusion / FLUX / Wan 2.2)
- 7-13B small-model serving at scale — 4 independent tenants or 64-128 concurrent pooled
- Development staging box for 70B Q4 tensor-parallel workflows
Published performance references
Kentino measured (4x4090 reference) + published 5080 estimates
| Benchmark | Result |
|---|---|
| 4x4090 reference: sustained fp16 | 647 TFLOPS |
| 4x4090 reference: vLLM Llama 3.3 70B AWQ (batch-32) | 179.3 tok/s aggregate |
| 4x4090 reference: llama.cpp 70B Q4_K_M (single) | 20.3 tok/s decode |
| 5080 estimated: Llama 3.3 70B Q4 TP-4 single | ~15-20 tok/s |
| 5080 estimated: FLUX.1 fp8 per card | ~2.2-2.8 s per 1024x1024 at 20 steps |
5080 tensor throughput ~1.35x 4090 per INT8 TOPS; single-stream decode is memory-bandwidth-bound (GDDR7 ~960 GB/s vs 4090 ~1 008 GB/s — roughly parity).
Not ideal for
- 70B dense at Q6+ (16 GB-per-card limits per-card footprint; 64 GB pool is tight for Q6)
- Long-context MoE flagships (Qwen3-235B, GLM-4.5) — insufficient VRAM even Q2
- Single-stream latency-sensitive work on very large models (TP overhead eats into 16 GB cards)
Warranty and lead time
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 PSU to dual 2 kW ATX synced — raises headroom to 55 %
- NVIDIA ConnectX-5 100 GbE MCX555A-ECAT
- Upgrade boot drive to 4 TB NVMe
- Upgrade RAM to 384 GB (6x 64 GB) — better multi-model concurrent headroom
- Rack PDU (C13/C19 metered) and 3 kVA online UPS
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.