Low-latency AI inference, hosted in Europe
AI inference hosting in Europe means your users stop paying the transatlantic round-trip tax — and their prompts stop leaving EU jurisdiction. Serve models from dedicated RTX 3090, 5090 and RTX PRO 6000 GPUs in Belgian edge datacenters, billed per second from $0.08/GPU/hr.
- Edge datacenters in Belgium
- EU jurisdiction, GDPR-native
- Dedicated GPUs, not a marketplace
- Per-second billing
- From $0.08/GPU/hr
Why serve models from Europe, not just to Europe
If your users are in Amsterdam, Paris or Frankfurt and your model runs in a US region, every request crosses the Atlantic twice before the first token appears. That baseline network delay sits on top of model latency in every chat turn, autocomplete keystroke and voice interaction — and no amount of GPU horsepower removes it. Serving from edge datacenters in Belgium, at a fiber crossroads between the major EU metros, keeps the network leg short so latency is dominated by your model, not by geography.
Latency is only half the argument. The prompts, documents and audio your users send at inference time routinely contain personal data, which makes model serving itself a GDPR processing activity. EponEdge runs on European-owned hardware and operations in Belgium, so inference traffic stays under EU jurisdiction end to end — GDPR-safe inference without the transfer paperwork a US-owned region drags in, and no exposure to the US CLOUD Act.
Model inference hosting in the EU, sized honestly
Each instance is a dedicated GPU: an NVIDIA RTX 3090 (24 GB of GDDR6X at 936 GB/s), an RTX 5090 (32 GB of GDDR7) or an RTX PRO 6000 Blackwell (96 GB of GDDR7). Memory bandwidth is what feeds token generation, and even the 3090 serves a lot: quantized 7B–13B LLMs, Stable Diffusion and Flux image generation, Whisper-class transcription, embedding and reranking endpoints. Bigger models scale up the lineup — 96 GB on the RTX PRO 6000 fits quantized 70B-class LLMs on a single card — and we would rather you size that honestly before you deploy than after.
Deploy your own stack, keep the keys
You get root SSH on a dedicated machine, not a shared endpoint: run vLLM for high-throughput OpenAI-compatible serving, TGI or Ollama for simpler setups, or your own containerized service. Automate deploys and scaling through the console and full REST API. Because billing is per second and paused time is never billed, low-traffic and bursty endpoints stop costing money the moment you stop them — and instances resume with processes and GPU memory intact, so a stopped model server comes back warm rather than reloading weights from scratch.
For inference that must never blink, launch on the Guaranteed tier; for batch pipelines, the Flexible tier at $0.08/GPU/hr makes low-latency inference in Europe cheaper than most teams expect. Need several nodes behind a load balancer? Talk to sales about reserved capacity and multi-node clusters.
What serves well on 24 GB
| Workload | Fits on one 3090? | Typical stack | Notes |
|---|---|---|---|
| LLM chat, ~7B (FP16) | Yes | vLLM, TGI, Ollama | Headroom for KV cache and concurrent sessions |
| LLM chat, ~13B (quantized) | Yes, quantized | vLLM (AWQ/GPTQ), llama.cpp | 4-bit quantization; watch context length |
| Image generation (SD 1.5 / SDXL / Flux) | Yes | ComfyUI, Diffusers, A1111 | Per-second billing suits bursty generation |
| Speech-to-text (Whisper family) | Yes | faster-whisper, Triton | Batch transcription pairs well with the Flexible tier |
| Embeddings & rerankers | Yes | TEI, Sentence-Transformers | Small models, high throughput per GPU |
| 70B-class LLMs | No | — | Beyond 24 GB — a fit for the 96 GB RTX PRO 6000 |
RTX 3090 $0.25 / $0.12 / $0.08 · RTX 5090 $0.65 / $0.44 / $0.26 · RTX PRO 6000 $1.49 / $0.99 / $0.59 per GPU per hour (Guaranteed / Balanced / Flexible), billed per second. Storage $0.15/GB/mo · bandwidth $0.02/GB. See full pricing details.
AI inference hosting in Europe, answered
Which models can I serve on a 24 GB RTX 3090?
As a rule of thumb: 7B-class LLMs in FP16, and 13B-class models quantized (GPTQ/AWQ/GGUF), with room left for KV cache. Stable Diffusion 1.5, SDXL and Flux, Whisper-family speech models, embedding and reranking models all serve comfortably. Very large models (70B-class) don't fit a single 3090 — for those, the 32 GB RTX 5090 or the 96 GB RTX PRO 6000 Blackwell is the better fit.
Is inference-time user data covered by GDPR?
Generally yes. Prompts, uploaded documents, audio and chat history routinely contain personal data, so where inference runs matters as much as where training ran. EponEdge processes everything in European-owned datacenters in Belgium, under EU jurisdiction only — no exposure to foreign instruments such as the US CLOUD Act.
Can I run vLLM, TGI or Ollama on EponEdge?
Yes. Instances come with root SSH, so you can install any serving stack — vLLM, Text Generation Inference, Ollama, llama.cpp, Triton or your own FastAPI service — and manage it through the full REST API. JupyterLab is included for interactive testing.
Which pricing tier should I use for inference?
For user-facing endpoints, Guaranteed (from $0.25/GPU/hr) — it is never paused, even during grid stress. Balanced (from $0.12) suits internal tools that tolerate rare pauses, and Flexible (from $0.08) fits batch inference such as overnight embedding or transcription jobs. All tiers bill per second, and paused time is never billed.
What if my model is too big for one GPU?
Step up the lineup: a single RTX 3090 tops out around quantized 13B-class LLMs, the 32 GB RTX 5090 stretches further, and the 96 GB RTX PRO 6000 Blackwell serves quantized 70B-class models on one card. For multi-node clusters or reserved capacity, contact sales and we'll tell you honestly whether your workload maps onto our hardware.
Related solutions
Serve your model from Europe.
Close to users, under EU law.
Load prepaid credit and deploy in minutes — or ask us about reserved capacity and multi-node inference clusters.