vLLM Hosting Europe
High-performance LLM inference with vLLM in European data centers.
Ask two teams to serve the same open model on the same GPU and you can get wildly different throughput. The difference is usually the serving engine. HostYourAI runs every model, shared or dedicated, on vLLM: the open source engine that has become the de facto standard for LLM inference in production.
You get vLLM's efficiency without operating it yourself, on GPUs in European datacenters, behind an OpenAI-compatible endpoint. Prompts stay inside the EU, a DPA is available, and nothing you send is ever used for training.
Why vLLM hosting in the EU
Efficient serving is a data protection choice too
The more requests a single EU-based GPU can handle, the less pressure there is to spill traffic to whichever cloud is cheapest that week. An efficient engine on European hardware keeps the whole path, network, GPU and logs, under GDPR-friendly jurisdiction.
Streaming feels fast when the server is nearby
vLLM streams tokens over SSE, and streaming makes distance visible: every chunk crosses the network. Serving European users from multiple European regions keeps that path short.
Operating vLLM well is a real job
Version bumps, CUDA compatibility, memory tuning, health probes: self-hosting vLLM means owning all of it. Here that work is done for you, backed by a 99.9% uptime SLA, and if you want to understand the self-managed path first, read Deploy your own LLM with vLLM.
from openai import OpenAI client = OpenAI( base_url="https://hostyourai.com/api/v1", api_key="hyai-...") client.chat.completions.create( model="llama-3.3-70b", messages=[{"role":"user","content":"Hallo!"}])
The serving engine matters more than raw GPU specs
A GPU spec sheet tells you almost nothing about tokens per second under real traffic. Two mechanisms in vLLM do the heavy lifting. Continuous batching means the engine does not wait for a batch to finish before admitting new requests; as soon as one sequence completes, the next one slots in, so the GPU rarely sits idle between requests. PagedAttention manages the attention cache the way an operating system manages memory, in small pages instead of one big contiguous block, which sharply reduces wasted VRAM and fits more concurrent sequences on the same card. No made-up speed multipliers here; the practical effect is simply better utilisation per GPU-hour, whether you pay per token on the EU Inference Router or per hour on dedicated GPU instances. Popular models stay warm through the Router's warm pool, and each dedicated instance runs vLLM behind its own encrypted API key.
| vLLM term | What it means for you |
|---|---|
| Continuous batching | New requests join mid-flight, less idle GPU time |
| PagedAttention | Less wasted VRAM, more concurrent users per GPU |
| SSE streaming | Tokens appear as they are generated, chat feels responsive |
| OpenAI-compatible server | Your existing SDK and tools work unchanged |
| Warm pool | Popular Router models answer without a cold start |
| Per-instance API key | Your dedicated endpoint is encrypted and yours alone |
How to start
Pick a model from the model catalog and call it with the standard OpenAI SDK. Streaming works out of the box.
from openai import OpenAI
client = OpenAI(
base_url="https://hostyourai.com/api/v1",
api_key="hyai-...",
)
stream = client.chat.completions.create(
model="gemma-3-12b",
messages=[{"role": "user", "content": "Explain PagedAttention in one paragraph."}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")Questions about vLLM hosting
Do I need to configure vLLM myself?
No. On the shared Router it is fully managed, and dedicated instances boot with vLLM configured for the chosen model, including a VRAM floor check so the GPU is never too small.
Is the endpoint really OpenAI-compatible?
Yes, /v1/chat/completions with SSE streaming, and there is also an Anthropic-style /v1/messages shim with x-api-key auth for text-only, buffered-streaming use.
What is a cold start and will I hit one?
Loading model weights onto a GPU takes time. Popular Router models are kept warm in a warm pool, and the catalog shows each model's state: warm, warming up, serveable or on request.
Can vLLM serve my fine-tuned model?
Yes, on a dedicated instance. vLLM loads open-weight variants, so a fine-tune of Llama, Qwen or Mistral serves the same way as the base model.
How is my dedicated instance protected?
Each instance has its own encrypted API key, traffic runs over TLS and data at rest is AES-256 encrypted. Instances also idle down when unused.
What does this cost?
Pay-as-you-go from one prepaid credit balance: per token on the Router, per hour for dedicated capacity. Details are on the pricing page.
Everything you need for AI
From model hosting to a customer-facing API, it is built for developers and businesses who want their AI running on infrastructure they actually control, inside the EU.
Your data and your models stay on European GPUs. GDPR-friendly by design.
Llama, Qwen, DeepSeek, Mistral, FLUX and plenty more. Pick one and it is warm in minutes, with no DevOps on your end.
Point your existing client at the Router and keep your tools. No rewrite, no lock-in.
From zero to a warm endpoint in minutes
No infra to manage. Pick a model, get an OpenAI-compatible URL, ship.
Pick a model
Choose from the Model Garden or paste any HuggingFace ID. Set the VRAM and pick an EU GPU.
Get your endpoint
We deploy vLLM, run readiness probes, and hand you a warm OpenAI- and Anthropic-compatible URL plus an API key.
Route and ship
Point your client at the Router. It auto-routes to a warm instance, idles GPUs when nobody is online, and logs every request.
Works with the tools you already use
The Router speaks the OpenAI and Anthropic APIs, so it drops straight into the clients and SDKs your team already runs. Just change the base URL.
Try HostYourAI for free
Built for teams that can't send data away
If a US cloud is off the table, HostYourAI gives you the same developer experience on European infrastructure.
Citizen data that legally has to stay in the EU, with full auditability.
Finance, healthcare and legal teams under GDPR, DORA and the AI Act.
Ship AI features your customers trust, without a US sub-processor.
Deliver private AI for clients on infrastructure you can stand behind.
Frequently asked questions
Can I run this in the EU?
Yes. HostYourAI runs open models on GPUs in European datacenters via vLLM. Your prompts and outputs never leave the EU and there is no US cloud provider in the chain.
Is it GDPR-compliant?
Yes. All processing happens inside the EU, a Data Processing Agreement (DPA) is available and the subprocessor list is public. Open weights also mean no training on your data.
Is the API OpenAI-compatible?
Yes. Point your existing OpenAI or Anthropic client at our Router (https://hostyourai.com/api/v1), change only the base URL and API key. No rewrite, no lock-in.
What does it cost?
Pay-as-you-go on one prepaid credit balance: the shared router per token or a dedicated GPU per hour. Free to start, no minimum, no fixed monthly fee.
Works with 390+ open models
Text and image models on dedicated EU GPUs. Every model tested on our own hardware.
Related pages
Explore more about EU-hosted AI on HostYourAI.
Host Qwen Coder 32B on dedicated NVIDIA A100 40GB in European data centers. GDPR compliant, pay-as-you-go, OpenAI-compatible API.
Read more →Use your Anthropic client with open models on European GPUs. A Claude API alternative that speaks /v1/messages and keeps your data in the EU.
Read more →OpenRouter often routes prompts to US providers and gates EU processing behind enterprise plans. HostYourAI hosts on EU GPUs, self service.
Read more →Machine learning hosting on dedicated GPUs. Training, inference, fine-tuning - all on European infrastructure.
Read more →HostYourAI Code: run coding agents such as Claude Code on EU-hosted open models. Pay per token from prepaid credits, with tools, streaming and live usage insight.
Read more →AI for government and the public sector on European GPUs. Citizen data stays in the EU, with a DPA, open models and full auditability.
Read more →