Deploy your own LLM with vLLM

Select a model from the Model Garden or paste a HuggingFace ID, for example Qwen/Qwen3-8B. Every model has a VRAM footprint; the deploy step estimates the minimum VRAM and stops you from picking a GPU that is too small. We request gated models (such as some Llama variants) centrally, so you do not have to arrange individual licences.

• Choose the VRAM and a European GPU region
• We source the capacity from European GPU marketplaces
• We generate a vLLM setup script automatically and start the deploy

The GPU fetches the setup script through a signed token and starts vLLM with its own API key (stored encrypted), so your endpoint is secured. We run readiness probes on /health; as soon as vLLM is ready, the instance calls back and the status flips to "warm". You get an OpenAI- and Anthropic-compatible URL plus an API key.

Reach the instance through the Router by its model slug, or directly on the instance endpoint:

curl https://hostyourai.com/api/instances/<instance-id>/chat/completions \
  -H "Authorization: Bearer hyai-..." \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"Hello"}]}'

Because the instance is OpenAI-compatible, the OpenAI SDK works here too: set the base URL to the Router and pick your model slug.

A dedicated instance bills per hour, but idles when nobody is online: you do not pay for unused time and the instance warms back up on the first request. Your dashboard shows the status, usage and logs. To add context from your own documents, link a knowledge base to the instance, see the RAG guide.

How much VRAM you need depends on the parameter count, quantisation and context length. The table below gives practical guidelines; the deploy step checks the floor automatically, so you can never pick a GPU that is too small. The current model catalogue is in the Model Garden.