From foundation model to hosted AI solution in minutes
What if you could build a custom AI on your own data with a single API call? Learn how to deploy powerful foundation models in minutes.
#1about 3 minutes
Introducing the IONOS AI Model Hub for easy inference
The IONOS AI Model Hub provides a simple REST API for accessing open-source foundation models and a vector database for RAG.
#2about 1 minute
Exploring the curated open-source foundation models available
The platform offers leading open-source models like Meta Llama 3 for English, Mistral for European languages, and Stable Diffusion XL for image generation.
#3about 7 minutes
How to implement RAG with a single API call
Retrieval-Augmented Generation (RAG) is simplified by abstracting vector database lookups and prompt augmentation into one API request using collection IDs and queries.
#4about 1 minute
Building end-to-end AI solutions in European data centers
Combine the AI Model Hub with IONOS Managed Kubernetes to build and deploy full AI applications within German data centers for data sovereignty.
#5about 3 minutes
Enabling direct GPU access within managed Kubernetes
The NVIDIA GPU Operator will enable direct consumption of GPU resources within IONOS Managed Kubernetes by automatically installing necessary drivers and components.
#6about 3 minutes
Deploying custom inference workloads with NVIDIA NIMs
Use the GPU Operator to request GPUs in a pod spec and deploy NVIDIA Inference Microservices (NIMs) to run custom, containerized AI models on your own infrastructure.
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