How to Choose a Server for Self-hosted AI | AIOOS

Start with the workload boundary

An AI application server may only run a web interface, database and workflow engine while calling a hosted model API. That is very different from loading a model locally. Write down which components run on the machine before comparing plans.

CPU and memory

Application stacks such as Dify include multiple services, so memory headroom matters. Local inference depends on the exact model, quantization and context size. Do not buy from a generic label such as AI-ready without checking the actual workload.

Disk and network

Model files, container images, vector indexes, uploads and backups all consume disk. Network needs depend on whether users upload files, stream responses or download models. Check both bandwidth and traffic accounting.

A safer buying process

• Begin with a test environment and measure it.

• Prefer monthly billing until the route and workload are verified.

• Keep the database and uploaded files backed up outside the instance.

• Scale from observed memory, latency and disk metrics.

• Treat CPU-only and GPU inference as separate product decisions.

Continue

Compare the AIOOS server plans, then follow the Dify deployment guide or Ollama installation guide.