I'm building a prototype and want the easiest way to serve a model

The easiest way depends on whether you want local prototyping or an actual API endpoint.

Fastest options

1) Use a hosted model API

If you just want to prototype quickly, this is usually the simplest:

• OpenAI API
• Hugging Face Inference API
• Together / Groq / Anthropic / etc.

Pros: no infra, instant setup, scalable
Cons: costs, external dependency

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2) Run a local server with vLLM or Ollama

If you want to serve a model on your own machine/server:

Ollama — easiest for local demos

Good if you want “download model → run API”.

Example:

ollama run llama3.1

Then call the local API:

curl http://localhost:11434/api/generate -d '{
  "model": "llama3.1",
  "prompt": "Write a haiku about servers"
}'

Pros: very easy, minimal setup
Cons: less flexible for production-scale serving

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vLLM — easiest for a real Python-based server

Good if you want something more production-like.

Install:

pip install vllm

Run an OpenAI-compatible server:

vllm serve meta-llama/Meta-Llama-3-8B-Instruct

Then use it like an OpenAI API endpoint.

Pros: fast, scalable, OpenAI-compatible
Cons: more setup than Ollama

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If you want the absolute simplest recommendation

• Local prototype: use Ollama
• Web app / API prototype: use hosted API or vLLM
• Need custom Python code and more control: use FastAPI + vLLM

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Minimal “serve a model” stack

If you want a basic setup with little effort:

1. Pick a model
2. Use Ollama or vLLM
3. Put a thin wrapper in FastAPI
4. Deploy to a small VM or Docker container

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My practical recommendation

For the easiest path:

Option A: Local proof-of-concept

• Install Ollama
• Pull a model
• Call its HTTP API

Option B: Prototype backend

• Use vLLM
• Expose an OpenAI-compatible endpoint
• Your app can swap to a hosted API later with minimal code changes

If you want, I can give you:

1. the single easiest local setup,
2. a FastAPI server example, or
3. a Docker deployment example.