Serving Stack — Ollama, vLLM, MLX, and UI Tools

The local LLM ecosystem has a few recurring layers:

runtime
serving layer
developer API
UI layer

Understanding the stack makes tool choices much easier.

Ollama

Best for:

easiest developer experience
simple local server
fast prototyping

It is usually the right first tool for local experimentation.

vLLM

Best for:

high-throughput self-hosted inference
batching multiple requests
serving larger models on stronger GPU hardware

If Ollama is "developer-friendly local server," vLLM is "serious serving layer."

MLX

Best for:

Apple Silicon
native-feeling local inference on M-series Macs

MLX matters because Apple hardware is a major part of the local LLM story.

UI Tools

Common UI layers include:

Open WebUI
LM Studio
AnythingLLM

These tools are useful for:

chatting with local models
prompt iteration
local RAG demos
non-engineer access to local inference

A Good Mental Model

textModel files / weights
  ↓
Runtime (llama.cpp / MLX / transformers)
  ↓
Server (Ollama / vLLM)
  ↓
App or UI (OpenAI SDK / Open WebUI / custom product)

Different tools may combine more than one layer, but the architecture is still useful to remember.

Recommended Starting Paths

Solo developer

Start with:

Ollama
one instruct model
one embedding model
optionally Open WebUI

Apple Silicon user

Try:

Ollama first
MLX when you want more Apple-optimized control

Team serving internal apps

Consider:

vLLM for throughput
proper observability and rate limiting
dedicated GPU nodes

Interview Answer

How do the local LLM tools fit together?

They form a stack. Runtimes execute the model, serving layers expose an API, and UI or application layers sit on top. Ollama is ideal for getting started, vLLM is better for higher-throughput serving, and MLX is especially relevant for Apple Silicon workflows.