Beyond Ollama — Engine Map

The four engines that matter beyond Ollama

Engine	Strength	When to reach for it
llama.cpp server	Raw GGUF execution; portable C/C++; low overhead	Embedded, edge, custom builds, when you need control of every flag
vLLM	Throughput; continuous batching; PagedAttention	Multi-user serving; high QPS; sharded multi-GPU
TGI (HF Text Generation Inference)	HuggingFace-native; production-shaped	HF model registry; sharded multi-GPU; HF telemetry
LM Studio	GUI; built-in OpenAI-compat server	Exploration, model comparison, demo days

What they share

All four expose an OpenAI-compatible chat completions endpoint at some path (usually /v1/chat/completions). They all support streaming. The differences are operational: throughput model, hardware target, ergonomics. The model itself (the weights) is portable across all of them.

Should you switch off Ollama?

Probably not, for most projects. Ollama covers ~90% of local serving needs with the lowest setup cost. Switch to llama.cpp server when you need a specific GGUF that Ollama doesn't ship; switch to vLLM when you're serving many concurrent users; switch to TGI when your team already lives in the HuggingFace ecosystem.

Code

Quick smoke tests across engines·bash

# Ollama (default)
curl -s http://localhost:11434/api/version

# llama.cpp server (assuming you started it on 8080)
curl -s http://localhost:8080/v1/models | python3 -m json.tool

# vLLM (default port 8000)
curl -s http://localhost:8000/v1/models | python3 -m json.tool

# TGI (default port 8080)
curl -s http://localhost:8080/info | python3 -m json.tool

# Drop-in OpenAI client at any of them
python3 - <<'PY'
from openai import OpenAI
client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
print(client.models.list())
PY

Beyond Ollama — Engine Map

The four engines that matter beyond Ollama

What they share

Should you switch off Ollama?

Code

External links

Exercise

Progress

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