Ollama vs vLLM vs llama.cpp: Choosing a Local Inference Engine for Your Stack

The inference engine is the backbone of any local AI setup. Ollama, vLLM, and llama.cpp are the three most popular options, but they serve very different use cases. Choosing the right one early saves you from rebuilding your stack later.

What each engine does well

Ollama

Ollama is the easiest entry point for local inference. It wraps model downloading, runtime configuration, and a basic API into one install-and-run experience. If you are new to local AI or just want a private ChatGPT replacement, start here.

It is ideal for personal use, small teams, and prototyping. The trade-off is that you have less control over advanced settings like scheduling, batching, and continuous batching.

For a step-by-step guide, read How to Run Llama 3 Locally with Ollama.

vLLM

vLLM is built for throughput. It uses PagedAttention to manage GPU memory efficiently and supports continuous batching, making it the best choice for serving multiple users or high-traffic applications.

If you are running a local AI service that several people depend on — a team chat interface, an internal API, or a document processing pipeline — vLLM will handle the load better than any other open-source option.

llama.cpp

llama.cpp is the most versatile engine. It runs on CPU, GPU, Apple Silicon, and even Android. It supports the widest range of quantization formats and model architectures. Its llama-server mode provides an OpenAI-compatible API.

Choose llama.cpp when you need maximum compatibility with unusual hardware, want to run entirely on CPU, or need to support model formats that other engines cannot handle.

Performance comparison

For single-user interactive chat on a GPU, Ollama and llama.cpp are comparable. For multi-user serving, vLLM's continuous batching gives it a clear advantage. For CPU-only inference, llama.cpp is the only practical choice.

Your hardware choices affect which engine makes sense. See Choosing the Right GPU for Local AI Inference to understand how your GPU determines the feasible options.

Production readiness

Only vLLM offers built-in monitoring, metrics export, and API-level rate limiting out of the box. Ollama and llama.cpp can be productionised, but you will need to add a reverse proxy, monitoring, and access control yourself.

For hardening the deployment side, read Harden Docker Compose Stacks for Local AI Services.

Recommendation

Start with Ollama for learning and personal use. Move to vLLM when you need to serve multiple users. Use llama.cpp when you need maximum hardware compatibility or CPU-only inference.

Conclusion

Do not treat the inference engine as a permanent decision. The model runtime is a layer that can be swapped as your requirements grow. The important thing is knowing when each tool is the right fit.

FAQ

Can I switch engines without changing my chat UI?

Yes. All three engines expose OpenAI-compatible APIs, so most chat UIs can switch between them with a URL change.

Which engine uses the least memory?

llama.cpp with GGUF quantised models typically has the smallest memory footprint.

Is Ollama suitable for production?

It can work for light production use, but you will need to add monitoring, rate limiting, and access controls yourself.