---
title: "exo vs Ollama, LM Studio and llama.cpp: what is the difference?"
description: "Ollama, LM Studio and llama.cpp are built mainly to run a model on one machine. exo is built to run one model across many machines: it pools their memory and, with RDMA over Thunderbolt 5, their compute. Use a single-machine tool when your model fits on one computer, and exo when it does not or when you want a cluster to run it faster."
url: https://exolabs.net/faq/exo-vs-ollama-lm-studio-llama-cpp/
updated: 2026-10-26
---

# exo vs Ollama, LM Studio and llama.cpp: what is the difference?

Ollama, LM Studio and llama.cpp are built mainly to run a model on one machine. exo is built to run one model across many machines: it pools their memory and, with RDMA over Thunderbolt 5, their compute. Use a single-machine tool when your model fits on one computer, and exo when it does not or when you want a cluster to run it faster.

On the same four Mac Studios, Jeff Geerling measured exo with RDMA at 32.5 tokens per second on DeepSeek V3.1, against 14.6 for llama.cpp over a regular network.

exo finds devices automatically and plans the split for you. llama.cpp can also split models over a network with its RPC backend, but setup is manual.

exo serves an Ollama-compatible API, so tools built for Ollama, such as Open WebUI, can talk to an exo cluster.

**Where exo fits:** Many people use both: a single-machine tool on a laptop, and exo on a shared cluster for the large models.

Sources:
- [exo on GitHub (README)](https://github.com/exo-explore/exo)
- [Jeff Geerling: 1.5 TB of VRAM on Mac Studio, RDMA over Thunderbolt 5](https://www.jeffgeerling.com/blog/2025/15-tb-vram-on-mac-studio-rdma-over-thunderbolt-5)
- [Ollama](https://ollama.com/)
- [llama.cpp](https://github.com/ggml-org/llama.cpp)

Page: https://exolabs.net/faq/exo-vs-ollama-lm-studio-llama-cpp/ · Updated 2026-10-26 · Deployments: https://exolabs.net/#business (sales@exolabs.net)