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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.

Sources

  1. exo on GitHub (README)
  2. Jeff Geerling: 1.5 TB of VRAM on Mac Studio, RDMA over Thunderbolt 5
  3. Ollama
  4. llama.cpp

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