---
title: "exo vs vLLM: which should I use?"
description: "Use vLLM to serve models on datacenter GPUs, mostly NVIDIA, at high concurrency. Use exo to run large models on Apple silicon Macs, especially when one model needs the memory of several machines. vLLM is built for throughput in a GPU server; exo is built to turn desk-side hardware into one cluster with no configuration."
url: https://exolabs.net/faq/exo-vs-vllm/
updated: 2026-10-26
---

# exo vs vLLM: which should I use?

Use vLLM to serve models on datacenter GPUs, mostly NVIDIA, at high concurrency. Use exo to run large models on Apple silicon Macs, especially when one model needs the memory of several machines. vLLM is built for throughput in a GPU server; exo is built to turn desk-side hardware into one cluster with no configuration.

vLLM’s strengths are continuous batching and PagedAttention, which keep many requests flowing through expensive GPUs. It suits a GPU server or cloud deployment with an operations team behind it.

exo’s strengths are automatic device discovery, topology-aware splitting of models across machines, and RDMA over Thunderbolt 5 for tensor parallelism between Macs. One 256 GB Mac Studio holds as much model as eight 32 GB consumer GPUs, in a single quiet box.

Both serve OpenAI-compatible APIs, so the apps on top do not need to change if you move between them.

**Where exo fits:** If you want to run frontier-size open models without a GPU server room, exo on a few Mac Studios is the shortest path. Exo Labs can help you compare it with a GPU build for your workload.

Sources:
- [exo on GitHub (README)](https://github.com/exo-explore/exo)
- [vLLM documentation](https://docs.vllm.ai/)

Page: https://exolabs.net/faq/exo-vs-vllm/ · Updated 2026-10-26 · Deployments: https://exolabs.net/#business (sales@exolabs.net)