They all run local models, but they're built for completely different people. One's for easy desktop use, one's for flexibility, one's for serving at scale. Here's how to pick without wasting a weekend.
Three names come up constantly in local AI — vLLM, Ollama, and llama.cpp — and newcomers waste hours because they assume they're competitors when they're really tools for different jobs. Ollama is the easy on-ramp: install it, type one command, chat with a model. llama.cpp is the flexible engine underneath a lot of things (including Ollama), the one that runs on CPUs, Macs, and lets you offload big models. vLLM is the production server: built for serving many requests at high throughput on GPUs. Pick by what you're actually doing, and the choice is obvious.
What each is actually for
Ollama optimises for the individual who just wants to run a model with zero fuss — it handles downloads, quantization choice, and a clean API, and it's the right first tool for almost everyone. llama.cpp is what you reach for when you need control: running on a Mac or CPU, offloading a big MoE model to system RAM, or squeezing a model onto limited VRAM with specific flags. It's the flexible workhorse (and it powers Ollama under the hood). vLLM is a different animal — it's for serving a model to many users at once with maximum throughput, using techniques like continuous batching and PagedAttention. If you're building an app or an internal service, vLLM; if you're one person chatting, Ollama or llama.cpp.
Ollama vs llama.cpp
Ollama
Easy · desktop
vs
llama.cpp
Flexible · low-level
Very easy
Ease of use
Moderate
Yes
CPU/Mac support
Yes
Limited
MoE offload control
Full (--n-cpu-moe)
Minutes
Setup time
Longer
Less
Fine control
More
Ollama wins 2wins 2 llama.cpp
vLLM is the odd one out — not a desktop tool but a high-throughput serving engine for many concurrent users. · Unsplash
And where does ExLlamaV2 fit?
There's a fourth name worth knowing: ExLlamaV2, which is the speed specialist for GPU-resident models using the EXL2 format — faster than llama.cpp on the GPU, but with no CPU offload, so the model must fit your VRAM. Between these tools you can cover every case: Ollama to start, llama.cpp for flexibility and offload, ExLlamaV2 for raw GPU speed when a model fits, and vLLM when you're serving at scale. Most individuals never need more than the first two.
Quick answers
Should I use Ollama or llama.cpp?
Start with Ollama — it's dramatically easier, handles everything with one command, and is built on llama.cpp anyway so you're getting the same engine with less friction. Switch to raw llama.cpp when you need fine control Ollama doesn't expose: specific offload flags for big MoE models, custom quantization, or running on unusual hardware. For most people, Ollama is the answer; llama.cpp is there when you outgrow it.
Is vLLM better than Ollama?
They're not comparable — they solve different problems. vLLM is far better for serving a model to many concurrent users at high throughput (an app backend, an internal service). Ollama is far better for one person running a model on their desktop. Using vLLM for personal chat is overkill and harder to set up; using Ollama to serve hundreds of users won't scale. Pick by whether you're serving at scale or running locally for yourself.
Which is fastest?
For serving many requests, vLLM wins on throughput. For single-user GPU generation where the model fits VRAM, ExLlamaV2 is typically fastest. Ollama and llama.cpp are comparable (Ollama uses llama.cpp) and prioritise flexibility over peak speed. 'Fastest' depends on the workload: throughput for many users (vLLM), single-stream GPU speed (ExLlamaV2), or flexibility with good-enough speed (Ollama/llama.cpp).
Don't overthink it: start with Ollama, move to llama.cpp when you need flexibility, add ExLlamaV2 for GPU speed, and use vLLM only when you're serving at scale. Match the tool to the job and local AI gets a lot simpler.
