Ollama vs llama.cpp vs vLLM vs LM Studio — which and when
Four popular ways to run models locally, and which one fits single-user desktop use versus multi-user serving, by GPU vendor and OS. Opinionated but fair.
4 min read Reviewed July 2026
TL;DR
For a single user wanting the easiest start, use Ollama (command line + local API) or LM Studio (graphical app). For control, CPU offload, or non-NVIDIA hardware, use llama.cpp directly. For serving many concurrent users on NVIDIA, use vLLM. The first three read GGUF files; vLLM centres on GPU formats like AWQ/GPTQ/FP8.
Which runtime fits which job?
Four names come up again and again. They overlap — two of them are built on the third — but each has a clear sweet spot.
| Runtime | Best for | Formats | Hardware paths | Interface |
|---|---|---|---|---|
| llama.cpp | Control, CPU offload, non-NVIDIA | GGUF | CUDA, ROCm, Vulkan, Metal, SYCL, CPU | CLI + server |
| Ollama | Easiest single-user start | GGUF | Same engine as llama.cpp (built on it) | CLI + local API |
| LM Studio | Non-technical desktop use | GGUF (+ MLX on Apple Silicon) | Windows, macOS (Metal), Linux | GUI + local server |
| vLLM | Multi-user production serving | AWQ / GPTQ / FP8 | CUDA (some ROCm) | OpenAI-compatible API |
llama.cpp — the flexible engine
The engine much of the ecosystem is built on. It runs GGUF models on CPU, GPU, or a split of both, across every vendor path — CUDA, ROCm, Vulkan, Metal and SYCL (GPU software stacks) — and ships a CLI and a server. Reach for it when you want control, need to offload layers to CPU because a model barely fits, or you are on non-NVIDIA hardware.
Ollama — the easy on-ramp
A friendly wrapper built on llama.cpp. One command pulls a model and starts a simple local API with sensible defaults. Ideal for a single user getting started or wiring a model into a small app — at the cost of less low-level control, because the defaults are chosen for you.
LM Studio — the desktop GUI
A graphical desktop app (also llama.cpp-based, with MLX on Apple Silicon). It gives you a model browser, a chat UI, and a local OpenAI-compatible server. Great for single-user, non-technical use on Windows, macOS or Linux, and it uses Metal on Macs.
vLLM — the serving engine
Built for production, multi-user serving. It is GPU-first (CUDA, with some ROCm), does high-throughput batched inference (PagedAttention), and exposes an OpenAI-compatible API. It centres on GPU formats like AWQ/GPTQ/FP8 rather than GGUF. Overkill for one person; the right tool when you are serving many concurrent requests.
How do I choose?
Pick in 30 seconds
- Serving an app or many concurrent users on NVIDIA? vLLM — batched throughput is what it exists for. Everyone else, keep going.
- Want the easiest single-user start? Ollama if you're happy in a terminal, LM Studio if you want a graphical app.
- On a Mac? Any of llama.cpp, Ollama or LM Studio — all use Metal, and LM Studio adds MLX (which Mac chip).
- Need control, CPU offload, or non-NVIDIA hardware? Go straight to llama.cpp.
Which model format you download depends on the runtime — GGUF for the llama.cpp trio, GPU formats for vLLM. See Quantization formats explained.
Can I use the models Ollama downloads in llama.cpp?
In practice, yes. Ollama stores model weights on disk as GGUF data, and llama.cpp reads GGUF — no conversion needed. The catch is that Ollama's model store names files by content hash rather than by model name, so finding the right file takes a little digging.
Do these runtimes give different output quality from the same model?
The runtime mostly determines speed, hardware support and interface — not intelligence. Output can still differ between runtimes because of different default sampling settings, prompt templates and context lengths, so if a model "behaves differently" in two runtimes, check those settings before blaming the runtime.
Can I switch runtimes later?
Yes, cheaply. Within the llama.cpp family (llama.cpp, Ollama, LM Studio) the same GGUF file works everywhere. Moving to vLLM means downloading the model again in a GPU format — the weights are the same model, packaged differently.