LM Studio vs Ollama
LM Studio — a desktop application over llama.cpp and MLX, with model discovery and hardware-aware suggestions built in. Ollama — llama.cpp wrapped in a daemon and a model registry. Convenient, at the cost of some control and some lag.
Side by side
| LM Studio | Ollama | |
|---|---|---|
| Model formats | GGUF, MLX | GGUF |
| KV cache quantization | yes | yes |
| CPU offload | yes | yes |
| MoE expert offload | no | no |
| Multi-GPU | Basic; exposes fewer controls than the engine underneath. | Inherits llama.cpp's layer split; little direct control. |
| Concurrency | Local server for personal use. | Fine for a handful of concurrent requests, not for a production workload. |
| Platforms | macOS, Windows, Linux | Linux, macOS, Windows |
Choose LM Studio if…
People who would rather not use a terminal, and Apple Silicon users who want MLX without setting it up themselves.
Closed source, and its convenience layer hides some of the memory controls that matter when a model is close to not fitting.
Choose Ollama if…
Getting started, and any application that wants a local OpenAI-compatible endpoint without managing the engine.
Its short model names map to specific quantizations that are not obvious — a bare tag is usually a 4-bit build, not the model's best available. It also lags upstream, so a very new architecture may not load yet even though llama.cpp supports it.
Why there is no speed comparison here
We have not benchmarked these against each other, so we will not rank them on speed. Most of them wrap the same engine, which makes the differences that matter capability rather than throughput — and where a real speed gap exists it usually comes from configuration, such as how much of the model fits on the GPU and whether the cache is quantized, rather than from the runtime itself.