Ollama vs LM Studio vs llama.cpp: A 5-Minute Guide

Ollama, LM Studio, and llama.cpp are not competitors but three costumes on the same engine. When you run a model in Ollama or LM Studio, it is effectively llama.cpp doing the inference underneath, so "which is fastest" matters less than "which fits your hands": LM Studio if you click, Ollama if you live in the terminal or automate, llama.cpp for maximum control and speed. The convenience tax (overhead) is a measured ~+0.3% for LM Studio and ~+10% for Ollama versus raw (same model, quant, and GPU), effectively negligible for 8B-class models. And since the three do not conflict, you can just install all of them and pick per task.

In one line: llama.cpp is the engine, and Ollama and LM Studio are cars built on it. If you only want to drive (chat), the finished car is easier; if you want to tune the engine, you open the hood.

How do the three runners differ?#

By interface, control, and default audience. Ollama runs as a daemon, giving you ollama run and an OpenAI-compatible API (local), with a solid model library and sensible default quantizations, strong for development and agent integration. LM Studio is GUI-first with model search and one-click chat, plus a server mode and an lms CLI, friendly to non-developers, and it has the lowest overhead. llama.cpp is the engine itself: llama-server exposes a web UI plus API, and you control CPU, CUDA, Metal, Vulkan, and hybrid offload precisely with flags - its strengths are the newest optimizations and obscure models and unusual hardware.

As the diagram below shows, the outer interfaces differ, but the engine that actually produces tokens converges on one llama.cpp-family core that reads GGUF.

Three local-LLM runners compared - interface, overhead, audience (2026 public measurements) · columns: Runner, Interface, Convenience overhead, Server/concurrency, For whom · 출처 Hax hax.moche.ai/en/p/1026?ref=ai_answer

Runner | Interface | Convenience overhead | Server/concurrency | For whom
Ollama | CLI (daemon) | measured ~+10% | OpenAI-compatible API, 1 concurrent by default | Dev, automation, agents
LM Studio | GUI (+lms CLI) | measured ~+0.3% | GUI server mode | Non-coders, research, Mac (MLX)
llama.cpp | CLI / llama-server | baseline (raw) | web UI + API directly | Control, speed, rare models

Contents

I hear MLX is faster on a Mac?

Where do beginners get stuck?

How do you compare them in 5 minutes?

I hear MLX is faster on a Mac?#

Yes - on Apple Silicon, MLX is a measured 15-25% faster. GGUF is the standard format created by llama.cpp that nearly every consumer tool uses, but on Macs Apple's own MLX engine is faster (one benchmark on an M4 Pro with Qwen3-Coder-30B showed MLX at about 130 tok/s versus llama.cpp's Metal at about 43 tok/s). LM Studio supports MLX natively, and Ollama added MLX too but only enables it on Macs with 32GB+ unified memory (below that it stays on the Metal path). Note that GGUF and MLX are different formats, so you cannot load one engine's model into the other.

On the same M4 Pro and same model, swapping only the engine widens throughput by about 3x - here it is as bars (vertical = tok/s, taller is faster).

Where do beginners get stuck?#

Three things: concurrency, context defaults, and format.

Concurrency: Ollama serves one request at a time by default, so throughput collapses under load (multi-user serving is vLLM's territory).

Context defaults: Ollama's default context scales with VRAM from 4k to 256k, so a large context eating memory slows even the same model (a hidden speed trap).

Format: if you want speed on a Mac, first check whether MLX weights exist for the same model.

How do you compare them in 5 minutes?#

Load the same model on all three. Follow the flow below.

If new, start one - LM Studio (click) or Ollama (terminal) - with an 8B-class model.

Measure tok/s side by side on the same model and quant (the gap is small for 8B-class).

If you need the last 10-25%, graduate to raw llama.cpp, or MLX on a Mac. There is no lock-in, so run them in parallel.

Reference links

Ollama (daemon-style runner)

llama.cpp (inference engine)

GGUF/ggml (format and library)

MLX (Apple Silicon engine)

mlx-lm (MLX LLM tooling)

Note: overhead and tok/s figures are public 2026 measurements and vary by model, quantization, context, hardware, and version (wrapper overhead in particular is shrinking with updates). Measure exact speed on your own device with the method above. Runners and formats update often, so this is reviewed quarterly.