LM Studio vs Ollama on Mac: Which Should You Use?

More on this topic: Ollama vs LM Studio (general) | Best Local LLMs for Mac | Running LLMs on Mac M-Series | LM Studio Tips & Tricks | llama.cpp vs Ollama vs vLLM

We already have a general Ollama vs LM Studio comparison. This isn’t that article. Most comparisons treat both tools as if they behave the same on every platform. They don’t. On Mac, the story is different because of three things: unified memory, Metal GPU acceleration, and Apple’s MLX framework.

LM Studio has an MLX backend that Ollama doesn’t. That one difference changes the performance picture enough that I kept seeing different numbers on Mac than what generic comparison articles reported. If you’re running local AI on Apple Silicon, the choice between these two tools depends on what you’re doing, not just what you prefer.

The Mac difference

Unified memory changes the math

On a PC with a discrete GPU, both tools use the same backend (llama.cpp) and get similar performance. The GPU has its own VRAM, the model loads into it, and the tool is mostly just a wrapper.

On Mac, there’s no separate VRAM. Your entire RAM pool is shared between CPU and GPU. How a tool manages that shared pool affects both speed and how much memory is left for everything else. Ollama and LM Studio take different approaches here, and the results are measurable.

Two backends, not one

On Windows and Linux, both tools use llama.cpp for inference. On Mac, LM Studio also supports MLX – Apple’s native machine learning framework, built specifically for Apple Silicon’s unified memory architecture. Ollama uses llama.cpp with Metal acceleration on Mac, which is good, but MLX is faster.

This isn’t a small difference. MLX consistently outperforms llama.cpp by 20-30% on token generation and up to 5x on prompt processing. A model running through LM Studio’s MLX backend will be faster and use less memory than the same model running through Ollama’s llama.cpp + Metal stack.

Ollama has talked about MLX support but hasn’t shipped it yet. Until they do, LM Studio is measurably faster on Mac.

Head-to-head comparison

Installation

Ollama: One command.

brew install ollama

Or download from ollama.com. It installs as a background service (launchd on Mac), starts automatically at boot, and sits quietly until you need it. Total footprint when idle with no model loaded: about 50MB of RAM.

LM Studio: Download the .dmg from lmstudio.ai, drag to Applications. It’s an Electron/Tauri desktop app, so it uses more resources at idle. Expect 300-500MB of RAM just sitting there with no model loaded. Close it when you’re not using it.

Winner: Ollama for lightweight installs. LM Studio for people who don’t want to touch a terminal.

Model discovery and download

Ollama has a curated registry of ~200 models. You pull by name:

ollama pull llama3.1:8b-instruct-q4_K_M

The naming convention takes some learning. You need to know the model family, size, and quantization tag. But once you do, pulling models is fast and predictable.

LM Studio connects directly to Hugging Face and has a built-in search with filters for size, format, quantization, and compatibility. Browse thousands of models, click download. It also shows you compatibility ratings and memory estimates before you commit.

On Mac specifically, LM Studio lets you choose between GGUF (llama.cpp) and MLX format for the same model. Always pick MLX when it’s available – it’s faster and more memory-efficient on Apple Silicon.

Winner: LM Studio for discovery. Ollama for scripted/automated pulls.

Performance on the same Mac

Same model, same quantization, same Mac. Different results depending on which tool (and which backend) is running.

Benchmarks from an M3 Pro MacBook Pro (18GB):

The prompt processing gap surprised me. If you paste a long document and ask a question about it, LM Studio processes the input 5x faster. For short prompts the difference is less noticeable, but for RAG pipelines or document analysis, it adds up fast.

Note: if you load a GGUF model in LM Studio (not MLX), performance is very close to Ollama because they’re both running llama.cpp underneath. The speed advantage is specifically from the MLX backend.

Memory usage

This is the other Mac-specific difference that matters. Tested with Qwen 3 8B:

LM Studio’s MLX backend uses roughly half the memory of the same model loaded through llama.cpp. On a 16GB machine, that’s the difference between 6GB left for the system or 11GB left. On a 24GB machine it barely matters. On 8GB or 16GB, it decides whether your model runs clean or swaps to disk.

Ollama also pre-allocates memory at model load, reserving space for the KV cache and safety margins upfront. LM Studio’s MLX backend is more efficient about lazy allocation. The practical result: Ollama is more likely to cause memory pressure warnings on constrained machines.

Multi-model loading

Ollama can keep multiple models in memory simultaneously. Configure it with:

OLLAMA_MAX_LOADED_MODELS=2

It handles routing between them automatically. This is useful if you’re running, say, a coding model and a chat model behind different API endpoints. The scheduler unloads the least-recently-used model when memory runs out.

LM Studio can also load multiple models at once through its server mode. Since version 0.4.0, it supports parallel requests with continuous batching, and JIT (just-in-time) model loading so it loads models on demand when a request comes in.

Winner: Tie, with different strengths. Ollama is more “set and forget” for background multi-model serving. LM Studio’s JIT loading is smarter about memory.

API compatibility

Both expose OpenAI-compatible endpoints. Both work as drop-in replacements for https://api.openai.com/v1/chat/completions.

Ollama runs its API on port 11434 by default. It starts automatically at boot via launchd, which means your local AI is always available without launching an app. Any tool that speaks the OpenAI API format (Open WebUI, Continue, Aider, custom scripts) can talk to Ollama immediately.

curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "llama3.1", "messages": [{"role": "user", "content": "Hello"}]}'

LM Studio runs its server on port 1234. You need to launch the app and explicitly start the server (or use lms server start from the CLI). As of version 0.4.0, it also offers an Anthropic-compatible API endpoint, which is useful if you’re building tools that target Claude’s message format.

Winner: Ollama for always-on background service. LM Studio for the extra Anthropic API compatibility.

Vision models

Ollama supports vision models natively. Pull llama3.2-vision, llava, or gemma3 and pass images through the API or the CLI. It handles the multimodal routing automatically. No extra files needed.

ollama run llama3.2-vision "What's in this image?" ./photo.jpg

LM Studio also supports vision models through its unified MLX engine. Models like Gemma 3 and Pixtral work with both text and image inputs. The recent architecture unification means vision models now get prompt caching too, which gives a 25x speedup on follow-up queries with the same image.

Winner: LM Studio for repeat queries on the same image (prompt caching). Ollama for simplicity of the one-line CLI.

Context length handling

On Mac, longer context means more memory consumed by the KV cache. Both tools let you configure the context window, but the defaults and behavior differ.

Ollama defaults to 2048 tokens of context. You can increase it:

ollama run llama3.1 --ctx-size 8192

Or set it in a Modelfile. Ollama’s recent 0.17 update added 8-bit KV cache quantization, which roughly halves the KV cache memory overhead. This helps on memory-constrained Macs.

LM Studio lets you set context length per model in the sidebar. With MLX, it uses a rotating cache that’s efficient for 4K-32K context. The memory impact of longer context is lower through MLX than through GGUF, because MLX handles memory allocation more efficiently on unified memory.

Winner: LM Studio (MLX) for longer contexts with less memory impact. Ollama’s 8-bit KV cache is a good counterweight but doesn’t close the gap entirely.

Feature comparison table

When to use Ollama on Mac

• You’re building apps or scripts that call a local model via API
• You want models available at boot without launching anything
• You’re using Open WebUI, Continue, Aider, or other frontends that need a background server
• Memory overhead matters (Ollama uses less when idle)
• You prefer open-source tools
• You need a fast, no-GUI install on a new machine

The typical Ollama-on-Mac workflow: install once, configure launchd to start it at boot, forget about it. Your local AI is always on port 11434, ready for whatever needs it.

When to use LM Studio on Mac

• You want the fastest possible inference (MLX backend, 20-30% faster)
• Memory is tight and you need the ~50% memory savings from MLX
• You’re browsing and testing new models regularly
• You need vision model support with prompt caching
• You want speculative decoding for faster generation
• You want fine control over generation parameters (temperature, top-p, repeat penalty)
• You’re new to local AI and want a visual interface

The typical LM Studio-on-Mac workflow: launch when you need it, browse models, load one, chat or test via the API, close when done.

When to use both

This is what most Mac power users end up doing. They’re not competing – they run on different ports and don’t interfere with each other.

• Ollama runs as the background server. Apps, scripts, and IDE extensions hit its API 24/7.
• LM Studio is the workbench. You open it to test a new model, compare quantization levels, or run something through MLX that you want the extra speed for.

Some people run the same model in both: Ollama for the always-on API (chat frontends, coding assistants) and LM Studio for interactive sessions where they want MLX speed and the parameter controls.

The bottom line

On Windows and Linux, Ollama and LM Studio are close to interchangeable. On Mac, LM Studio is faster because of MLX. A 20-30% speed gain and 50% memory savings from a different backend is the kind of difference you actually notice while using the tool, not just in benchmarks.

If you have to pick one: use Ollama if you’re a developer who needs an always-on API server. Use LM Studio if you want the best performance and a visual interface.

If you can install both (and you can, they coexist fine): do it. Ollama for the background, LM Studio for the foreground. I’ve run this setup for months and haven’t found a reason to change it.

Related guides

• Ollama vs LM Studio (general)
• Best Local LLMs for Mac in 2026
• Running LLMs on Mac M-Series
• LM Studio Tips & Tricks
• llama.cpp vs Ollama vs vLLM