LM Studio vs Ollama: Which Local LLM Runner Fits Your Hardware?

What is actually the same

LM Studio and Ollama load the same GGUF model files through the same open source engine stack, so a given model needs the same memory and gives the same answers in either one. LM Studio's own documentation describes it as running models on Mac, Windows, and Linux "using llama.cpp," with Apple's MLX engine as a second option on Apple Silicon. Ollama started on llama.cpp, built its own engine on the same GGML library in 2025, and now runs llama.cpp alongside Apple's MLX.

That means the hardware calculator applies to both, and so does the quantization ladder (the quantization calculator shows every level side by side). Both also expose an OpenAI-compatible API on localhost, which means any program written against OpenAI's chat format can point at either one by changing a base URL. LM Studio serves it on port 1234 (http://localhost:1234/v1) and Ollama on port 11434 (http://localhost:11434).

If you only wanted to know whether one of them runs models "better," that's the answer: neither. The choice is about workflow, memory behavior, platform support, and licensing.

Speed: the numbers, and why they flip

Speed differences between LM Studio and Ollama come from how current each tool's bundled build of llama.cpp is and how much overhead the app adds, because both share the same engine. InventiveHQ ran a recent public test, published June 1, 2026 and updated in August: Qwen2.5-Coder-7B at Q4, twelve prompts across code, math, reasoning, summarization, and chat, measured end to end through each runner's API on two machines. All figures are tokens per second.

Two things stand out. On the NVIDIA card, LM Studio was essentially at raw llama.cpp speed while Ollama trailed by about 10 percent. On the Mac, the order reversed and LM Studio was the one behind, by 29 percent against llama.cpp. The authors attribute the Mac gap to LM Studio's bundled llama.cpp lagging upstream at the time of the test. It tested LM Studio's llama.cpp engine with a GGUF model, not its MLX engine. The Mac figures also predate Ollama 0.40.0's switch to MLX by default, so treat them as a snapshot. That is the right way to read any runner benchmark: the gap belongs to a specific version on a specific date, and it closes or reopens with the next update. Don't pick a tool on a 10 percent speed difference. Pick it on the things below, which don't change release to release.

One Mac-specific note. Ollama has been moving Macs onto Apple's MLX framework since a March 2026 preview, which reported 1,851 tokens per second prefill and 134 tokens per second decode on a Qwen3.5 35B-A3B coding model. With version 0.40.0 in September 2026, models the MLX runtime supports run on MLX by default on Apple Silicon. The original preview required more than 32GB of unified memory; the September release notes don't restate a minimum, so on a smaller Mac, check what ollama ps reports. LM Studio has run MLX-format models on any Apple Silicon Mac since version 0.3.4 in October 2024. See the Apple Silicon guide for which Macs make sense for local AI at all.

Memory behavior: the difference that trips people up

Memory behavior is how long each tool keeps a model loaded and how much context it reserves, and it is where the two tools differ most in daily use. Both load a model into VRAM or unified memory; they disagree about when to let go of it.

With Ollama, a model you chatted with ten minutes ago has to reload before it answers, which on a 14B model from a SATA SSD can mean a noticeable pause. People read that pause as "Ollama is slow" when it's really "Ollama unloaded the model." Setting OLLAMA_KEEP_ALIVE=-1 keeps it resident. With LM Studio, the opposite problem shows up: you load a 13GB model in the chat window, forget about it, open a game two hours later, and wonder where your VRAM went. Eject the model or let the server's TTL handle it.

Ollama's context default deserves its own warning. On any GPU under 24GiB of VRAM, Ollama starts models with a 4,096 token context window unless you change it, and its own docs say coding tools and agents "should be set to at least 64000 tokens." A 4K window silently drops the start of a long conversation. Change it with the slider in the Ollama app's settings or with OLLAMA_CONTEXT_LENGTH=64000 ollama serve, and confirm with ollama ps, which shows the context in use and whether the model is fully on the GPU. LM Studio shows the context slider every time you load a model, which is clumsier, but you can't forget it's there.

GPU and platform support

GPU support in LM Studio and Ollama covers NVIDIA, AMD, and Apple Silicon, but the edges differ, and the edges are exactly where people with older or unusual hardware live.

Intel Macs are out for LM Studio entirely, so if you are on a 2019 iMac, Ollama is the only one of the two that will install. On the AMD side, Ollama's ROCm requirement is specific (ROCm v7, and on Windows only certain RX 7000 and W7000 series cards), while its Vulkan backend catches most other Radeon cards at lower performance. LM Studio lets you pick between its Vulkan and ROCm runtimes from inside the app, which is easier to experiment with than reinstalling a driver stack. The hardware buying guide covers which GPUs are worth the money in the first place; this table is only about whether the software will talk to the card you already own.

If you are on Windows and going the Ollama route, read running Ollama on Windows first. There's an install-order mistake that breaks GPU detection, and it's much easier to avoid than to fix.

Licensing and openness

Ollama is open source under the MIT license, which means you can read it, modify it, and bundle it into something you sell. LM Studio is closed source. It has always been free for personal use, and on July 8, 2025 its maker removed the separate commercial license requirement, writing that "starting today, LM Studio is free to use both at home and at work." The current terms, dated August 23, 2026, grant a license for "personal and / or internal business purposes" and add that "certain features of the Software may be offered for a fee," which is where the company's paid Teams and Enterprise plans sit.

For most readers of this site that distinction won't matter much. You are running a model on your own machine, and both tools let you do that for free. It matters if you are building a product on top of the runner, want to audit the code that handles your prompts, or work somewhere with a policy about closed source software touching internal data. In those cases Ollama is the simpler conversation to have.

The models themselves carry their own licenses regardless of which tool loads them. Llama, Gemma, Qwen, and DeepSeek each have different terms, and LM Studio's documentation makes the same point: check the model's license before you use it for work.

LM Studio vs Ollama by hardware tier

Recommendation, based on the documented behavior above and the same tiers as the best local LLM by hardware tier page.

8GB GPU or an 8GB to 16GB laptop

LM Studio, unless you are on Linux. At this tier you are running 7B and 8B models, often with partial GPU offload, and LM Studio's loader shows you the offload slider and the context slider before anything loads. That visibility prevents the two most common beginner mistakes: loading a model that does not fit, and running out of memory to a context window you did not know you had set. Ollama hides both decisions.

12 to 16GB GPU

Either. This is the tier where the tools are closest, because 14B models at Q4 fit with headroom and neither memory policy bites. Pick on workflow: LM Studio if you mostly chat, Ollama if you mostly want a local model for a coding assistant or another program to call. Ollama's one-line install and automatic GPU detection make it the lower-friction service.

24GB GPU

Ollama, with the context length set properly. At 24GiB of VRAM Ollama's default context jumps to 32K, which is a much saner starting point for a 32B model, and the ability to keep up to three models resident means you can run a chat model and a small embedding model side by side without juggling. LM Studio works fine here too; you'll just manage the second model by hand.

Apple Silicon

Either works well now. LM Studio has run MLX models since version 0.3.4 in October 2024, on any Apple Silicon Mac; Ollama made MLX its default for supported models in September 2026. Pick on workflow, as in the 12 to 16GB tier. Either way, an Intel Mac is a poor fit for both; LM Studio will not install, and Ollama's macOS docs list x86 as "CPU only."

Linux box or home server

Ollama. It installs as a systemd service, runs headless by design, and the Docker image makes it easy to keep off the host. LM Studio's llmster daemon now covers the same ground, but Ollama has been the default for this job for longer and more guides assume it. If several programs will hit the same model at once, LM Studio 0.4's default of four parallel slots is worth a look; raise OLLAMA_NUM_PARALLEL on Ollama for the same effect.

Running both at once

Yes. The two tools listen on different ports and keep separate model folders (Ollama in ~/.ollama/models on macOS, /usr/share/ollama/.ollama/models on Linux, or C:\Users\%username%\.ollama\models on Windows; LM Studio in ~/.lmstudio/models). Nothing conflicts. The costs are disk space, because a model you download in both tools takes twice the space, and memory, because a model left loaded in LM Studio while Ollama answers a request from another program leaves both fighting for the same VRAM. If you run both, make a habit of ejecting LM Studio's model when you switch, or set a short TTL on its server.

A reasonable setup for someone who wants to learn is to install Ollama as the always-on service and LM Studio as the window you open when you want to try a new model and watch what it does. Once you know which models you actually use, you can consolidate. If you have never run a model locally at all, the beginner guide walks through the first install step by step.