LM Studio vs Ollama — same weights, two very different front doors.
Both run open-weight models on your own hardware, and on one machine both can load the same GGUF file. LM Studio is a desktop app with a model browser, a load-time context slider and an MLX engine that exists only on Apple Silicon. Ollama is an MIT-licensed background service, wired into more third-party tools. The clouds differ more than the apps: LM Studio publishes per-token rates, Ollama sells $20/mo subscriptions without saying how many tokens that buys.
| Category | Winner | Margin |
|---|---|---|
| First hour · install to first token | ALM Studio | LM Studio ships a model browser, a hardware-fit estimate and a chat window in one installer; Ollama assumes you already know which tag to pull |
| Licence · what you are allowed to do with it | BOllama | Ollama is MIT — fork it, ship it, embed it; LM Studio is proprietary software under an EULA that forbids combining it with copyleft code |
| Cost at work · commercial use of the app | ·Tie | Both cost nothing in a company; LM Studio dropped its separate commercial licence on Jul 8, 2025 and now grants internal business use in the same EULA |
| Apple Silicon · M-series Macs | ALM Studio | LM Studio carries both llama.cpp and MLX and can filter downloads to MLX builds with lms get --mlx; Ollama documents no MLX path |
| Old hardware · what still boots | BOllama | LM Studio requires macOS 14+ on Apple Silicon and drops Intel Macs entirely; Ollama still supports NVIDIA cards back to compute capability 5.0, which reaches the GTX 750 |
| Context defaults · the silent truncation trap | ALM Studio | Ollama documents a 4K default under 24 GiB of VRAM — the single most common cause of an agent 'forgetting'; LM Studio puts the number on a slider when the model loads |
| API surface · what a client can call | ALM Studio | Both speak OpenAI and Anthropic; LM Studio adds a native /api/v1 with stateful chats, MCP, auth tokens and load/unload endpoints |
| Serving many requests · concurrency knobs | BOllama | Ollama documents OLLAMA_NUM_PARALLEL, OLLAMA_MAX_LOADED_MODELS and a 512-request queue; LM Studio's server is built around one operator at a keyboard |
| Third-party support · being the default option | BOllama | Ollama's docs carry integration pages for Claude Code, Codex, Zed, VS Code, JetBrains, n8n and two dozen more; it is the local backend most tools ship with |
| Hosted models · when the laptop is not enough | ·Depends | LM Studio sells per-token credits and publishes the rate card; Ollama sells $20/mo Pro and describes the allowance as '50x more cloud usage than Free' |
| Price transparency · knowing what a token costs | ALM Studio | We joined LM Studio's cloud card to our snapshot of vendor list prices across all 8 models it resells; Ollama publishes no per-token figure to join against |
| Best overall · for most people, most of the time | ·Depends | A person who wants to chat with a local model should install LM Studio; a machine that serves models to other software should run Ollama |
CHOOSE A · LM STUDIO
If a human is going to sit in front of it.
- Model browser — search, size estimate and download without leaving the app
- Visible context — the length is a slider at load time, not an environment variable you learn about after the model forgets
- MLX on Macs — a second engine built for Apple Silicon that Ollama does not offer
- Native REST API — /api/v1 adds stateful chats, MCP and model load/unload on top of the OpenAI and Anthropic endpoints
- Published cloud rates — if you spill over to hosted models, you can price the spill before you pay it
CHOOSE B · OLLAMA
If other software is going to talk to it.
- MIT licence — no EULA to read before you embed it in a product or an internal platform
- Runs as a service — a daemon on :11434 that starts with the machine and needs no window open
- The default local backend — its docs carry integration pages for Claude Code, Codex, Zed, VS Code, JetBrains, Cline, n8n and more
- Concurrency documented — parallel requests, multiple loaded models and queue depth are all environment variables
- Wider hardware floor — NVIDIA GPUs back to compute capability 5.0, plus Intel Macs that LM Studio no longer supports
| Aspect | LM Studio | Ollama |
|---|---|---|
| The app itselfwhat you pay to download and run it | $0 Desktop app for macOS, Windows and Linux, no account required to run local models | $0 CLI and service for macOS, Windows, Linux and Docker, no account required to run local models |
| Licencewhat the terms let you do with it | Proprietary EULA Source is not published; the terms forbid combining it with open-source products whose licence would force disclosure of LM Studio's own source | MIT Fork it, modify it, redistribute it, ship it inside a commercial product — attribution is the only condition B wins |
| Use inside a companythe licence a workplace needs | $0 since Jul 8, 2025 The EULA grants personal and internal business use; the separate commercial licence and its request form were dropped that day | $0 MIT imposes no distinction between personal and commercial use |
| Hosted models, entrywhen the model will not fit locally | Pay per token Cloud credits with a published per-model rate card, US-hosted with zero data retention by default; you pay for what you send A wins | $20/mo · Pro Or $200 a year. Three concurrent cloud models and '50x more cloud usage than Free' — the allowance is never stated in tokens or requests |
| Hosted models, heavythe next rung up | Same meter, more of it Pay-as-you-go has no tier above it; a heavy month simply costs more, and Bionic agent sessions draw on the same credits | $100/mo · Max Ten concurrent cloud models and five times Pro's usage — but the page marks new signups as paused |
| Teamsseats, SSO, shared billing | Contact sales LM Studio Enterprise is a form, not a price; nothing about seats or features is published | $25/seat/mo Five-seat minimum, marked introductory pricing; adds US/Europe hosting, zero retention, shared billing and priority support, with SSO listed as coming soon B wins |
| What you actually pay locallythe bill neither vendor sends | Hardware + power 16 GB of RAM recommended, 4 GB of dedicated VRAM on Windows; the model file is the only other cost and it is zero | Hardware + power Identical: the same GGUF file, the same GPU, the same electricity. Neither tool changes the cost of the machine under it |
| Model weightsthe thing you are actually running | $0 Open weights from Hugging Face; lms get takes a full Hugging Face URL and --mlx filters to Apple Silicon builds | $0 Open weights from Ollama's own library or Hugging Face; the same file can be loaded by either tool |
| Capability | LM Studio | Ollama |
|---|---|---|
| Primary interface | ✓ Desktop GUI | ✓ CLI + background service |
| Headless deployment | ✓ llmster daemon | ✓ Native — it is a daemon |
| Licence | Proprietary | ✓ MIT |
| Free for commercial use | ✓ Since Jul 8, 2025 | ✓ Always |
| GGUF models | ✓ via llama.cpp | ✓ via llama.cpp |
| MLX models | ✓ Apple Silicon only | ✗ |
| Pull straight from Hugging Face | ✓ lms get <hf url> | ✓ own library + hf.co tags |
| OpenAI-compatible endpoint | ✓ :1234/v1 | ✓ :11434/v1 |
| Anthropic Messages endpoint | ✓ /v1/messages (0.4.1) | ✓ /v1/messages |
| OpenAI Responses endpoint | ✓ /v1/responses | ✓ /v1/responses |
| Native REST API | ✓ /api/v1 — stateful chats, MCP, auth | ✓ /api — generate, chat, embed, pull |
| Model load/unload over HTTP | ✓ download, load, unload | ~ keep_alive controls unload only |
| MCP servers | ✓ in-app and via API | ~ through the client, not the server |
| Speculative decoding | ✓ draft_model in the request | ✗ not exposed |
| Default context length | Slider at load time | 4K under 24 GiB VRAM |
| K/V cache quantization | ~ engine-level | ✓ OLLAMA_KV_CACHE_TYPE q8_0 / q4_0 |
| Parallel requests per model | ~ single-operator design | ✓ OLLAMA_NUM_PARALLEL, default 1 |
| Multiple models loaded at once | ✓ JIT loading per request | ✓ 3 × GPU count by default |
| Request queue when busy | ~ undocumented | ✓ 512 deep, then HTTP 503 |
| macOS support | Apple Silicon, macOS 14+ | ✓ Apple Silicon and Intel |
| Windows support | ✓ x64 (AVX2) + Snapdragon X | ✓ x64 + ARM |
| Linux support | ✓ AppImage, Ubuntu 20.04+ | ✓ install script + Docker image |
| NVIDIA floor | 4 GB VRAM recommended | ✓ compute capability 5.0, driver 550+ |
| Vision models | ✓ | ✓ |
| Embeddings endpoint | ✓ /v1/embeddings | ✓ /api/embed + /v1/embeddings |
| Tool calling | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Built-in agent | ✓ Bionic | ✗ — you bring the agent |
| Hosted models | ✓ per-token credits | ✓ subscription, usage unstated |
| Documented third-party integrations | ~ clients find it via the OpenAI endpoint | ✓ ~25 integration pages in its own docs |
§ 04 / BENCHMARKS
Why there's no chart here.
No neutral benchmark covers this pair. Neutral leaderboards score models, not the programs that load them. LM Studio and Ollama both run llama.cpp under the hood, so on one machine they can execute the identical GGUF file — a bar chart here would compare a model against itself and call the noise a result. Throughput does differ, but it moves with your GPU, quantization, context length and batch size, which makes any single number a description of our test box rather than of the software. We compare what is stable instead: licences, published prices, hardware floors, defaults and API surface.
A · LM STUDIO
LM Studio
A desktop application that treats local inference as something a person does, not something a server does.
Strengths
- Discovery is built in — the model browser searches Hugging Face, estimates whether a file fits your machine and downloads it without a terminal
- Two engines — llama.cpp everywhere, plus MLX on Apple Silicon, which is the format Apple's own tooling targets
- The API kept growing — 0.3.29 added OpenAI's /v1/responses, 0.4.0 added a native /api/v1 with stateful chats and MCP, 0.4.1 added Anthropic's /v1/messages
- Headless when needed — llmster packages the same core as a daemon for servers and CI with no GUI dependency
- Speculative decoding is a request field — pass a
draft_modeland the response carries a stats object showing what it bought you - Its cloud publishes rates — a per-model table in dollars per million tokens, which is rare among tools that resell inference
Weaknesses
- Proprietary — you cannot read it, fork it or vendor it into your own product, and the EULA specifically blocks combining it with copyleft code
- Apple Silicon or nothing on Mac — Intel Macs are not supported, and macOS 14 is the floor
- Built for one operator — there is no documented equivalent of Ollama's parallelism and queue-depth controls
- Enterprise is a contact form — no seat price, no published feature list
Best for
- Trying local models for the first time without learning a CLI
- Apple Silicon machines where MLX builds are available
- Anyone who wants to see the context length before the model silently truncates
- Developers who want MCP and stateful chats from the inference server itself
B · OLLAMA
Ollama
A background service that makes a local model look like an API endpoint to everything else on the machine.
Strengths
- MIT licensed — the whole Go codebase, with no distinction between what you may do at home and at work
- It is the default — its own docs carry integration pages for Claude Code, Codex, opencode, Cline, Zed, VS Code, JetBrains, Xcode, n8n and more
- Speaks both dialects — OpenAI-compatible and Anthropic Messages, so Claude Code points at it with two environment variables
- Serving knobs are documented — parallel requests, loaded-model count, queue depth and K/V cache quantization are all environment variables
- Wide hardware floor — NVIDIA back to compute capability 5.0, AMD, Apple Silicon, Intel Macs, plus an official Docker image
- Cloud models look local — a
:cloudtag runs on Ollama's hardware through the same CLI and API
Weaknesses
- The 4K default — under 24 GiB of VRAM a model gets a 4K window unless you set it, which is the usual explanation for an agent losing the plot mid-task
- Parallelism costs memory linearly — its own docs note that RAM scales by parallel requests times context length, because each slot gets its own window
- Cloud usage is unquantified — 'light usage' and '50x more' are the only published units, so you cannot price a month before you buy it
- No MLX — on a Mac you get the llama.cpp path only
Best for
- Serving a model to editors, agents and scripts on the same machine
- Anything that will be embedded, forked or shipped, where MIT matters
- Linux boxes and containers, where a daemon is the natural shape
- Older NVIDIA cards and Intel Macs that LM Studio no longer supports
01
Trying a local model for the first time on a MacBook
An M-series Mac with 16 GB of unified memory, no CUDA, no appetite for a terminal session that ends in a build error.
Reasoning: LM Studio installs as one application, shows which quantizations fit in 16 GB before anything downloads, and offers MLX builds that exist only on this hardware. The context length is a slider you set while loading, so the first long conversation does not quietly fall off a cliff.
Picked
LM Studio
Runner-up: Ollama, if the plan is to script it within the week
02
Pointing Claude Code at a model on your own box
A developer wants an agent CLI to talk to local weights instead of a metered API, ideally without a window open.
Reasoning: Ollama documents the exact path: an Anthropic-compatible /v1/messages endpoint, two environment variables, and a service that is already running. LM Studio added the same endpoint in 0.4.1, so this is close — but Ollama's integration page for Claude Code means the wiring is a documented feature rather than an inference.
Picked
Ollama
Runner-up: LM Studio with llmster, if you want the GUI on the same machine
03
A small studio standardising on one local runtime
Six designers and engineers, mixed Macs and Windows machines, an internal rule that everything shipped must be licence-clean.
Reasoning: MIT removes the review entirely: nobody has to read an EULA before embedding Ollama in an internal tool or a build script. Ollama's Team plan at $25 a seat with a five-seat minimum also gives the studio a hosted fallback with zero data retention when a model will not fit a laptop.
Picked
Ollama
Runner-up: LM Studio, which is also free at work but proprietary
04
Deciding whether a local model is cheaper than the API
The current spend is a few hundred dollars a month on a hosted frontier model, and someone has suggested a GPU instead.
Reasoning: LM Studio is the better instrument for the experiment because it publishes cloud rates per million tokens next to the local option, so the two halves of the comparison sit in the same units. Neither tool tells you what your electricity or your GPU costs — that part you bring yourself, and our calculator holds the API side.
Picked
LM Studio
Runner-up: Ollama, whose cloud tiers are priced per month rather than per token
05
An older gaming PC with a GTX 1080
A Pascal-generation card, 8 GB of VRAM, Windows, and a wish to run a small model without buying anything.
Reasoning: Ollama publishes support back to compute capability 5.0, and the GTX 1080 sits at 6.1 with room to spare. LM Studio's requirements are written around 4 GB of dedicated VRAM and AVX2 rather than a supported-card table, so Ollama is the safer bet on hardware this age.
Picked
Ollama
Runner-up: LM Studio, which will often work but publishes no card list
06
Building an internal tool that ships to colleagues
A Python service that needs a local model behind it, deployed to a handful of Linux servers by CI.
Reasoning: Both can do this now — Ollama has always been a daemon, and llmster gives LM Studio a headless core for servers and CI. Ollama still wins on licence: MIT means the runtime can be bundled, documented and modified with no legal question, and the Docker image makes the deployment step a single line.
Picked
Ollama
Runner-up: LM Studio with llmster, where the team already runs the desktop app
Frequently asked.
Common questions about this comparison, with sources where they matter.
Q · 01 Is LM Studio or Ollama faster? +
On the same machine, running the same GGUF file, neither is meaningfully faster — both execute it through
llama.cpp. Speed differences come from what you loaded and how: quantization, context length, how many layers fit in VRAM, and whether flash attention is on. The one structural exception is Apple Silicon, where LM Studio can run MLX builds of a model instead of GGUF; those are compiled for Apple's own framework and can be quicker on M-series hardware. Ollama documents no MLX path.Q · 02 Is LM Studio free for commercial use? +
Yes. LM Studio dropped its separate commercial licence on July 8, 2025; there is no form to fill in and no fee. The current terms grant a licence to use the software "solely for Your personal and / or internal business purposes", and note that when an employee uses it at work the employer is the customer. That is a permission, not open source: LM Studio remains proprietary, and the terms forbid combining it with open-source products in a way that would require its own source to be disclosed. Ollama is MIT, which carries no such restriction.
Q · 03 Why does my local model forget things in Ollama? +
Almost always the default context length. Ollama's own documentation sets the window from available VRAM: 4K under 24 GiB, 32K between 24 and 48 GiB, 256K above 48 GiB. A 4K window is small enough that a coding agent loses the earlier half of its own conversation. The fix is explicit — set the slider in the app, or start the server with
OLLAMA_CONTEXT_LENGTH=64000, which the docs recommend as a floor for agents, web search and coding tools. Raising it costs memory, so check that the model still fits. LM Studio surfaces the same number as a slider when the model loads, which is why fewer people meet this problem there.Q · 04 Which one can serve several users at once? +
Ollama, within limits. It documents
OLLAMA_NUM_PARALLEL (default 1), OLLAMA_MAX_LOADED_MODELS (three per GPU) and a 512-request queue that returns HTTP 503 when full. The catch is in the same docs: memory scales by parallel requests multiplied by context length, because each slot gets its own window. That is fine for a small team and wrong for a product. If you are serving real concurrency, the tool built for it is vLLM — see vLLM vs Ollama.Q · 05 Do both tools also sell hosted models? +
Yes, and they price them differently. LM Studio sells pay-as-you-go cloud credits with a published per-model rate card in dollars per million tokens. Ollama sells subscriptions: Free with one concurrent cloud model and "light usage", Pro at $20/mo or $200/year with three concurrent models and "50x more cloud usage", Max at $100/mo with ten concurrent models and new signups paused, and Team at $25 per seat per month with a five-seat minimum. Neither the Free nor the Pro allowance is stated in tokens, so a monthly bill cannot be estimated in advance the way LM Studio's can.
Q · 06 Is LM Studio's cloud a markup on the model vendors' own prices? +
Partly, and not in one direction. We joined its rate card to our own snapshot of vendor list prices for all eight models it resells. Three match the vendor to the cent — Kimi K3, Kimi K2.6 and GLM-5.3-Flash. GLM-5.2 sits above Zhipu's published list. Both DeepSeek rows sit below DeepSeek's own list, and V4 Flash is cheaper than DeepSeek's own off-peak discount rate. So it is a real rate card rather than a fixed margin, and it is worth checking a specific model against the vendor's own price before assuming either way.
Q · 07 Can I use the same downloaded model in both? +
Yes, if it is GGUF. Both tools run GGUF through llama.cpp, so one file can serve both — LM Studio takes a Hugging Face URL through
lms get, and Ollama can pull the same weights from its own library or from Hugging Face tags. They keep separate model directories by default, so you will either point one at the other's folder or accept two copies on disk. MLX builds are the exception: those are LM Studio only.Q · 08 Which should I install if I only want one? +
Install LM Studio if the answer to "who talks to the model?" is "me". Install Ollama if the answer is "my editor, my agent, my scripts". The reason the question is worth asking rather than benchmarking is that both are free, both run the same weights, and installing the second one later costs nothing but disk.