Running models locally is not one decision — it’s a workflow decision.

Both Ollama and LM Studio can:

• run LLMs on your machine
• expose OpenAI-compatible endpoints on localhost so your existing app code can point at them

The difference is how they want you to work.

TL;DR (quick pick)

If you’re already building an app that speaks to OpenAI-style endpoints, pick the one you’ll actually keep running.

TL;DR #2: pick by scenario (not preference)

The real difference: workflow shape

Most “local LLM” debates get stuck on models. For these tools, ask a simpler question:

Do you want local inference to feel like a developer dependency (Ollama), or like a desktop product (LM Studio)?

Ollama is a runtime you script around

Ollama’s docs are explicit about the two most common integration paths:

• its native API (example in the quickstart uses POST http://localhost:11434/api/chat)
• an OpenAI-compat layer (examples set base_url='http://localhost:11434/v1/' for OpenAI SDKs)

This makes it a great “local service” to run alongside your dev stack.

LM Studio is a local “model app” with an API on the side

LM Studio is optimized for a desktop workflow: pick models, download them, chat, and tune.

When you need app integration, LM Studio documents OpenAI-compatible endpoints and shows how to reuse existing OpenAI clients by switching the base URL (examples assume port 1234).

Compatibility and system requirements (the part that decides the tool)

Before you pick based on vibes, check whether it even runs on your machine.

LM Studio requirements (from its docs)

• macOS: Apple Silicon only (M1/M2/M3/M4), macOS 14.0+; Intel Macs not supported
• Windows: x64 and ARM supported; AVX2 required on x64
• Linux: x64 and ARM64 supported; Ubuntu 20.04+; distributed as AppImage

Ollama on macOS

Ollama’s macOS download page states it requires macOS 14 Sonoma or later.

If you’re on an Intel Mac, LM Studio is currently a non-starter — and that alone decides the comparison.

OpenAI-compatible endpoints (what you actually care about)

If your goal is “run local models but keep my code the same”, the key is: what endpoints exist?

LM Studio (OpenAI Compatibility Endpoints)

LM Studio’s OpenAI-compatible docs list these supported endpoints:

• GET /v1/models
• POST /v1/responses
• POST /v1/chat/completions
• POST /v1/embeddings
• POST /v1/completions (legacy)

The same page shows how to set your OpenAI client base URL to http://localhost:1234/v1.

Ollama (OpenAI compatibility)

Ollama’s OpenAI compatibility docs include examples for:

• client.chat.completions.create(...) with base_url='http://localhost:11434/v1/'
• client.responses.create(...) with the same base URL

What “OpenAI-compatible” really means in practice

Treat compatibility as an integration convenience, not a promise of identical behavior.

Practical implications:

• you can often keep your client library the same and change only the base URL
• you should validate the endpoints you rely on (/v1/chat/completions vs /v1/responses, embeddings, streaming)
• you should expect some differences in the “edges” (streaming event semantics, tool calling behaviors, model identifiers)

If your app is moving from Chat Completions to Responses, read this first: Chat Completions to Responses API: A Practical Migration Guide.

Setup guide: get a working local API in 10 minutes

This is the practical path most people want: a local model you can call from code.

Option A — Ollama (OpenAI-compatible base URL on 11434)

1. Install Ollama for your OS.
2. Run the interactive menu once:

ollama

3. Hit the native API (example from the Ollama quickstart):

curl http://localhost:11434/api/chat -d '{
  "model": "gemma3",
  "messages": [{ "role": "user", "content": "Hello!" }]
}'

4. If you already use an OpenAI SDK, repoint it:

from openai import OpenAI

client = OpenAI(
  base_url="http://localhost:11434/v1/",
  api_key="ollama",  # required but ignored
)

Option B — LM Studio (OpenAI-compatible base URL on 1234)

Once your LM Studio server is running, you typically only need to repoint your OpenAI client:

from openai import OpenAI

client = OpenAI(
  base_url="http://localhost:1234/v1"
)

Note: some OpenAI client libraries require a non-empty API key even when pointing to localhost. If yours does, set any placeholder key in your environment and keep the base URL change as the real switch.

LM Studio’s docs also provide a cURL example by swapping:

• https://api.openai.com/v1/chat/completions
• → http://localhost:1234/v1/chat/completions

If you’re building apps: prefer /v1/responses sooner

The local tooling story is catching up to the modern OpenAI surface area. The important bit (and the reason this comparison matters in 2026) is that both projects document support for the Responses endpoint:

• LM Studio lists POST /v1/responses as a supported endpoint.
• Ollama’s OpenAI compatibility docs include a “Simple /v1/responses example” using the OpenAI SDK pointed at http://localhost:11434/v1/.

If you’re starting a new local-first app integration, it’s often cleaner to standardize on Responses in your app layer (even when running locally), and treat Chat Completions as legacy compatibility.

Which should you choose? (practical recommendations)

Choose Ollama if:

• your workflow is terminal-first (scripts, Makefiles, local dev services)
• you want “local inference” to behave like a background dependency
• you want a native API and an OpenAI-compat layer side-by-side

Choose LM Studio if:

• you want the best desktop UX for downloading and managing models
• you want OpenAI-compatible endpoints without living in CLI land
• you’re on supported hardware (especially Apple Silicon on macOS 14+)

A lightweight “local-first” checklist (so you don’t get surprised)

• Hardware reality: LM Studio’s docs list Apple Silicon + macOS 14+ and “Intel Macs not supported”; Windows x64 needs AVX2; Linux notes Ubuntu 20.04+. Verify before you build a workflow around it.
• Port sanity: docs assume 11434 for Ollama and 1234 for LM Studio’s OpenAI-compat examples. Make your app config explicit so teammates don’t guess.
• Endpoint choice: decide whether your app talks to /v1/chat/completions or /v1/responses, then stick to it.
• Integration contract: treat the tool’s docs as your contract; don’t assume “OpenAI-compatible” implies every SDK feature works the same way.

A clean mental model for teams

If you’re picking a default for a mixed team:

• developers who ship code tend to prefer Ollama (it feels like infrastructure)
• power users and analysts tend to prefer LM Studio (it feels like a product)

And if you’re doing local training or fine-tuning work, this is adjacent but not the same job — see: Unsloth Studio: No-Code Local LLM Training.

One warning: “OpenAI-compatible” is not “feature-complete”

Both tools aim to reduce integration friction by implementing OpenAI-style endpoints, but you should still treat the local server as:

• compatible with specific endpoints (use the docs as the contract)
• compatible with a subset of semantics (especially around streaming/events and tool calling)

If you’re migrating app code between Chat Completions and Responses, this helps: Chat Completions to Responses API: A Practical Migration Guide.

Sources

• Ollama Quickstart (availability, native API example)
• Ollama OpenAI compatibility (base URL, chat + responses examples)
• Download Ollama (macOS requirement)
• LM Studio System Requirements
• LM Studio OpenAI Compatibility Endpoints (supported endpoints, base URL)