Ollama vs LM Studio vs Jan: Best Local LLM Runner in 2025

Running LLMs locally has gone from “technically possible but painful” to genuinely practical in the past 18 months. The hardware requirements dropped, quantization improved, and a handful of tools made the setup experience nearly frictionless.

Ollama, LM Studio, and Jan are the three most popular local LLM runners in 2025. They serve different types of users and different use cases — here’s the honest breakdown.

---

Why Run LLMs Locally?

Before the comparison, a quick reminder of why local inference matters:

• Privacy — your data never leaves your machine
• Cost — no per-token API charges for high-volume use
• Latency — no network round-trip for short prompts on fast hardware
• Offline use — works without internet access
• Customization — fine-tuned models, custom system prompts, no content filters

The tradeoff: you’re capped by your hardware. A 70B model still needs serious RAM and VRAM.

---

Ollama

What It Is

Ollama is a command-line tool (with an optional REST API) for running quantized open-source LLMs. It’s designed to feel like Docker for models — pull, run, and build on top of them with a clean API.

Installation

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows
# Download installer from ollama.com

One command, no dependencies to manage. Ollama handles model downloads, quantization selection, and GPU detection automatically.

Running Models

# Pull and run (first run downloads the model)
ollama run llama3.2

# Run with a system prompt
ollama run mistral "Explain monads in plain English"

# List downloaded models
ollama list

# Pull without running
ollama pull qwen2.5-coder:7b

API Compatibility

Ollama exposes an OpenAI-compatible REST API at http://localhost:11434:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama"  # placeholder, not validated
)

response = client.chat.completions.create(
    model="llama3.2",
    messages=[{"role": "user", "content": "Hello!"}]
)

This makes Ollama a drop-in local backend for any app that supports OpenAI’s API — LangChain, LlamaIndex, Cursor, Continue.dev, and hundreds of others.

Model Support

Ollama’s library covers all major open-source models: Llama 3.x, Mistral, Qwen, Phi, Gemma, DeepSeek, Code Llama, and many more. You can also create custom Modelfiles to adjust parameters:

# Modelfile
FROM llama3.2

SYSTEM """
You are a concise technical assistant. Always include code examples.
"""

PARAMETER temperature 0.3
PARAMETER num_ctx 8192

ollama create my-coder -f Modelfile
ollama run my-coder

GPU Support

Ollama automatically uses:

• NVIDIA CUDA (Linux/Windows)
• Apple Metal (macOS with M-series chips)
• AMD ROCm (Linux)
• CPU fallback if no GPU is available

Metal support on M1/M2/M3/M4 Macs is excellent — 7B models run at 30-50 tok/s.

Strengths

• Cleanest CLI experience
• Best developer API (OpenAI-compatible)
• Excellent documentation
• Active model library with regular additions
• Low memory overhead when idle
• Best choice for integrating into other tools and apps

Weaknesses

• No GUI (unless you install a third-party frontend like Open WebUI)
• Less accessible to non-technical users
• Model management is entirely CLI-based

---

LM Studio

What It Is

LM Studio is a desktop application for discovering, downloading, and running local LLMs. It has a full GUI, a built-in chat interface, and an OpenAI-compatible API server. It’s designed for users who prefer a graphical experience.

Installation

Download the installer from lmstudio.ai — available for macOS, Windows, and Linux. No command line required.

User Interface

LM Studio’s GUI includes:

• Discover tab — browse and search models from Hugging Face
• My Models tab — manage downloaded models
• Chat tab — built-in chat interface with per-session parameters
• Local Server tab — start/stop the API server

The model discovery experience is the best of the three — you can search Hugging Face, filter by task, hardware requirements, and quantization level, all within the app.

API Compatibility

LM Studio’s local server is OpenAI-compatible:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:1234/v1",
    api_key="lm-studio"
)

response = client.chat.completions.create(
    model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF",
    messages=[{"role": "user", "content": "Hello!"}]
)

Model Support

LM Studio supports GGUF format models from Hugging Face directly. This is a broader selection than Ollama’s curated library — if a model exists as a GGUF on HuggingFace, LM Studio can run it. The tradeoff is that you need to choose the quantization level yourself (Q4_K_M, Q5_K_M, Q8_0, etc.).

GPU Support

LM Studio supports:

• NVIDIA CUDA — excellent Windows/Linux support with layer offloading
• Apple Metal — excellent macOS M-series support
• AMD — basic support, improving
• CPU — slower but works

The layer offloading slider in the UI lets you manually control how many model layers go to GPU vs CPU RAM — useful for systems with mixed memory setups.

Performance Notes

LM Studio’s performance is comparable to Ollama for the same model and quantization. On Apple Silicon, both are fast. On Windows with NVIDIA GPUs, performance depends heavily on VRAM — LM Studio’s layer offloading controls give you more fine-grained control when VRAM is limited.

Strengths

• Best UI/UX — excellent for non-developers
• Built-in Hugging Face model search and download
• Fine-grained control over inference parameters (temperature, top-p, context length)
• No command line required
• GPU layer offloading slider for mixed hardware

Weaknesses

• Larger application footprint
• Slower startup than Ollama
• Model files stored in GGUF format only
• Less convenient for programmatic/scripting use cases

---

Jan

What It Is

Jan is an open-source desktop application for local AI, built by Menlo Research. It combines a chat interface, model manager, and local API server with a focus on privacy and extensibility. It’s somewhere between LM Studio (GUI-first) and Ollama (developer-first).

Installation

Download from jan.ai — available for macOS, Windows, and Linux. Fully open source (Apache 2.0).

User Interface

Jan’s interface includes:

• Chat — persistent conversation threads with customizable assistants
• Hub — model discovery and download
• Local API Server — toggle on/off with port configuration
• Settings — hardware configuration, privacy controls

The chat experience is more polished than LM Studio for ongoing conversations — Jan stores your history and supports named assistants with persistent system prompts.

API Compatibility

Jan’s local server is OpenAI-compatible on port 1337 by default:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:1337/v1",
    api_key="jan"
)

Model Support

Jan supports GGUF models through its Hub and direct manual import. The model catalog is smaller than LM Studio’s but covers the major open-source models. You can also import models manually by path.

Extensions

Jan has a plugin/extension system that differentiates it from the others:

• Remote model integrations (OpenAI, Anthropic, Groq) alongside local models
• Custom assistant profiles
• Tool integrations (web search, code execution in development)

This makes Jan useful if you want a single interface that switches between local and cloud models depending on the task.

GPU Support

Jan supports NVIDIA CUDA, Apple Metal, and CPU inference. AMD support is limited. Hardware configuration is handled through the settings panel.

Strengths

• Open source — full transparency, no licensing concerns
• Best persistent chat experience with named assistants
• Unified interface for local + remote models
• Extension system for added functionality
• Privacy-first design philosophy
• No telemetry by default

Weaknesses

• Smaller model catalog than LM Studio
• Extension ecosystem is still early
• Performance slightly behind Ollama for pure inference speed
• Less documentation than Ollama or LM Studio

---

Side-by-Side Comparison

Feature	Ollama	LM Studio	Jan
Interface	CLI + API	GUI + API	GUI + API
Installation	One command	Installer	Installer
Model discovery	CLI / ollama.com	Built-in HF search	Built-in Hub
OpenAI API	Yes (port 11434)	Yes (port 1234)	Yes (port 1337)
Model format	GGUF (auto-handled)	GGUF (manual select)	GGUF
GPU support	CUDA / Metal / ROCm	CUDA / Metal / AMD	CUDA / Metal
Apple Silicon	Excellent	Excellent	Good
NVIDIA GPU	Excellent	Excellent	Good
Open source	Yes (MIT)	No (proprietary)	Yes (Apache 2.0)
Telemetry	Opt-out	Opt-in telemetry	None
Remote model support	No	No	Yes (via extensions)
Best for	Developers	Non-developers	Privacy-focused users

---

Performance in Practice

For a 7B model (e.g., Llama 3.2 7B Q4_K_M) on an M2 MacBook Pro:

• Ollama: ~45-50 tok/s
• LM Studio: ~42-48 tok/s
• Jan: ~38-44 tok/s

Differences are marginal and vary by model and quantization. For 13B models, all three become more similar because you’re GPU-bound rather than framework-bound.

On Windows with an RTX 4090 (24GB VRAM), all three perform similarly — the bottleneck is hardware, not the runner.

---

When to Choose Each

Choose Ollama when:

• You’re a developer integrating local LLMs into an application
• You want the cleanest OpenAI-compatible API with minimal overhead
• You’re using tools that have native Ollama support (LangChain, Continue.dev, Open WebUI)
• You prefer CLI-first workflows
• You want the lowest resource usage when the server is idle

Choose LM Studio when:

• You want a no-code setup with a polished GUI
• You want to browse and compare Hugging Face models before downloading
• You need fine-grained control over inference parameters without editing config files
• You’re sharing a machine with non-technical users
• You occasionally use local LLMs for chat rather than integration

Choose Jan when:

• Privacy and open-source licensing matter more than anything else
• You want a unified interface for local and cloud models
• You prefer persistent chat threads with named assistants
• You’re interested in the extension ecosystem for added capabilities
• You want zero telemetry out of the box

---

The Setup Most Developers Actually Use

In practice, many developers run Ollama as the backend (always-on server, OpenAI-compatible API) and Open WebUI as the frontend (browser-based chat interface that connects to Ollama). This gives you the best of both worlds — developer API access and a clean chat UI.

# Install Ollama (backend)
curl -fsSL https://ollama.com/install.sh | sh

# Run Open WebUI (Docker)
docker run -d -p 3000:8080 \
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  ghcr.io/open-webui/open-webui:main

This combination has become the defacto local AI stack for developers who want both programmatic access and a usable chat interface.

---

The Bottom Line

• Ollama for developers who want to integrate local LLMs into their tools and workflows
• LM Studio for users who want a polished GUI experience and easy model discovery
• Jan for privacy-focused users and anyone who wants open-source transparency

All three are actively developed and free to use. Start with Ollama if you’re comfortable with the command line — its ecosystem integrations are unmatched. If you want a GUI without any CLI setup, LM Studio is the most polished option.