Four tools dominate local LLM inference. Each serves a different user. This guide breaks down exactly which one fits your situation, with real setup examples and performance notes.

Quick Decision Guide

→ Developer / CLI power user: Use Ollama → Non-technical user wanting a GUI: Use LM Studio → Maximum privacy / offline-only: Use Jan → Absolute beginner, one-click setup: Use GPT4All → VS Code / IDE integration: Use Ollama + Continue.dev → Local AI server for a team: Use Ollama + Open WebUI

Full Feature Comparison

Feature	Ollama	LM Studio	Jan	GPT4All
Interface	CLI + REST API	Full GUI	Full GUI	Full GUI
Platform	Win / Mac / Linux	Win / Mac / Linux	Win / Mac / Linux	Win / Mac / Linux
OpenAI-Compatible API	Yes	Yes	Yes	No
Auto GPU Detection	Yes	Yes	Yes	Yes
Model Manager (GUI)	No (CLI only)	Yes	Yes	Yes
HuggingFace Models	Via GGUF URL	Direct browse	Direct browse	Limited
Built-in Chat UI	No	Yes	Yes	Yes
Offline / Air-Gapped	Yes	Yes	Yes (design goal)	Yes
Custom System Prompts	Via Modelfile	Yes	Yes	Yes
Multi-Model Chat	No	Yes (side-by-side)	No	No
Vision / Image Input	Yes (vision models)	Yes	Partial	No
Open Source	MIT	No (free)	AGPL-3.0	MIT
IDE Plugins	Yes (Continue, Cline)	Yes	No	No
Active Development	Very Active	Very Active	Active	Moderate
Best VRAM Management	Auto	Manual sliders	Auto	Auto

Ollama — The Developer Standard

Website: ollama.com | License: MIT | Install size: ~50 MB

Ollama has become the de facto standard for running local LLMs programmatically. It packages models with their configuration, serves an OpenAI-compatible REST API on port 11434, and integrates with virtually every local AI tool.

Why developers choose Ollama

One-command model management: ollama pull llama3.1 — done
OpenAI SDK drop-in: Switch your code from OpenAI to Ollama by changing the base URL
Ecosystem integrations: Open WebUI, Continue.dev, Aider, LangChain, LlamaIndex all connect to Ollama out of the box
Modelfiles: Custom model configurations with system prompts, temperature, and context baked in
Multi-GPU support: Automatic tensor parallelism across multiple GPUs

Ollama setup and usage

bash

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

# Windows: download installer from ollama.com/download

# Run a model interactively
ollama run llama3.1

# Pull without running
ollama pull gemma3:27b

# List downloaded models
ollama list

# Check running models and VRAM usage
ollama ps

Using Ollama as an OpenAI drop-in

python

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama"  # required by the client, not used
)

response = client.chat.completions.create(
    model="llama3.1",
    messages=[{"role": "user", "content": "Explain RAG in one paragraph"}]
)
print(response.choices[0].message.content)

Custom Modelfile (system prompt + parameters)

bash

# Create a Modelfile
cat > Modelfile << 'EOF'
FROM llama3.1:8b
SYSTEM "You are a senior Python developer. Be concise and use type hints."
PARAMETER temperature 0.2
PARAMETER num_ctx 16384
EOF

# Build and run
ollama create python-assistant -f Modelfile
ollama run python-assistant

Weaknesses

No built-in GUI (use Open WebUI separately)
Model discovery requires knowing exact names (no browse UI)
No side-by-side comparison of models

LM Studio — The Best GUI Experience

Website: lmstudio.ai | License: Free, proprietary | Install size: ~200 MB

LM Studio is the most polished local AI application. If you want a ChatGPT-style experience without touching a terminal, this is your tool. It downloads, manages, and runs models through a clean interface.

Why non-technical users choose LM Studio

Built-in model browser: Search and download from HuggingFace directly in the app
Chat interface: Multiple conversation threads, markdown rendering, code highlighting
GPU sliders: Visually control how many model layers go to GPU vs CPU
Local server mode: One-click OpenAI-compatible API for other apps
Side-by-side comparison: Run two models simultaneously and compare answers (unique feature)

LM Studio workflow

Download from lmstudio.ai and install
Open the Discover tab → search for "llama 3.1" → click Download
Switch to the Chat tab → select your model → start chatting
Enable the Local Server tab to expose the API at http://localhost:1234/v1

GPU offload control

LM Studio shows a slider: "GPU Offload Layers." Set it to maximum for your VRAM. If the model doesn't fit fully, partial GPU offload still helps — some layers on GPU is much faster than all-CPU.

Weaknesses

Closed-source (you can't audit the code)
Heavier application footprint than Ollama
Model path management can be confusing when you have many models

Jan — Offline-First and Fully Open Source

Website: jan.ai | License: AGPL-3.0 | Install size: ~150 MB

Jan's entire design philosophy is offline by default, open source always. Unlike LM Studio, every line of Jan's code is publicly auditable. Unlike Ollama, it ships with a built-in GUI. It's the choice for users who won't compromise on privacy or openness.

Why privacy-focused users choose Jan

No telemetry, zero network calls after initial model download
Fully auditable — open source under AGPL
Local server included — OpenAI-compatible API at http://localhost:1337/v1
Extensions system — add capabilities without bloating the core
Clean conversation management — chat history stored locally in plain JSON

Jan setup

Download from jan.ai
Open Jan → Hub tab → browse and download models
Start chatting in the Thread tab
Enable the Local API Server in settings for external app access

Weaknesses

Smaller model library than LM Studio's HuggingFace integration
Slightly less polished UI than LM Studio
Less frequent updates compared to Ollama/LM Studio

GPT4All — The Simplest Entry Point

Website: gpt4all.io | License: MIT | Install size: ~200 MB

GPT4All prioritizes getting you chatting with an AI in under 5 minutes, no terminal required, no configuration needed. It's built for the absolute beginner who just wants to try local AI.

Why beginners choose GPT4All

Double-click installer — no command line ever
Curated model list — only well-tested, beginner-friendly models
Local docs chat — built-in RAG for chatting with your own files
Completely free and open source

Weaknesses

No OpenAI-compatible API (can't use with most dev tools)
Limited to GPT4All's curated model selection (fewer cutting-edge models)
Less actively developed vs Ollama and LM Studio
No vision/multimodal support

Head-to-Head: Real Scenarios

Scenario 1: "I'm a developer building an app"

Winner: Ollama The OpenAI-compatible API means your existing code works with a one-line change. Integrate with LangChain, LlamaIndex, or any OpenAI SDK in minutes. Add Open WebUI for a management interface.

Scenario 2: "I want to replace ChatGPT for daily use"

Winner: LM Studio Best chat UI, easiest model discovery, no setup friction. For non-developers who want to chat locally, nothing is more polished.

Scenario 3: "I work with sensitive legal/medical data"

Winner: Jan Fully auditable code, no telemetry, offline by design. When you need to prove to clients or regulators that no data leaves the premises, open-source software you can audit is the only defensible choice.

Scenario 4: "I want to use AI in VS Code"

Winner: Ollama + Continue.dev Install Ollama, then add the Continue extension to VS Code. You get AI code completion and chat inline in your editor, powered by any Ollama model.

bash

# Setup for VS Code integration
ollama pull qwen2.5-coder:7b
# Install Continue extension from VS Code marketplace
# Configure Continue to use ollama at http://localhost:11434

Winner: Ollama + Open WebUI Run Ollama on a server, deploy Open WebUI with user accounts and SSO. Everyone on the team gets a private ChatGPT-style interface backed by your local hardware.

bash

# On your server
OLLAMA_HOST=0.0.0.0:11434 ollama serve

# Deploy Open WebUI with multi-user support
docker run -d -p 3000:8080 \
  -e OLLAMA_BASE_URL=http://your-server:11434 \
  -e WEBUI_AUTH=true \
  -v open-webui:/app/backend/data \
  ghcr.io/open-webui/open-webui:main

Performance Comparison

All four tools use llama.cpp under the hood (for GGUF models), so raw inference speed is essentially identical given the same model and quantization. Ollama 0.35 also added a /v1/systemone endpoint for decision models — see the Nimble and Tev1 setup guide and decision models explained. The differences between the tools are in:

Startup time: Ollama (fastest, daemon pre-loaded) > LM Studio > Jan > GPT4All
VRAM management: All auto-detect GPU; LM Studio gives most manual control
API latency: Ollama (lowest overhead) > LM Studio / Jan > GPT4All (no API)

Frequently Asked Questions

Can I use multiple tools at the same time? Yes, but they can't share the same GPU memory at once. Running Ollama and LM Studio simultaneously will cause VRAM conflicts. Use one at a time for inference.

Which tool supports the most models? LM Studio (full HuggingFace browse) and Ollama (GGUF URL import + registry) tie for the most model options. GPT4All has the smallest curated selection.

Does LM Studio send my data anywhere? LM Studio has a privacy policy and may collect anonymized usage telemetry. The actual model inference is local. If you want zero-telemetry guarantees, use Jan or Ollama (both open source with auditable network behavior).

Is Ollama's API actually OpenAI-compatible? Yes — it implements the /v1/chat/completions, /v1/completions, and /v1/models endpoints. Most code using the OpenAI Python or JavaScript SDK works with a base_url change. A few advanced features (function calling schemas, fine-tuning endpoints) may differ.

Which tool is best for vision models? Ollama and LM Studio both support vision models (Llama 3.2 Vision, Gemma 3). Open WebUI makes it easiest with drag-and-drop image uploads into chat.

→ Installing Ollama
→ Complete Beginner's Guide
→ System Prompts 101
→ Set Up a Local API Server
→ True Offline / Air-Gapped Setup
→ Inference Engines Compared — beyond Ollama and LM Studio: vLLM, SGLang, MLX and the rest, and when you need them

← PreviousChatbot Web Apps Next →Inference Engines Compared

Quick Decision Guide

Full Feature Comparison

Ollama — The Developer Standard

Why developers choose Ollama

Ollama setup and usage

Using Ollama as an OpenAI drop-in

Custom Modelfile (system prompt + parameters)

Weaknesses

LM Studio — The Best GUI Experience

Why non-technical users choose LM Studio

LM Studio workflow

GPU offload control

Weaknesses

Jan — Offline-First and Fully Open Source

Why privacy-focused users choose Jan

Jan setup

Weaknesses

GPT4All — The Simplest Entry Point

Why beginners choose GPT4All

Weaknesses

Head-to-Head: Real Scenarios

Scenario 1: "I'm a developer building an app"

Scenario 2: "I want to replace ChatGPT for daily use"

Scenario 3: "I work with sensitive legal/medical data"

Scenario 4: "I want to use AI in VS Code"

Scenario 5: "I want to share local AI with my whole team"

Performance Comparison

Frequently Asked Questions

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