Current
16 GB RAM
Best for compact 4B to 8B models and short local assistant sessions.
16 GB RAMNo dedicated GPUchat
Local AI tool picker
Ollama vs LM Studio vs Jan vs Open WebUI: Which Local AI Tool Should You Install?
Choose Ollama, LM Studio, Jan, or Open WebUI for local LLMs by RAM, GPU, desktop UI, API server, privacy, and self-hosting needs.
Best first download
Qwen3 8B
Model rows
76
local model rows
Updated
Jun 28, 2026
metrics snapshot
Families
15
model families
Hardware simulator
Simulate a GPU upgrade before downloading a 20 GB model.
Compare the machine you have with the machine you might buy, then reverse-check the hardware needed for a target model.
Now fits
37
Target fits
59
Upgrade comparison
Target
RTX 4090
Good for strong 14B to 32B local coding and reasoning models.
64 GB RAM24 GB VRAMreasoning
Models unlocked by this upgrade
These did not fit or stretch on the current machine, but become realistic on the target.
Qwen3 30B-A3B
30B MoE / Q4 about 18 GB / Efficient MoE reasoning
Status
Fits comfortably
Score
95/100
Qwen3 32B
32B / Q4 about 20 GB / Workstation-grade open model
Status
Fits comfortably
Score
94/100
Qwen3 14B
14B / Q4 about 9 GB / Higher-quality local reasoning
Status
Fits comfortably
Score
90/100
DeepSeek-R1 Distill Qwen 32B
32B / Q4 about 20 GB / Serious local reasoning
Status
Fits comfortably
Score
88/100
DeepSeek-R1 Distill Qwen 14B
14B / Q4 about 9 GB / Better local math and logic
Status
Fits comfortably
Score
88/100
Model requirement planner
Qwen3 8B
Strong everyday pick for multilingual chat, coding, and reasoning on consumer hardware.
RAM floor
16 GB
VRAM target
6 GB
Q4 size
5.2 GB
Install hint
ollama run qwen3:8bMinimum comfortable hardware paths
First exact: 16 GB RAM16 GB RAM
16 GB RAM / no dedicated GPU / usable model memory 11 GB
Fits comfortably
16 GB Mac
16 GB RAM / no dedicated GPU / usable model memory 11 GB
Fits comfortably
32 GB RAM
32 GB RAM / no dedicated GPU / usable model memory 17 GB
Fits comfortably
RTX 3060 Ti
32 GB RAM / 8 GB VRAM / usable model memory 8 GB
Fits comfortably
RTX 3070
32 GB RAM / 8 GB VRAM / usable model memory 8 GB
Fits comfortably
RTX 4060
32 GB RAM / 8 GB VRAM / usable model memory 8 GB
Fits comfortably
Qwen3 8B
AlibabaApache 2.0Default open local assistant
Strong everyday pick for multilingual chat, coding, and reasoning on consumer hardware.
Parameters
8B
Q4 size
5.2 GB
RAM floor
16 GB
VRAM target
6 GB
Performance
62/100
Pulls
31.5M
chatcodingreasoningWorkload match
Fit order
Performance + adoption + fit
#1
Match score
73/100
Adoption
94/100
Install hint
ollama run qwen3:8bTool answer
Ollama vs LM Studio vs Jan vs Open WebUI
Install Ollama first if you want repeatable commands, local APIs, and easy benchmarking. Install LM Studio first if you want a polished desktop chat and model browser. Try Jan when you want an open-source desktop assistant workflow. Use Open WebUI when you want a browser UI, shared workspace, or self-hosted layer on top of Ollama or OpenAI-compatible endpoints.
Updated with local model metrics
2026-06-28
Pick the model size with the simulator first, then choose the runtime or UI layer.
Decision matrix
Which local AI app should you install first?
Ollama
I want the simplest repeatable test and one command to rerun later.
It is the cleanest path for command-line pulls, local API checks, scripts, and reproducible test notes.
LM Studio
I want a desktop chat app, model search, and manual control over loaded models.
It is friendlier when the user wants a visual workflow before caring about automation.
Jan
I want an open-source desktop assistant and may mix local and cloud models.
It fits people who want a ChatGPT-like desktop workspace while keeping a local-first option open.
Open WebUI
I want a web UI for a home server, team machine, or always-on local model box.
It is a better second layer when the model runtime is already stable and the browser UI matters.
Tool fit
Ollama, LM Studio, Jan, and Open WebUI are not the same decision.
Ollama
Install first for repeatable tests- Best for
- Terminal commands, local APIs, quick model pulls, benchmark records, automation, and developer workflows.
- Avoid when
- The user mainly wants a polished desktop chat interface, visual model browsing, or manual unload controls.
- Install first on
- 8GB to 32GB laptops, developer desktops, RTX workstations, and machines used for repeatable model tests.
LM Studio
Install first for desktop chat- Best for
- People who want a local desktop app, model discovery, chat history, manual loading, and a local server without starting from the terminal.
- Avoid when
- The main goal is scripted benchmarking, headless server use, or a minimal command-line workflow.
- Install first on
- 16GB to 64GB desktops, MacBooks, and users who prefer clicking through model setup before using an API.
Jan
Try for an open-source assistant- Best for
- A desktop assistant workspace where local models, privacy, and an open-source app feel more important than raw benchmark repeatability.
- Avoid when
- The only requirement is the smallest possible runtime surface or a server UI for several users.
- Install first on
- MacBook and Windows desktop users who want a local-first chat assistant experience.
Open WebUI
Add after the runtime works- Best for
- Browser-based local AI, shared workstations, home servers, always-on machines, and a UI layered over Ollama or compatible endpoints.
- Avoid when
- The user has not yet proven that the model, GPU, memory, and runtime are stable on the machine.
- Install first on
- RTX desktops, Linux boxes, home servers, and machines where a browser UI or multiple users matter.
Install order
Avoid turning tool setup into the hard part.
1
Use the hardware simulator first so you know whether your machine belongs in the 1B, 7B, 14B, 32B, or larger model range.
2
Install Ollama or LM Studio first. They answer the core question: can this machine run one useful local model without fighting the setup?
3
Only add Jan or Open WebUI after the first model is stable, unless the desktop assistant or browser UI is the whole reason you are installing local AI.
4
Run the same real prompt in two tools before choosing a default. Compare memory pressure, startup friction, speed, and whether the workflow matches your daily use.
Tool path by machine
8GB RAM laptop
Ollama first, LM Studio only with tiny models
The main risk is memory pressure. Prove the model with a small command-line install before judging the whole local AI category.
16GB to 32GB desktop
Ollama or LM Studio first, Jan if you want a desktop assistant
This is the common sweet spot for 7B to 14B experiments, so the best tool depends more on workflow than raw fit.
RTX 3060 / 4060 Ti / 4090
Ollama for repeatable GPU tests, Open WebUI after the runtime is stable
Use VRAM as the first filter, then choose the UI layer once the model and context size are behaving.
Apple Silicon MacBook
LM Studio or Ollama first, MLX-aware options when a model supports them
Unified memory changes the limit. Choose the tool that makes unloading models and comparing prompts easiest for your Mac.
Home server or shared workstation
Ollama plus Open WebUI
A browser UI makes more sense after the always-on runtime and model storage path are reliable.
Next pages
Main local LLM hardware simulator
Start here when you know the RAM or GPU but not the model size.
8GB RAM Ollama and LM Studio path
Use this when the computer is a low-memory laptop or mini PC.
MacBook local LLM guide
Use this before choosing between LM Studio, Ollama, and MLX-aware workflows on Apple Silicon.
Measured 8GB RAM Ollama test
Use the real test record to avoid downloading a model that only technically loads.
AI Jupyter scoring method
See how the site separates fit, comfort, adoption signals, and real prompt tests.
Tool FAQ
Should beginners install Ollama or LM Studio first?
Install LM Studio first if the user wants a desktop chat app and model browsing. Install Ollama first if the user wants commands, repeatable tests, a local API, or notes that are easy to reproduce later.
Is Open WebUI a replacement for Ollama?
No. For most local setups, Open WebUI is the browser interface layer and Ollama or another compatible runtime is the model-serving layer. Prove the runtime first, then add the web UI.
Is Jan better than LM Studio?
Jan is a better fit when an open-source local-first desktop assistant matters. LM Studio is usually easier when the priority is model discovery, desktop chat, and local server controls.
Which tool is best for an 8GB laptop?
Use Ollama first with a very small model, then try LM Studio only if the model size is conservative. The tool matters less than avoiding oversized 7B or 14B downloads on low-memory machines.
Which tool is best for an RTX 4090 local AI workstation?
Use Ollama for repeatable GPU tests and API experiments. Add LM Studio for desktop chat or Open WebUI when the machine becomes a shared browser-based local AI box.
More local AI tool scenarios