The comparison tables above tell you what features each tool has, but they don't tell you what it's actually like to live with these tools day after day.
The real story is in the details
- the crashes, the memory leaks, the configuration hell, and the rare moments when everything works perfectly.
My Chat
GPT bill hit $200 last month and I thought "fuck this, I have a decent GPU sitting here doing nothing." So I tried every local AI tool I could find. Some work, some don't, and some make you want to throw your computer out the window.
Here's what six months of daily use taught me about each tool:
Ollama:
Actually Works in Production
Ollama is what I ended up using because it doesn't crash every few hours.
Command-line tool that downloads models with ollama run llama3.1 and serves them on localhost:
11434.
Why I keep coming back to it:
- Models usually load in 20-40 seconds on my RTX 4090
- Memory usage stays pretty consistent
- Llama 3.1 8B uses around 8GB VRAM
- Docker container has been running for months without issues
- API actually works when I need it to
- I got it load balanced behind nginx without too much pain
The annoying part: No GUI.
You're stuck with curl commands or you need to install Open WebUI separately.
Docker setup that hasn't broken yet:
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama --restart=unless-stopped ollama/ollama
LM Studio:
Pretty But Crashes
LM Studio looks amazing
- clean interface, works like Chat
GPT, and you can download models by just clicking on them.
What's great about it:
- Actually has a GUI that makes sense
- Model browsing and downloading is really well done
- Built-in API server that's OpenAI compatible
- Great for showing off to non-technical people
What makes me want to scream:
- Memory leaks like a rusty bucket
- Ate all my RAM again yesterday
- think it was around 40GB before I killed it
- Crashes randomly when loading bigger models
- Desktop only, so no server deployments
I just restart it every few hours when I notice it getting slow.
Not ideal but the interface is too good to give up completely.
Reality: I use it for demos because it looks professional, then switch to Ollama for anything that needs to actually work.
Performance notes: On my RTX 4090, LM Studio consistently delivers around 45-55 tokens/sec with Llama 3.1 8B, but the memory leak pattern is predictable
- starts around 8GB RAM usage and climbs to 25-30GB within 2-3 hours of active use.
Jan:
Too Many Damn Settings
Jan wants to be everything
- local models, cloud models, extensions, plugins.
It's like VS Code had a baby with AI tools.
What works:
- Install it and start chatting immediately
- Works the same on Windows, Mac, Linux
- Extension system if you're into that
- Can mix local and cloud models
What doesn't:
- So many settings I don't know which ones actually matter
- Memory usage is all over the place
- sometimes 3GB, sometimes 15GB
- Updates randomly break things
- Lost my configuration twice during updates
Honest assessment: I spent way too much time tweaking settings instead of actually using it.
If you like configuring things for hours, you'll love it. If you just want it to work, you'll hate it.
The configuration rabbit hole: Jan offers 47 different settings across 8 categories.
While this flexibility sounds good, the default configurations often need tweaking for optimal performance. Memory allocation settings particularly require manual adjustment based on your hardware
- something that should happen automatically.
GPT4All: Just Works
GPT4All from Nomic AI is for normal people who want local AI without the hassle.
Why it's solid:
- Download, install, pick a model, done
- Local
Docs thing lets you chat with your files
- Performance is consistent, no weird surprises
- MIT license so no legal bullshit
- Python bindings work as advertised
The downsides:
- Desktop only, no server deployment
- Model downloads take forever
- GPU acceleration isn't as good as others
- Won't scale beyond single user
Good for: Solo developers, small teams, or anywhere you can't let data leave your building.
Reliability factor: GPT4All has been rock-solid in my testing.
Zero crashes in 6+ months of use, consistent memory usage around 8-9GB, and model loading times that don't vary much (30-45 seconds for most 7B models). The LocalDocs feature actually works
- I've indexed 50GB of technical documentation and it reliably finds relevant context.
Llama.cpp: Fast But Painful
Llama.cpp by Georgi Gerganov is the low-level C++ engine that powers most of these tools.
When it works, it's fast:
- Faster than everything else on my 4090
- Uses less memory than the GUI tools
- Complete control over every setting
- This is what Ollama and GPT4All use under the hood
Getting it working is pure hell:
- CUDA compilation fails randomly
- One Windows update broke my WSL2 setup completely
- Spent an entire weekend trying to get it compiled on Ubuntu
- Documentation assumes you know what you're doing
Use it if: You need maximum performance and have time to fight with compilation.
Avoid if: You have deadlines or value your sanity.
What I Actually Use
For production stuff: Ollama.
It's boring but it doesn't break.
For personal projects: GPT4All if I want simple, LM Studio if I want pretty (but I restart it frequently).
For maximum speed: Llama.cpp when I can get it working.
For team use: Ollama with Open WebUI frontend.
Developers get APIs, everyone else gets a GUI.
For privacy-critical stuff: GPT4All.
No cloud, no telemetry, no bullshit.
The local AI scene is actually usable now, but each tool has trade-offs. Ollama is reliable but ugly. LM Studio is pretty but crashes. Jan has every feature but breaks constantly. GPT4All just works but only for single users. Llama.cpp is fast but hates you.
The decision matrix is actually straightforward:
- Need production reliability? → Ollama (only option that won't embarrass you in front of users)
- Want the best UX? → GPT4All (consistently works, looks decent)
- Prototyping and demos? → LM Studio (beautiful when it works)
- Maximum performance? → llama.cpp (if you have the patience)
- Team collaboration? → Ollama + Open WebUI
Hardware reality check: You need more VRAM than the marketing materials claim.
Budget 8-10GB for 7B models, 12-16GB for 13B models. CPU-only inference works but feels like dial-up internet
- fine for testing, painful for actual use.
Pick based on what you can tolerate: crashes, ugly interfaces, or spending weekends debugging CUDA drivers.