Ollama vs LM Studio vs Jan: The Real Deal After 6 Months Running Local AI

Here's the deal: each tool has a completely different philosophy. Ollama says "keep it simple, stupid." LM Studio says "make it pretty." Jan says "make it configurable as hell."

Ollama: The Tank That Actually Works

Ollama is the boring choice that actually works in production. It's a command-line tool that downloads models with ollama run llama3.1 and starts a server on localhost:11434. No GUI, no bullshit, just AI models that respond to HTTP requests.

Key Resources:

• Official Documentation
• API Reference
• Model Library
• Docker Hub

What makes it bulletproof:

• Models load in 30 seconds on my RTX 4090, every single time
• API responses are consistent - no random timeouts like the other two
• Memory usage is predictable: Llama 3.1 8B uses exactly 8.2GB VRAM
• The Docker container never crashes (I've had it running for 3 months straight)

But here's the problem: No built-in chat interface. You're stuck using curl commands or building your own frontend. For quick testing, you need a separate tool like Open WebUI.

LM Studio: Pretty But Fragile

LM Studio has the best GUI - clean, modern, works like ChatGPT. Model discovery is incredible: you can browse, download, and chat with new models in under 5 minutes.

Essential Links:

• Download LM Studio
• Documentation
• Model Catalog
• API Documentation
• GitHub Issues
• Community Discord

The good stuff:

• Beautiful chat interface that your non-technical team can actually use
• Model management is chef's kiss - search, download, organize everything visually
• Built-in API server with OpenAI compatibility
• Works great for demos and client presentations

The nightmare fuel:

• Memory leaks in version 0.3.20 that consumed 64GB RAM in 24 hours
• Made my server unusable until I rolled back to 0.3.19
• Random crashes during model loading (especially with 70B models)
• No Docker support means it's desktop-only

I set up a cronjob that restarts the LM Studio server every 10 minutes as a workaround for the memory leaks. Not exactly production-ready.

Jan: The Swiss Army Knife

Jan AI is trying to be everything to everyone. Local models, cloud models, extensions, plugins - it's the VS Code of AI chat tools.

Core Resources:

• Jan Download
• Documentation
• GitHub Repository
• Discord Community
• Model Context Protocol
• Settings Guide
• Troubleshooting

What it does well:

• Zero configuration required - download, install, pick a model, start chatting
• Supports both local and cloud providers (OpenAI, Anthropic) in the same interface
• Extension system for custom integrations
• Cross-platform: Windows, Mac, Linux all work the same way

Where it falls apart:

• Configuration is a nightmare - too many options, unclear which ones matter
• Resource usage is unpredictable (sometimes uses 2GB, sometimes 20GB for the same model)
• Updates break existing setups regularly
• Performance varies wildly based on your specific hardware setup

I tried running it on a Windows server for a client demo and it blue-screened the machine. Had to explain to 12 executives why the AI wasn't working. Good times.

After running all three in actual production environments (not just toy projects), here's what you need to know before you commit to any of these tools.

Ollama in Production: The Safe Choice

Ollama is the only one I trust in production. It runs as a systemd service, handles crashes gracefully, and the API never randomly breaks.

Production Resources:

• Ollama Docker Guide
• API Documentation
• FAQ and Troubleshooting
• Production Deployment Guide
• Model Library
• Community GitHub
• Discord Community

Real production setup:

• Docker container behind nginx reverse proxy
• Auto-scaling based on GPU utilization (using NVIDIA's container runtime)
• Monitoring with Prometheus metrics at /metrics endpoint
• Load balancing across 3 GPU servers using simple round-robin

What broke in production:

• GPU drivers crashed twice in 6 months (not Ollama's fault)
• One model corruption after server hard restart (fixed with ollama pull llama3.1 re-download)
• Zero application-level crashes or memory leaks

Monitoring commands that actually work:

## Check model status (example API call)  
## curl your-server:11434/api/tags

## Monitor GPU usage
nvidia-smi dmon -s pucvmet

## Memory consumption
docker stats ollama-container --no-stream

Additional Production Resources:

• Monitoring Guide
• Performance Tuning
• Environment Variables

LM Studio: Don't. Just Don't.

I spent 3 days getting GPU acceleration working properly with LM Studio in a Windows Server 2022 environment. The GUI looks professional, but underneath it's an Electron app that fights with Windows GPU drivers.

Production problems I hit:

• Application hangs during model switching (requires force kill)
• Memory usage grows to 40GB+ after running for 8 hours
• No built-in health checks or auto-recovery
• GUI becomes unresponsive under high load
• No way to script model management

The breaking point was during a client presentation where LM Studio froze mid-conversation. Had to restart the entire server while 8 people waited on a video call. Never again.

Jan: Configurability Hell

Jan has more configuration options than you'll ever need, which sounds good until you're troubleshooting why your 70B model runs at 3 tokens/second when it should be doing 12.

Jan Production Resources:

• Production Deployment
• Performance Optimization
• Data Management
• Support Forums
• GitHub Issues
• Community Discord

Production learnings:

• Default settings are terrible for most hardware configurations
• Extension system breaks after updates (happened 3 times)
• Database corruption after unexpected shutdown
• Resource allocation is unpredictable

What actually works:

• Disable all extensions except core chat
• Set model context to 4096 max (anything higher causes OOM crashes)
• Pin to specific model versions (auto-updates break things)
• Run single model only - model switching causes memory leaks

Debugging Jan when it breaks:

## Check Jan logs (good luck finding them)
## Windows: %APPDATA%\Jan\logs\
## macOS: ~/Library/Application Support/Jan/logs/
## Linux: ~/.config/Jan/logs/

## Clear Jan data when it corrupts (happens monthly)
## This wipes all your conversations - backup first
rm -rf ~/.config/Jan/models ~/.config/Jan/conversations

The Harsh Reality: TCO Analysis

After 6 months, here's what it actually cost to run each tool:

Ollama:

• Setup time: 2 hours
• Maintenance: 1 hour/month
• Downtime: 4 hours total
• Hardware depreciation: $150/month (RTX 4090)

LM Studio:

• Setup time: 16 hours (GPU driver hell)
• Maintenance: 8 hours/month (memory leak restarts)
• Downtime: 24 hours total
• Therapy costs: Priceless

Jan:

• Setup time: 6 hours (configuration tweaking)
• Maintenance: 4 hours/month
• Downtime: 12 hours total
• Aspirin budget: $20/month

The winner? Ollama, by a mile. It's boring, reliable, and doesn't make me question my life choices.