Trying to figure out the best way to run open source AI models for your specific situation? There are actually six distinct categories, ranging from dead simple to seriously advanced, and picking the wrong one can waste your time or leave you hitting walls you didn’t expect.
Tina Huang, an ex-Meta data scientist, just dropped a comprehensive breakdown of every major way to run open source models in 2026, ranked from easiest to hardest with demos and tool recommendations for each. This is one of the clearest maps I’ve seen for navigating what can feel like an overwhelming landscape.
Here’s the core insight that makes this worth your time: open source models are now just as good as closed source ones. They’re free to use, fully customizable, and you have complete control over where they run. The only real question is which deployment method fits your needs.
The Four Major Categories (Plus Two Bonus)
The creator lays out four primary approaches, then adds two advanced bonus categories for people ready to go deeper. Let me walk through each one with the key trade-offs.
Category 1: Local
Running models directly on your own machine. Private, free (beyond hardware costs), and works offline. The easiest entry point is downloading Ollama, picking a model, and chatting with it in about two minutes.
What’s surprising is how little hardware you actually need. A MacBook Air M4 with 16GB of memory can handle 4B models easily and most 8B models too. The expert puts specific hardware requirements on screen for different model sizes, which is genuinely helpful for setting expectations.
The workflows here scale in difficulty:
- Easy: Download Ollama, pick a model, start chatting
- Medium: Call the local model via code at localhost:11434 to build apps or agents
- Medium-Hard: Use a dedicated machine like a Mac Mini for 24/7 uptime and more power
- Hard: Expose your local setup to the internet via Cloudflare tunnels for demos
- Very Hard: Fine-tune models locally using a GPU and tools like Unsloth
One practical tip from the video: many builders are buying Mac Minis specifically to avoid the disruption problem. Your laptop competes for resources when you’re video editing or multitasking. A dedicated Mini runs your models and agents around the clock without interruption.
Category 2: Browser and Hosted Playgrounds
The absolute easiest way to touch open source models. Someone else has downloaded, hosted, and served them for you. Just show up and use them.
- 👍 Pros: Zero setup, no hardware requirements, great for learning and experimenting, mostly free
- 👎 Cons: Not private (your data goes to the host), rate limited, sessions expire (Colab), limited functionality
Tools to explore include arena.ai, groq.com, Hugging Face Spaces, and Google Colab. The Colab workflow is particularly interesting for educators since you get a free T4 GPU from Google during sessions and can share notebooks with students. But the creator warns clearly: nothing is truly free here. Sessions expire and everything disappears, and your data flows back to Google.
Category 3: Managed Inference APIs
This is where builders who don’t want to touch infrastructure go to ship fast. You sign up with a provider like Groq, Together AI, or Fireworks AI, grab an API key, and call it from your code. About five lines of code to get started.
Best for indie hackers, startups, and personal projects. You get open source model access without hosting anything yourself. When ready, deploy your app on Railway, Vercel, or similar platforms. The creator notes that while you can plug these APIs into no-code tools, you’ll get the most value here if you know how to code.
Category 4: VPS (Virtual Private Server)
Renting someone else’s computer (without physically having it) for $5-10 per month. You SSH in and treat it like your own machine: install Ollama, download models, build whatever you want.
This is the sweet spot for builders who need privacy, data control, and the ability to run multiple models and services simultaneously. Industries like healthcare, legal, and finance should be looking here.
The most clever workflow in this section is the hybrid approach: run your models locally on a Mac Mini for security and data privacy, but host your application on a VPS so users can access it over the internet. Connect the two using Tailscale. You only pay the $5-10 VPS cost and skip GPU rental entirely.
More advanced VPS patterns include renting GPUs hourly from RunPod or Vast.AI for larger models, and using Docker containers to run multiple apps simultaneously without conflicts.
Bonus Category 1: Managed Cloud Solutions
For when you need real scalability. Your models live in the cloud with auto-scaling infrastructure. Think 100,000+ users, unpredictable traffic spikes, or deploying fine-tuned custom models for others to use. Best for startups, enterprise teams, and compliance-heavy industries. Most people won’t need this.
Bonus Category 2: On-Device and Edge
The creator flags this as a space to watch. This means shipping an AI model inside your application so the user’s device runs everything. Currently niche and mostly driven by big corporations (Apple Intelligence, Samsung with Gemini Nano), but the potential for mobile and desktop apps that prioritize privacy and offline capability is significant. The challenge is keeping models small enough to package while still delivering useful results.
Which Path Should You Take?
Here’s a practical decision framework based on the video’s breakdown:
- Just exploring: Go with hosted playgrounds (Category 2), zero commitment
- Building personal projects or MVPs: Managed inference APIs (Category 3) let you ship fast
- Need privacy or always-on reliability: Local setup with a dedicated machine (Category 1)
- Getting serious about products or working with sensitive data: VPS (Category 4), optionally hybrid with local models
- Scaling to large user bases: Managed cloud (Bonus 1)
- Building mobile or offline-first apps: On-device/edge (Bonus 2)
The full video includes specific tool recommendations, hardware requirement charts, and summary slides for each category that are worth screenshotting. Check it out for the complete picture and all the visual references.