Why Everyone's Switching to Linux for Local AI

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I ran over 100 benchmarks to find out if Linux actually beats Windows for local AI - and the results surprised me.
Linux wins, but not for the reason most people think.

The real game-changer isn't speed (only 2-3% faster). It's the 800MB of VRAM you save on every single model load.
On a 16GB GPU, that's 5% of your total memory - the difference between your model fitting or not fitting at all.

What You'll Learn:
The actual benchmark results: Linux vs Windows vs WSL2 on identical hardware
Why VRAM savings matter more than raw inference speed
How context window size destroys your performance (75% degradation at 60K tokens)
Why WSL2 gives you the worst of both worlds (uses MORE VRAM than Windows alone)
The Docker advantage: keeping your Linux system clean while Windows accumulates bloat
Why professional AI tools (VLLM, TensorRT, Lambda Stack) target Ubuntu specifically
How to set up a dual-boot system with zero risk to your Windows installation

Timestamps:
0:00 Linux wins local AI, but not why you think
0:30 Initial Test Results
1:27 Why Ubuntu specifically (not your favorite distro)
2:49 Live benchmark demo on Linux
5:41 Running LLM Tests
6:32 Important Test Results
8:28 Another Linux Benefit
9:31 Why not WSL2?
10:18 Other Linux Tools
11:06 Start with Linux

Why did I create this video?
I kept running out of VRAM while trying to run coding models locally. That frustration sent me down a rabbit hole to find out exactly how much GPU memory Windows was eating up. After running over 100 benchmarks, I wanted to share the real data - not opinions, not vibes, actual numbers you can verify yourself with the benchmark tool I've open-sourced.

Connect with me:
  / zen-van-riel  
https://www.skool.com/ai-engineer

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