Local AI on Linux #4 — Setup Day | Linux + GPU Drivers + Docker, All At Once
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In this video, we dive deep into professional IT workflows to help you learn faster and code smarter. Whether you're a DevOps engineer, a SysAdmin, or a curious developer, our "No Filler" approach ensures you get the facts without the fluff.
The last foundations part. Today we install everything the next 20 parts will need — Linux + GPU drivers + Docker — verified at every step. Tedious, but one-time and forever.
By the end: working Ubuntu 24.04 + Python + Docker + (if GPU) NVIDIA driver + CUDA + NVIDIA Container Toolkit. With a sanity-check list — when it's all green, you're ready for Part 5.
This closes Phase 1. Phase 2 starts next: your first local LLM.
📚 IN THIS PART YOU'LL LEARN
✓ The 4-layer install stack — what goes in what order
✓ 3 OS starting points: Ubuntu native / WSL2 Windows / macOS
✓ apt update + essential packages (curl, git, Python, Docker tooling)
✓ NVIDIA driver install the right way (ubuntu-drivers)
✓ nvidia-smi verification — what success looks like
✓ Docker install + sudo-less usage (usermod -aG)
✓ NVIDIA Container Toolkit — so containers see the GPU
✓ Sanity check list — 6 commands that verify everything
✓ 5 most common setup traps + their fixes
✓ 50GB+ disk + stable net — practical tips
✓ Workspace folder: ~/local-ai/ (the series hub)
⏱️ TIMESTAMPS
00:00 Intro
01:30 What we're installing — 4 layers
02:00 3 OS starting points
03:00 Step 1: Linux terminal
03:30 Step 2: Update + essentials
04:00 Step 3: NVIDIA drivers (GPU only)
04:30 nvidia-smi verification
05:30 Step 4: Docker install
06:30 NVIDIA Container Toolkit
07:00 Sanity check list
07:30 5 common setup traps
08:00 Disk + network practical tips
08:15 Workspace folder setup
08:25 Recap + cheat sheet
⚡ IF THIS WAS HELPFUL
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#localai #ollama #linux #ubuntu #docker #nvidia #cuda #wsl2 #setup #aitutorial #localllm #aifor beginners #2026ai #dargslan
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