Local AI on Linux #3 — Hardware Reality Check | What You Can ACTUALLY Run, No Hype

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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.

Reality time. Before installing anything in Part 4, this part tells you — honestly, with numbers — what your existing hardware can do. The real difference between CPU, GPU, and Mac M-series. Concrete GPU prices. How to decide before spending $1000+ unnecessarily.

We map three real paths: CPU-only (16GB RAM, ~10 tok/s) · Consumer GPU (RTX 3060+, ~50 tok/s) · Apple Silicon Mac (unified memory advantage). Pick the one that matches you — every later part shows BOTH a CPU path and a GPU path.

📚 IN THIS PART YOU'LL LEARN

✓ What actually matters — RAM/VRAM, bandwidth, compute (in that order!)
✓ The 3 real hardware paths — CPU, GPU, Mac M-series
✓ Concrete GPUs with prices: RTX 3060 ($280) → RTX 4090 ($1700)
✓ Why Apple Silicon is special with unified memory
✓ Cloud GPU rental: Vast.ai, RunPod, Lambda, Paperspace ($0.30-2/hr)
✓ Memory math — how to predict GB needs for any model
✓ How to check your own machine: free -h, nvidia-smi, system_profiler
✓ Should you buy a GPU? — honest decision guide
✓ 4 common hardware traps to avoid
✓ How the series accommodates both CPU and GPU paths

⏱️ TIMESTAMPS

00:00 Intro
00:30 What actually matters — RAM, bandwidth, compute
01:00 The 3 real paths
01:30 Path A: CPU-only laptop details
02:00 Path B: NVIDIA GPU table ($280-2000)
02:30 Path B': Apple Silicon Mac
03:30 Path C: Cloud GPU rental
04:00 The memory math
05:30 Typical configurations
06:00 How to check your own machine
07:00 Should you buy a GPU? — decision guide
07:30 4 common hardware traps
08:00 The series path — CPU vs GPU
09:00 Recap + cheat sheet

⚡ IF THIS WAS HELPFUL
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