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Complete Guide to Running LLMs Locally
Running LLMs locally gives you privacy, zero API costs, and full control over your AI stack. But choosing the right hardware matters: too little VRAM and your model won't load, too slow a GPU and inference crawls. This hub collects every guide, tutorial, and comparison you need to go from zero to running 70B+ parameter models on your own machine — covering GPU selection, quantization trade-offs, software setup with Ollama and llama.cpp, and real-world benchmark data from our testing.
Top Picks
NVIDIA GeForce RTX 5090
$1,999 – $2,199
- VRAM: 32GB GDDR7
- CUDA Cores: 21,760
- Memory Bandwidth: 1,792 GB/s
NVIDIA GeForce RTX 4090
$1,599 – $1,999
- VRAM: 24GB GDDR6X
- CUDA Cores: 16,384
- Memory Bandwidth: 1,008 GB/s
Apple Mac Mini M4 Pro
$1,399 – $1,599
- Chip: Apple M4 Pro
- CPU Cores: 12-core
- GPU Cores: 18-core
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