How to Get the Most Out of a 24GB or 32GB VRAM GPU for Private Local AI Models
Learn how to run private local AI models efficiently on a 24GB or 32GB VRAM GPU. This tutorial covers Ollama…
Quantized AI Models Explained: What FP16, FP8, FP4 (and 4-bit) Really Mean for Your GPU, VRAM, and Unified Memory
Quantized AI models store weights (and sometimes activations/cache) using fewer bits—like FP8 or FP4 instead of FP16—cutting VRAM/unified memory requirements…
Model Format vs Runtime vs Runner vs Hardware: The 4-Layer Map That Fixes “AI Runner” Confusion
Most local “AI runner” confusion comes from mixing four layers: model format (GGUF, safetensors), runtime/backend (llama.cpp, MLX, ONNX Runtime, TFLite,…