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…
Create AI Videos 100% Locally on Your GPU: A Practical Guide to LTX-2 (No API Keys, No Per-Second Billing)
LTX-2 is an open-source AI video generation model that runs entirely on your own GPU—no cloud subscriptions, no API keys,…
Local LLMs vs Premium Models on a 24GB GPU: The Real Limits in Quality, Coding, and Context Windows
A 24GB VRAM GPU can run powerful local language models for coding, refactoring, and private assistants—but it still can’t fully…
10 Local “Unlocked” LLM Setups You Can Run on CPU/GPU (How to Find Models, Load Them, and Verify Their Capabilities)
A technical guide to running local “unlocked” LLMs on CPU/GPU: the best runtimes (Ollama, LM Studio, llama.cpp, LocalAI, vLLM, Transformers),…