Run AI on hardware you control.
Learn what local inference means, what it requires, how to install a supported runtime, and how to diagnose common problems.
Benefits, limits, and realistic expectations
Local AI can improve control and offline availability, but model capability is constrained by memory, software support, and processing speed. “Local” also does not guarantee privacy if an application uploads telemetry or files.
Local AI Explained for Beginners
What it means to run a model on your own computer, including privacy, storage, speed, and maintenance tradeoffs.
Explore →Local AIHow to Install Ollama
A cautious Windows and Linux installation walkthrough with verification, model management, and removal guidance.
Explore →Hardware and ModelsHow to Choose an AI Model
Select models by task, license, memory fit, context, tools, language, and measured quality—not hype.
Explore →Hardware and ModelsUnderstanding RAM and VRAM
See how system memory and graphics memory affect model loading, context, and local inference.
Explore →Hardware and ModelsCan My Computer Run Local AI?
A practical capability check for storage, memory, processor support, and realistic model selection.
Explore →Hardware and ModelsCPU vs GPU for AI
Understand parallel processing, compatibility, memory placement, and when acceleration is worth it.
Explore →Local AICommon Local AI Problems and Solutions
Diagnose model download, memory, speed, GPU, context, and service failures with a safe evidence-first process.
Explore →Safe installation sequence
- Inventory RAM, GPU or shared graphics memory, operating system, and free storage.
- Confirm the runtime supports your operating system and hardware.
- Install from the official source and record the version.
- Start with one modest instruct model and non-sensitive test data.
- Monitor resources, verify output, and scale only when evidence supports it.