How Much VRAM Does an LLM Need? Parameters, Quantization and Context
How much VRAM does an LLM need? Use weights + KV cache + overhead to estimate from parameters, quantization and context, with quick tables for 8GB to 48GB.
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LM Studio Tutorial: Download and Run Models Without the Command Line
A complete LM Studio tutorial: install, search and download models, pick a quantization, tune context, chat with documents and start the local API.
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How to Run an LLM Locally: Hardware You Need and How to Pick a Model (2026)
Want to run an LLM locally? Learn how much VRAM you need, which models fit 8GB to 48GB, and how to get your first model running with Ollama in five steps.
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Local LLM PC Build: Four Tiers From 12GB to 32GB VRAM
Planning a local LLM PC build? Four VRAM tiers from 12GB to 32GB, with CPU, RAM, PSU and storage advice, a GPU price snapshot and a build checklist.
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Local LLM vs API: How to Decide Based on Usage, Privacy and Budget
Local LLM vs API: decide on usage, privacy, model quality, budget and upkeep, with a cost method using hypothetical numbers and a hybrid local-plus-cloud plan.
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How to Run LLMs on a Mac: How Much Unified Memory Do You Need?
How to run LLMs on a Mac: what 16GB to 128GB of unified memory can hold, usable GPU share, bandwidth by chip, and Mac vs GPU advice.
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How to Install Ollama on Windows, Mac and Linux: From Download to Your First Model
Install Ollama on Windows, macOS or Linux, verify it works, run your first model, move the model folder and fix common problems. Updated October 2026.
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Open WebUI Ollama Setup: A Chat Interface for Your Local Models
Open WebUI Ollama setup with Docker or pip: create the admin account, connect to Ollama, download models and fix ‘can’t reach Ollama’ errors.
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Run Qwen Locally: Picking a Qwen3, 3.5, 3.6 or 3.8 Size
How to run Qwen locally: Ollama file sizes for Qwen3, 3.5, 3.6, 3.8, Coder and VL, a VRAM matching table, one-line commands and thinking-mode control.
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What Is a Local LLM? How It Differs from Cloud AI and Who It Is For
What is a local LLM? See how local, offline and on-device models differ, how they compare with cloud AI on privacy, cost and capability, and who it suits.
