Local Deployment & Hardware
Local AI means running large language models on hardware you control — no API keys, no per-token billing, no prompts leaving your machine. This hub is the complete reference for running local AI models with Ollama and the tools around it: which models to pull from the ollama model list, how to choose between runners, and how to fix the errors that stop most setups. Whether you want the best ollama models 2026 for coding, a private chatbot on a laptop, or are comparing ollama alternatives like LM Studio and llama.cpp, every guide here includes the exact commands and configs we tested.
Start here: pick your runner
The first decision in any local setup is the runner itself. Ollama is the default choice for most people — one installer, a model registry, and an OpenAI-compatible API on port 11434. But it is not the only option, and depending on your workflow it may not be the best one.
Our head-to-head LM Studio vs Ollama comparison breaks down the GUI-first and CLI-first approaches: performance overhead, model management, context handling, and which one fits a developer workflow versus a casual chat setup. If you care more about raw control and scripting, the llama.cpp vs Ollama performance comparison shows what Ollama’s abstraction layer costs you in tokens per second — and when it is worth paying that cost for convenience.
Choose the right model for your workload
Model choice matters more than hardware in most local setups. A well-quantized 8B model tuned for code will outperform a generic 70B model for a developer, at a tenth of the VRAM. Our guide to the best Ollama models for coding and roleplay covers the current leaders, what quantization level to pull, and which models to avoid for each use case.
As a rule of thumb for VRAM planning: a 7-8B model at Q4 needs roughly 5-6 GB, a 13-14B model needs 9-11 GB, and a 70B model at Q4 needs 40+ GB or aggressive CPU offloading. Every model guide on this site states the memory requirement before anything else.
Manage your installation like a pro
Local models eat disk space fast — a single unquantized 70B pull can exceed 100 GB. When you need to clean up, our guide on how to delete Ollama models covers the exact commands for PowerShell, macOS Terminal, and Linux, including how to fully uninstall Ollama and reclaim every gigabyte of blob storage.
For deeper control, the Ollama environment variables and Modelfile guide explains where Ollama stores models on each operating system, how to move the storage directory, set a default system prompt, control GPU layers, and tune keep-alive behavior so models do not unload between requests.
Serve models to your apps
Ollama’s real power is the local API: once ollama serve is running, any tool that speaks the OpenAI API can use your models. The Ollama local API server setup guide covers port 11434 configuration, exposing the server to other machines on your LAN, HTTPS fronting, and how to stop the server cleanly.
Then connect it to the tools you already use: connecting Ollama to AnythingLLM, Cursor and Open WebUI walks through each integration, including the “model not found” error that trips up most first-time setups.
Fix what’s broken
Local AI fails in predictable ways: the GPU sits idle while the CPU crawls, VRAM overflows mid-prompt, or the server returns a 500 with no useful message. Our Ollama GPU and memory error troubleshooting guide covers CUDA detection, VRAM optimization, and memory pressure on Apple Silicon — with the diagnostic commands to identify which problem you actually have before changing anything.
Why run models locally at all?
Three reasons keep coming up in production teams. Privacy: prompts and documents never leave your infrastructure, which settles most compliance questions instantly. Cost: past a few million tokens per day, a single GPU beats API pricing by an order of magnitude. Control: you pick the model, the quantization, the context length, and the system prompt — no provider deprecations, no rate limits, no silent model swaps.
The tradeoff is operational: you own the hardware, the updates, and the troubleshooting. That is exactly what this hub exists to make painless. Start with the runner comparison, pull a model that fits your VRAM, and wire it into your editor or app. Every article below is part of that path.
