Run AI on Your Own Hardware
No cloud. No subscription. No data leaving your machine. This section covers everything you need to run, configure, and fine-tune AI models locally — from a $400 mini PC to a $2000 GPU workstation.
100% private
Your prompts and data never leave your machine. No API logs, no training on your conversations.
No subscription
Pay once for hardware and run unlimited queries for free. One GPU pays for itself in weeks.
Works offline
Run a full LLM on a plane, in a bunker, or on an air-gapped server. No internet required.
Where do you want to start?
Step-by-step guides
All local AI guidesHow to Install Ollama on macOS
Get Ollama running on a Mac in under 10 minutes: install via Homebrew or the native app, pull your first model, and confirm the local server is responding.
How to Install Ollama on Windows
Download the Windows installer, confirm PATH is set, verify the local API server is up, and understand when WSL2 gives you better GPU performance.
How to Run Llama 3 Locally with Ollama
Pull the Llama 3.3 model, run an interactive chat session, switch between the 8B and 70B variants, and understand the VRAM requirements for each size.
How to Use Ollama as a Drop-In OpenAI API
Point any OpenAI-compatible SDK or tool at your local Ollama server by changing one URL, and use OLLAMA_HOST to expose it on your network.
How to Set Up LM Studio for Local AI
Download LM Studio, grab a model from the built-in hub, start the local server, and have a working private AI chat running on your own hardware in about ten minutes.
How to Run Mistral 7B Locally
Pull Mistral 7B with Ollama, understand when to use mistral-nemo instead, check your VRAM requirement, and tune a system prompt to get sharper answers.
How to Set Up Open WebUI for Local AI
Run Open WebUI in Docker, connect it to Ollama, switch between models in one click, and create custom personas that save your best system prompts.
How to Pick the Right Local AI Model for Your Hardware
Match your GPU VRAM tier to concrete model recommendations, understand Q4/Q5/Q8 quantization, and use a simple rule of thumb to avoid out-of-memory crashes before you download.
Models to run locally
16 modelsEvery model is free to download and run. Requirements listed are for the Q4 quantized version — the best balance of quality and size.
Llama 3.1 8B
Meta · Llama 3.1 Community
Meta's 8B workhorse with a massive 128K context window. Punches well above its weight class for everyday coding and chat tasks, runs comfortably on any consumer GPU with 6 GB VRAM, and delivers near-70B quality on many benchmarks.
Min VRAM
5 GB
Context
128K
Run with Ollama
ollama run llama3.1:8bLlama 3.3 70B
Meta · Llama 3.3 Community
Meta's flagship open-weight model and the go-to choice for users who want GPT-4-class performance without sending data to the cloud. Consistently tops open-source leaderboards. Needs ~48 GB VRAM at Q4_K_M — a dual-GPU rig or Apple Silicon with 48 GB unified memory.
Min VRAM
48 GB
Context
128K
Run with Ollama
ollama run llama3.3:70bLlama 3.2 3B
Meta · Llama 3.2 Community
The smallest production-quality Llama model, designed to run on laptops, Raspberry Pis, and other edge devices with minimal VRAM. Response latency is sub-second on modern CPUs, making it ideal for real-time assistants and local automation scripts.
Min VRAM
2 GB
Context
128K
Run with Ollama
ollama run llama3.2:3bQwen3 8B
Alibaba · Apache 2.0
One of the most-downloaded mid-size AI models on HuggingFace in 2026, with tens of millions of downloads. Qwen3 8B outperforms the previous Qwen2.5 14B on most benchmarks, supports hybrid thinking mode and tool calling, and runs on any GPU with 6 GB VRAM. Apache 2.0 license — fully commercial.
Min VRAM
5 GB
Context
128K
Run with Ollama
ollama run qwen3:8bMistral 7B v0.3
Mistral AI · Apache 2.0
The model that proved 7B parameters can match much larger models on reasoning tasks when trained carefully. Apache 2.0 licensed, so it is fully free to use commercially with no restrictions.
Min VRAM
4 GB
Context
32K
Run with Ollama
ollama run mistral:7bMixtral 8x7B
Mistral AI · Apache 2.0
A sparse mixture-of-experts model that activates only 12B parameters per token while carrying 47B total, delivering 70B-class results at a fraction of the compute cost. One of the best open-weight models available under a fully permissive license.
Min VRAM
26 GB
Context
32K
Run with Ollama
ollama run mixtral:8x7bBest local LLM for your hardware
Already know your VRAM budget, GPU or Mac? These pages list every model from the verified catalog that fits, with headroom, run commands and spec-sheet links.
Hardware picks
Full buyer's guideThese are affiliate links. We earn a small commission if you buy through them — at no extra cost to you. Disclosure
NVIDIA RTX 5090 32GB
32 GB GDDR7, 512-bit bus, 575W TDP, 21,760 CUDA cores
Most VRAM on any consumer card — the current flagship for 34B–70B local inference
Runs these models
NVIDIA RTX 5080 16GB
16 GB GDDR7, 256-bit bus, 360W TDP, 10,752 CUDA cores
Current-gen 16 GB card with GDDR7 speed for fast 7B–34B inference
Runs these models
NVIDIA RTX 5070 Ti 16GB
16 GB GDDR7, 256-bit bus, 300W TDP, 8,960 CUDA cores
Best current-gen value — 16 GB GDDR7 for 7B–34B at a mid-tier price
Runs these models
NVIDIA RTX 4060 Ti 16GB
16 GB GDDR6, 128-bit bus, 165W TDP, 4,352 CUDA cores
Best VRAM-per-dollar entry card for running 13B models locally
Runs these models
NVIDIA RTX 4070 Super 12GB
12 GB GDDR6X, 192-bit bus, 220W TDP, 7,168 CUDA cores
Sweet-spot performance for fast 7B and 13B token generation
Runs these models
NVIDIA RTX 4080 Super 16GB
16 GB GDDR6X, 256-bit bus, 320W TDP, 10,240 CUDA cores
High-throughput inference for 7B–34B models at serious speed
Runs these models
Weekly local AI drops
New models, what runs on your hardware, and the guides to set them up. One email a week, unsubscribe any time.
Affiliate disclosure
Hardware links on this page go to Amazon via our affiliate program. We earn a small commission when you buy through them. This does not change the price you pay or influence our recommendations — every pick is chosen based on VRAM, value, and real performance for local AI workloads.