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Your complete guide to local AI on your own hardware
Running AI locally offers significant advantages over cloud-based solutions. Understanding these benefits helps you decide if local AI is right for you.
Your data never leaves your computer. This is critical for sensitive documents, proprietary code, personal conversations, and HIPAA/GDPR compliance. Cloud services like ChatGPT process and potentially store your data on their servers.
After initial hardware investment, local AI is essentially free. No monthly subscriptions ($20-120/month saved), no API costs ($0.01-0.12 per 1K tokens), and no usage limits. Heavy users save thousands per year.
Run AI anywhere without internet. Perfect for travel, remote work, areas with poor connectivity, or situations where network access is restricted. Once models are downloaded, you're completely independent.
Fine-tune models on your data, use any model you want, no content filters unless you add them, and complete control over context length, temperature, and other parameters.
GPU VRAM is the most important factor for local AI. More VRAM means larger, smarter models. Here's what different budgets can achieve.
RTX 3060 12GB or Intel Arc B580 12GB. Runs 7B-13B parameter models (Llama 3 8B, Mistral 7B, Phi-4). Good for basic chat, simple coding help, and image generation with SDXL. 30-60 tokens per second.
RTX 4070 Ti Super 16GB or RX 7900 XTX 24GB. Runs 32B parameter models (Qwen 2.5 32B, DeepSeek Coder 33B). Excellent for serious coding, complex analysis, and high-quality image generation with Flux. 40-80 tokens per second.
RTX 4090 24GB. Runs 70B parameter models (Llama 3.1 70B, Qwen 2.5 72B). Approaches GPT-4 quality for most tasks. Fast inference at 50-100 tokens per second. The sweet spot for enthusiasts.
Dual RTX 4090, RTX 6000 Ada 48GB, or A100 80GB. Runs 405B models and beyond. Full training capability. Enterprise-grade inference speeds. For researchers and production deployment.
The local AI ecosystem has matured significantly. Here are the key tools you'll use.
Free desktop app with built-in model hub. One-click downloads, automatic GPU detection, OpenAI-compatible API. The easiest way to get started with local AI. Works on Windows, macOS, and Linux.
Node-based interface for Stable Diffusion, SDXL, and Flux. More complex than Jan but offers unlimited customization. Required for advanced image workflows.
VS Code extension that connects to local models. Use as GitHub Copilot alternative with complete privacy. Works with any OpenAI-compatible API including Jan.
Connect your local LLM to documents, databases, and other knowledge sources. Essential for company knowledge bases and research applications.
Choosing the right model depends on your task and hardware. Here's a decision framework.
Llama 3.1 (8B, 70B) - Best all-around open model. Qwen 2.5 (7B-72B) - Strong multilingual, excellent at Chinese. DeepSeek V3 - Best reasoning, especially math. Mistral 7B - Fastest, Apache 2.0 license.
DeepSeek Coder V2 - Best open coding model. CodeLlama 34B - Meta's dedicated coding model. Qwen 2.5 Coder - Strong alternative. Continue.dev integration makes these excellent Copilot replacements.
Flux Dev - Best quality, excellent text rendering. SDXL - Huge ecosystem of LoRAs and fine-tunes. SD 1.5 - Runs on 8GB GPUs, massive model library.
LLaVA - Best open vision model. Llama 3.2 Vision - Meta's multimodal offering. Can analyze images, charts, screenshots, and documents.
Follow these steps to run your first local AI model in under 30 minutes.
Download from jan.ai. Install and launch. The app auto-detects your GPU and configures optimal settings.
Click Model Hub. For 8GB GPUs: Llama 3.1 8B. For 12GB: Mistral 7B or Llama 3.1 8B at higher quality. For 16GB+: Qwen 2.5 32B or DeepSeek Coder. Click download and wait (1-15 GB depending on model).
Once downloaded, click the model to load it. Start a new chat. Type your first message. You're now running AI completely locally!
Try the API (Settings > API Server) for integration with other apps. Import documents for context. Adjust generation settings like temperature and context length.
Common issues and their solutions.
Check GPU drivers are updated. Verify Jan Settings > Advanced shows your GPU. Try restarting Jan. On NVIDIA, install CUDA toolkit if not present.
Model is too large for your VRAM. Try a smaller model or lower quantization (Q4 vs Q8). Close other GPU applications. Enable CPU offloading if available.
Normal speeds: 30-100 tokens/second on GPU, 1-10 on CPU. If unexpectedly slow, check you're using GPU (not CPU). Reduce context length. Try a smaller model.
Larger models generally produce better output. Try different prompt styles. Adjust temperature (lower for factual, higher for creative). Some tasks simply need bigger models.
Check our step-by-step setup guides and GPU recommendations.