Local AI Infrastructure
Run LLMs, deploy inference engines, and build AI pipelines on your own hardware. Complete guides for Ollama, vLLM, vector databases, and GPU-accelerated workloads.
Overview
Local AI infrastructure lets you run machine learning models on your own hardware without cloud dependencies. This covers the full stack: inference engines like Ollama and vLLM, vector databases for semantic search, model management, and GPU acceleration with CUDA and ROCm.
Running AI locally gives you privacy, predictable performance, and no per-token costs. Your data never leaves your network, and you control which models run and how they are configured.
Key Components
Inference Engines
Ollama, vLLM, llama.cpp for running models locally
Vector Databases
Chroma, Qdrant, Milvus for embeddings and RAG
GPU Acceleration
CUDA, ROCm, and hardware encoding for inference
Model Quantization
GGUF, AWQ, GPTQ for efficient model deployment
RAG Pipelines
Retrieval-augmented generation with local documents
API Endpoints
OpenAI-compatible APIs for application integration
Use Cases
Private AI Assistant
Run a local ChatGPT alternative on your homelab. Process documents, answer questions, and generate content without sending data to third parties.
Document Intelligence
Process PDFs, contracts, and research papers with local LLMs. Extract information, summarize content, and query private documents using RAG pipelines.
AI-Powered Monitoring
Analyze logs, detect anomalies, and predict system failures using ML models running on your infrastructure.
Why Run AI Locally
Privacy: Your data never leaves your infrastructure. Process sensitive documents, proprietary code, or personal information without third-party exposure.
Cost Control: No per-token API fees. After hardware investment, inference costs are essentially zero. Run unlimited queries without usage limits.
Latency: Local inference means millisecond response times. No network hops, no API rate limits, just direct processing on your hardware.
Offline Operation: Work without internet access. Suitable for field deployments, secure environments, or areas with unreliable connectivity.