The ultimate guide to building a local LLM homelab (2025)

Running a local LLM at home or in a small lab is no longer just for experts. With tools like Ollama, llama.cpp, vLLM, and Open WebUI, you can create a powerful, private, and customizable AI setup. But the real challenge isn’t just getting a model running, it’s building a stack that’s flexible, secure, and easy to maintain. This guide by InteligenAI walks you through every step, from hardware selection to security best practices, based on commonly reported benchmarks and practices from the local LLM community

This guide does not cover training foundation models or replacing cloud-scale inference. It focuses on practical, maintainable local and small-team deployments.

Step 1: Define your goals and hardware

Before you install a single tool, ask yourself: What do you want to do with your local LLM? Are you tinkering with AI, building internal tools, or supporting a small team? Your answer will shape your hardware and software choices. Note that CPU inference is suitable for experimentation or batch processing, but expect high latency for interactive use unless models are heavily quantized.

• Solo tinkerer: A single GPU (e.g., RTX 4070/4080) is enough for most 7–14B models.
• Small team lab: Go for 24–48 GB VRAM (e.g., RTX 4090 or dual GPUs) if you want to run larger models or support multiple users.
• Prosumer production: For high concurrency or SLA requirements, consider 64+ GB VRAM and multi-GPU setups.

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Step 2: Choose your serving engine

The serving engine is the heart of your setup. Each has strengths and trade-offs:

• llama.cpp: Portable, runs on CPU and GPU, supports GGUF quantization, and is great for experimentation. Use it if you want maximum flexibility and don’t need high throughput.
• Ollama: Easy to set up, great for beginners, and supports a wide range of models. It’s less customizable than llama.cpp but offers a smooth experience.

Note: Ollama internally relies on llama.cpp for many models, but abstracts away most configuration and optimization details.

• vLLM: Built for high-throughput, multi-user scenarios. If you need fast responses and can invest in GPU resources, vLLM is the go-to.
• SGLang: It focuses on programmable generation, structured decoding, and agent-style execution, and is often used alongside or instead of vLLM in research-heavy or agentic setups.
• TabbyAPI/ExLlamaV2/V3: Best for running Llama-family models at maximum speed on CUDA.

Engine	Single-user latency	Concurrency	Setup complexity
llama.cpp	Medium	Low	Medium
Ollama	Medium	Low–Medium	Low
vLLM	Low	High	High
SGLang	Low	Medium–High	High

Step 3: Set up your frotnend

A good frontend makes your LLM accessible and user-friendly. Popular choices include:

• Open WebUI: Feature-rich, supports Ollama, llama.cpp, and more. Ideal for power users who want a lot of control.
• LM Studio: Desktop app, beginner-friendly, supports MLX natively. Great for getting started quickly.
• Aider/OpenHands: Terminal-based, perfect for coding and automation.

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Step 4: Add RAG and web search

RAG makes your LLM smarter by giving it access to your documents and the web.

• Offline RAG: Use a chunker, embedding model, and vector store to index your local files. Plug this into your frontend or workflow engine.
• Online RAG: For web search, SearXNG is a privacy-focused option, while Tavily and Jina offer paid APIs tuned for LLM agents.

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Step 5: Secure Your Setup

Security is critical, especially if your LLM is accessible beyond localhost or used by a team.

Follow these best practices:

• Use a VPN: Tailscale, headscale, or netbird keep your services private and secure.
• Enable authentication: Protect your UIs and APIs with passwords at minimum, and add 2FA via a reverse proxy (e.g., Authelia, Authentik) if services are exposed.
• Limit exposed services: Only expose what you need, and use firewalls to block unauthorized access.

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Step 6: Monitor and evaluate

• Model evaluation: Use lm-evaluation-harness for benchmarking and Promptfoo for application-level testing.
• Observability: Tools like LangFuse help you track latency, cost, and error rates.
• Logging: Keep logs of all activity, and scrub any personally identifiable information.

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Step 7: Build workflows and agents

For advanced use cases, orchestrate your LLMs with workflow engines:

• Dify, Flowise, LangFlow: No-code/low-code tools for building multi-step flows and agents.
• n8n, Open WebUI Pipelines: More advanced options for custom workflows and automation.

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Step 8: Fine-Tune and optimize

As your needs evolve, fine-tune your models and optimize your stack:

• Quantization: Use INT4, INT8, or FP16 to balance speed and quality.
• Fine-tuning: Lightweight fine-tuning or LoRA can adapt your models to specific tasks.

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Here are three references builds for different scenarios:

• Low-end: Ubuntu + llama.cpp (GGUF) + Open WebUI + SearXNG + Tailscale
• Mid: Ubuntu + vLLM (AWQ) + LiteLLM + Open WebUI + Dify + LangFuse + Tailscale
• High-end: Ubuntu + SGLang (multi-GPU) + LiteLLM + Dify + Tavily + LangFuse + headscale

Start simple, experiment, and gradually add complexity as your needs grow. By following this guide, you’ll create a stack that’s not only powerful and flexible but also secure and maintainable.