Local LLMs
The core promise of self-hosting: a language model that answers on your machine, on your terms, with your data never leaving the box. These guides cover the serving stacks (Ollama, vLLM, llama.cpp), the quantization trade-offs that make models fit, and the frontends that make them usable.
AI Prompt Engineering for Self-Hosted LLMs
Learn practical prompt engineering techniques for self-hosted LLMs, with a focus on smaller models, structure, retrieval, and repeatable production behavior.
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GGUF Format Explained: Why Self-Hosted AI Users Keep Running Into It
A practical explanation of the GGUF model format, including why it exists, how it differs from raw checkpoints, and when to use it for self-hosted inference.
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LibreChat vs OpenWebUI: Which Self-Hosted AI Chat Interface Fits Better?
A practical comparison of LibreChat and OpenWebUI covering deployment style, model support, user experience, admin needs, and ideal use cases.
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llama.cpp Setup and Optimization for Self-Hosted Inference
How to set up and optimize llama.cpp for local inference, with real build commands, quantized model choices, context sizing, and tuning on CPU and GPU.
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LLM Fine-Tuning on Consumer Hardware: What Actually Works in 2026
A practical guide to fine-tuning large language models on consumer hardware, including VRAM limits, QLoRA workflows, and dataset prep.
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LoRA Training Guide for Beginners: Fine-Tune Models Without Huge Hardware
A beginner-friendly guide to LoRA training, covering what LoRA is, how it works, dataset preparation, common settings, and practical mistakes to avoid.
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RAG vs Fine-Tuning: When to Use Each for Real AI Systems
Understand the practical difference between retrieval-augmented generation and fine-tuning, including cost, maintenance, data freshness, and when each approach makes sense.
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Self-Hosted AI Model Evaluation Tools: What to Measure and Why
A practical guide to self-hosted AI model evaluation tools, including what to measure, how to avoid misleading benchmarks, and how to build repeatable eval workflows.
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CPU vs GPU for AI Inference: Which One Makes Sense?
Compare CPU vs GPU for AI inference, including cost, latency, throughput, power use, and when each option makes sense for self-hosted model serving.
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Local AI Model Serving: What It Takes to Run Models Reliably
A practical look at local AI model serving, including runtimes, APIs, hardware planning, and the operational habits that make self-hosted inference sustainable.
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How to Set Up Ollama for Local AI on Linux or macOS
A practical Ollama setup guide covering installation, model pulls, API usage, and hardware expectations for a useful local AI endpoint.
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Open-Source Model Hosting: How to Serve Models You Control
A practical guide to open-source model hosting, including storage, runtimes, APIs, and the tradeoffs involved in serving models on infrastructure you control.
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Quantization for Small Servers: More Model, Less Hardware
Learn how quantization helps small servers run useful AI models by reducing memory use, improving feasibility, and balancing quality against speed.
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Best Self-Hosted Chat UI Options for Local LLMs
Explore what makes a good self-hosted chat UI for local LLMs, from authentication and model switching to document upload and API compatibility.
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Self-Hosted LLM Inference: A Practical Guide for Small Teams
Learn how self-hosted LLM inference works, what hardware matters, and how small teams can run private, reliable model serving without overbuilding.
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vLLM Deployment Guide: Fast OpenAI-Compatible Model Serving
Understand when to use vLLM, how to deploy it, and why it is popular for fast, OpenAI-compatible serving of open models on GPUs.
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