Self-Hosting

Strategic guide to hosting large language models locally, on consumer hardware, in containers, or in the cloud. Compare tools, performance trade-offs, and cost considerations.

Self-hosting LLMs keeps data, models, and inference under your control-a practical path to AI sovereignty for teams, enterprises, nations. Here: what sovereign AI is, which aspects and methods are used to build it, how LLM self-hosting fits in, how countries are addressing the challenge.

Running large language models locally gives you privacy, offline capability, and zero API costs. This benchmark reveals exactly what one can expect from 9 popular LLMs on Ollama on an RTX 4080.

The Go ecosystem continues to thrive with innovative projects spanning AI tooling, self-hosted applications, and developer infrastructure. This overview analyzes the top trending Go repositories on GitHub this month.

Today we are looking at the top-level consumer GPUs, and RAM modules. Specifically I’m looking at RTX-5080 and RTX-5090 prices, and 32GB (2x16GB) DDR5 6000.

Open WebUI is a powerful, extensible, and feature-rich self-hosted web interface for interacting with large language models.

vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs) developed by UC Berkeley’s Sky Computing Lab.

The NVIDIA DGX Spark (GB10 Grace Blackwell) is now available in Australia at major PC retailers with local stock. If you’ve been following the global DGX Spark pricing and availability, you’ll be interested to know that Australian pricing ranges from $6,249 to $7,999 AUD depending on storage configuration and retailer.

Cognee is a Python framework for building knowledge graphs from documents using LLMs. But does it work with self-hosted models?

Choosing the Best LLM for Cognee demands balancing graph-building quality, hallucination rates, and hardware constraints. Cognee excels with larger, low-hallucination models (32B+) via Ollama but mid-size options work for lighter setups.

Ollama’s Python library now includes native OLlama web search capabilities. With just a few lines of code, you can augment your local LLMs with real-time information from the web, reducing hallucinations and improving accuracy.

Choosing the right vector store can make or break your RAG application’s performance, cost, and scalability. This comprehensive comparison covers the most popular options in 2024-2025.

Ollama’s Web Search API lets you augment local LLMs with real-time web information. This guide shows you how to implement web search capabilities in Go, from simple API calls to full-featured search agents.

The memory market is experiencing unprecedented price volatility in late 2025, with RAM prices surging dramatically across all segments.

Local deployment of LLMs has become increasingly popular as developers and organizations seek enhanced privacy, reduced latency, and greater control over their AI infrastructure.

Let’s compare prices for top-level consumer GPUs, that are suitable for LLMs in particular and AI in general. Specifically I’m looking at RTX-5080 and RTX-5090 prices.