LLM Hosting

Ollama is at its happiest when it is treated like a local daemon: the CLI and your apps talk to a loopback HTTP API, and the rest of the network never finds out it exists.

Ollama works great on bare metal. It gets even more interesting when you treat it like a service: a stable endpoint, pinned versions, persistent storage, and a GPU that is either available or it is not.

Running Ollama behind a reverse proxy is the simplest way to get HTTPS, optional access control, and predictable streaming behaviour.

SGLang is a high-performance serving framework for large language models and multimodal models, built to deliver low-latency and high-throughput inference across everything from a single GPU to distributed clusters.

Soon you are juggling vLLM, llama.cpp, and more—each stack on its own port. Everything downstream still wants one /v1 base URL; otherwise you keep shuffling ports, profiles, and one-off scripts. llama-swap is the /v1 proxy before those stacks.

LocalAI is a self-hosted, local-first inference server designed to behave like a drop-in OpenAI API for running AI workloads on your own hardware (laptop, workstation, or on-prem server).

I keep coming back to llama.cpp for local inference—it gives you control that Ollama and others abstract away, and it just works. Easy to run GGUF models interactively with llama-cli or expose an OpenAI-compatible HTTP API with llama-server.

Self-hosting LLMs keeps data, models, and inference under your control-a practical path to AI sovereignty for teams, enterprises, nations.

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.

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.

Running LLMs locally is now practical for developers, startups, and even enterprise teams.
But choosing the right tool — Ollama, vLLM, LM Studio, LocalAI or others — depends on your goals:

Configuring context sizes in Docker Model Runner is more complex than it should be.

Docker Model Runner is Docker’s official tool for running AI models locally, but enabling NVidia GPU acceleration in Docker Model Runner requires specific configuration.

Docker Model Runner (DMR) is Docker’s official solution for running AI models locally, introduced in April 2025. This cheatsheet provides a quick reference for all essential commands, configurations, and best practices.

Running large language models (LLMs) locally has become increasingly popular for privacy, cost control, and offline capabilities. The landscape shifted significantly in April 2025 when Docker introduced Docker Model Runner (DMR), its official solution for AI model deployment.