Serve GGUF Models with llama-server on a VPS
Run llama-server as a long-lived CPU process, bind GGUF weights to localhost, and expose chat routes on an Intel Xeon Platinum VPS without a GPU.
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34 practical AI Infrastructure articles from Netbay Engineering — written for operators running Linux and Windows VPS in India.
34 articles in this topic
Run llama-server as a long-lived CPU process, bind GGUF weights to localhost, and expose chat routes on an Intel Xeon Platinum VPS without a GPU.
Split markdown by headings and code by functions so RAG retrieves complete units instead of random 512-token windows on your private VPS corpus.
Turn off nginx proxy buffering and gzip for Server-Sent Events so tokens from a remote model API reach the browser as they generate live on Ubuntu.
Point existing OpenAI clients at llama-server on localhost so a small GGUF model serves chat completions on a CPU VPS in Lucknow without a GPU.
Store float32 embeddings in SQLite or Postgres on one VPS, skip a hosted vector database, and run honest nearest-neighbor search you can backup.
Send easy prompts to a cheap model and escalate only when a classifier, token budget, or failed JSON parse says the expensive model is worth it.
Install Ollama on Ubuntu 24.04, pull a small 3B GGUF-backed model, and run it under systemd on an Intel Xeon Platinum CPU VPS without a GPU.
Encode docs on CPU with MiniLM or llama.cpp, normalize 384-d vectors, and keep embeddings on one Lucknow VPS instead of a billed embedding API.
Keep OpenAI and Anthropic keys in a root-owned systemd EnvironmentFile on Ubuntu so a Lucknow VPS never leaks model credentials through git or logs.
Compile llama.cpp, load a Q4 GGUF 3B to 8B model, and generate text on an Intel Xeon Platinum CPU VPS without a GPU on High-Speed SSD in Lucknow.