Deploy and Host

Deploy a private, OpenAI-compatible LLM inference server on Railway in minutes. Drop GGUF-format models into a persistent volume and instantly serve chat completions, text completions, and embeddings through standard OpenAI API endpoints — no GPU, no API keys from third parties, no data leaving your infrastructure.

About Hosting

LLaMA.cpp runs as a single Docker container using llama-cpp-python, optimized for Railway's Hobby tier with a ~50–200 MB baseline RAM footprint. Models live in a persistent Railway volume and load lazily on the first request, so builds stay fast and redeploys never re-download your weights. The server exposes OpenAI-compatible endpoints (/v1/chat/completions, /v1/completions, /v1/models, /v1/embeddings) that work as a drop-in backend for OpenAI SDKs, LangChain, and any ChatGPT-style UI.

Why Deploy

• No GPU required — runs entirely on CPU with multi-threaded inference (Railway has no GPU instances)
• OpenAI-compatible — point any OpenAI client, SDK, or UI at your instance and it just works
• Privacy-first — all inference stays in your Railway project; nothing is sent to external LLM providers
• Cost-effective — Hobby tier handles small-to-mid models (up to ~7B params) comfortably
• Fast startup — model loads on first request, not at build time; ~10–30 seconds to first token
• Small image — ~300 MB vs 2–5 GB for full Ollama images
• Hugging Face integration — huggingface_hub pre-installed; pull GGUF models with one MODEL_REPO_ID env var

Common Use Cases

• Private ChatGPT-like chat API for personal or team use
• Backend LLM endpoint for AI-powered apps, bots, and workflows
• Embedding generation for RAG pipelines and semantic search
• Model evaluation and prompt-engineering playground
• Paired with Open WebUI for a full self-hosted AI chat interface
• Local GGUF model serving for development and prototyping

Dependencies for LLaMA.cpp

Deployment Dependencies

• A Railway account (free Hobby tier works for small models)
• GGUF-format model file(s) — download from Hugging Face Hub or mount an existing volume

Optional Dependencies

• HF_TOKEN — only needed to pull from private Hugging Face repos