A practical open-source LLM stack for teams starting from zero
A compact reference for choosing local model runners, inference servers, model libraries, retrieval layers, and evaluation tools.
An open-source LLM stack does not have to start with a large platform decision. For most teams, the first useful stack is a small chain: model runner, application layer, retrieval layer, and evaluation loop.
Start with the runtime question
Local runners are useful for prototyping, privacy-sensitive demos, and offline experiments. Dedicated inference servers become more important when throughput, batching, and latency are the main constraints.
Separate model choice from application choice
Do not let one impressive demo lock the whole stack. Keep the application layer able to swap models, because model quality, licensing, memory requirements, and serving cost change faster than the product workflow.
Add retrieval only when the task needs it
RAG is valuable when answers depend on private or fast-changing documents. It is not a default improvement for every chatbot. If the answer can be generated from the prompt and model knowledge, retrieval adds moving parts without much benefit.
Compare tools by failure mode
When shortlisting open-source tools, write down what will break first: model memory, slow responses, weak retrieval, poor observability, or difficult deployment. That list is more useful than a generic feature checklist.