You have 12 proven, actively maintained tools for running LLMs and vector databases offline. All are in production use. The question is not whether they work—they do—but which one fits your actual constraints: your hardware, your interface preference, and what you're building.
This guide compares only the mature, actively developed tools verified in our directory. We exclude experimental projects and focus on what differs in practice: RAM floor, GPU dependency, deployment model, and the type of work each tool is designed for.
Both run on 8GB RAM, both work offline, both are MIT or AGPL licensed. Pick GPT4All if you want a single executable that starts and runs a chat interface with no setup. It's the fastest path from download to conversation.
Pick text-generation-webui if you need flexibility: support for many backends (gguf, transformers, exllama), parameter tuning, or batch processing. Setup takes longer, but you gain configurability. License is AGPL-3.0, so check your redistribution requirements.
LocalAI is the pragmatic choice if you're on CPU or have modest GPU. It's MIT licensed, claims OpenAI API compatibility, and requires 8GB RAM minimum. Use it when you want to swap your LLM inference backend without changing your application code.
text-generation-inference is Hugging Face's production inference server. It requires a GPU with 16GB+ VRAM and is Apache-2.0 licensed. Pick it if you're already in the HF ecosystem, need inference speed over compatibility, or plan to run at scale. CPU-only won't work here.
koboldcpp is a fork of llama.cpp packaged as one executable with a UI. It runs on 4GB+ RAM, CPU only, AGPL-3.0 licensed. Use it when you want the simplest possible single-file deployment with a built-in interface.
llama-cpp-python is the MIT-licensed Python binding to llama.cpp. It's the tool for developers embedding LLM inference into Python applications. Same 4GB+ RAM requirement, same CPU-only design. Choose this if you're writing code, not clicking buttons.
MLC-LLM is different: it compiles LLMs for edge, mobile, and GPU targets. Apache-2.0 licensed, 4GB+ RAM, optional GPU. Pick it if you're deploying to phones, edge servers, or multiple hardware targets and want one unified compiler approach. Setup and compilation time are higher.
Haystack is an Apache-2.0 licensed pipeline framework for retrieval-augmented generation. It doesn't dictate which LLM or vector database you use—you plug them in. Pick Haystack if you're building search or question-answering systems and want to swap components without rewriting pipelines.
For vector storage itself, choose based on your data volume and integration style:
Chroma: Apache-2.0, 2GB+ RAM, Python-native, embedded. Best for small-to-medium datasets and Python developers. Simple API, minimal ops.
Weaviate: BSD-3-Clause, 4GB+ RAM, GraphQL-first, modular. Better for larger datasets, multi-client access, and structured queries. More operational overhead than Chroma.
Faiss: MIT licensed, Meta's similarity search library. It's not a full database—it's a search index. Pick Faiss if you're building your own storage layer on top and need speed at scale.
pgvector: PostgreSQL License, runs as a Postgres extension. If you already operate Postgres, add pgvector instead of another database. Otherwise, start with Chroma or Weaviate.