Key facts
| Runtime | Ollama, LM Studio or llama.cpp serve models with no network |
| Knowledge | A local vector store enables document chat and citations |
| Interface | Desktop or web UI running on the same machine or LAN |
| Offline scope | Chat, summarization and retrieval work fully offline |
| Sync plan | Carry model and content updates in on a schedule or approved media |
| Hardware | 8-16 GB machines run small quantized models comfortably |
| Hosted option | Plugsky provides OpenAI-compatible chat and embeddings when online |
| Endpoint status | Chat, streaming, tools, JSON mode and embeddings are live |
TL;DR
- A local runtime plus a local vector store covers chat and document Q&A offline.
- Pick a small 4-bit model for responsiveness on everyday laptops.
- Design the interface to work with networking disabled, not degraded.
- Plan updates, because an offline assistant cannot pull new models itself.
- Optional sync to a hosted API keeps quality high when you are online.
How it works, step by step
- Decide the assistant's jobs: chat, summarization, document Q&A or coding help.
- Install a local runtime and pull a small 4-bit model that fits the machine.
- Add a local vector store and ingest the documents the assistant needs.
- Build or choose an interface that runs without network access.
- Test with connectivity disabled, including cold starts.
- Define a sync process for model and content updates.
- Optionally add a hosted fallback to use when online.
Try it yourself
Open the local model recommender →
What an offline assistant needs
Three parts make an offline assistant useful: a model runtime, a knowledge layer and an interface. The runtime serves a quantized model on the machine. The knowledge layer parses your documents, embeds them locally and retrieves relevant passages. The interface is a desktop app or a small web app served on localhost or the LAN.
Choose the model by the machine. A 7B-8B model at 4-bit is comfortable on 16 GB and handles chat, summarization and grounded answers. On 8 GB, use a 3B-4B model and keep context modest. Everything else is application code.
Building the retrieval side
Document chat is what separates a toy from a tool. Parse files into text, split into chunks with source metadata, embed with a local embedding model and store vectors locally. At question time, retrieve a small set of chunks and instruct the model to answer only from them, returning citations.
- Keep metadata: source, page or section, and date, so answers can be verified.
- Filter before generating: restrict retrieval to the corpus the user is allowed to see.
- Log retrieval: record which chunks were used, because that is what makes failures debuggable.
Parsing and chunking decide answer quality more than the generator does.
Updates, sync and hybrid mode
Offline does not mean static. Models improve, runtimes ship security fixes and your documents change. Define a refresh cycle: export updated content from the system of record, transfer it in, re-embed changed files, and keep a manifest of what is loaded.
If the assistant is sometimes online, add a hybrid path. Plugsky serves 30+ models over an OpenAI-compatible API for chat, streaming, tools, JSON mode, embeddings, RAG and agents, all live; audio, image, moderation, files and batch endpoints are coming soon. The same client code can switch between local and hosted with a base URL change. See pricing for plans and start free with plugsky-micro and plugsky-lite.
Honest comparison
| Concern | Offline assistant | Plugsky hosted API | Check before deciding |
|---|---|---|---|
| Availability | Works with no connectivity | Needs a network path | Where it will be used |
| Knowledge | Local documents you load | Your data, when online | Corpus size and freshness |
| Model choice | Limited to local memory | 30+ models on one API | Quality bar |
| Updates | Manual or scheduled transfer | Continuous | Maintenance capacity |
| Privacy | Nothing leaves the device | Region and deployment choices | Data classification |
Frequently asked questions
Can an AI assistant really work fully offline?
Yes, for chat, summarization, extraction and document Q&A. Features that depend on live data, web search or cloud APIs will not work unless you provide an offline data source.
What hardware does an offline assistant need?
A modern laptop with 16 GB of memory runs a quantized 7B-8B model plus a local vector store. Machines with 8 GB should use a 3B-4B model.
How do I keep knowledge current offline?
Define a scheduled content refresh: export updated documents from your main system, transfer them in, and re-embed the changed files. Keep a manifest so you know which version is loaded.
Can I add voice offline?
Speech-to-text and text-to-speech models can run locally, but they add memory and dependency weight. Test them separately before bundling them into the assistant.
How do I handle model updates?
Treat models like software artefacts: pin versions, verify checksums and roll out deliberately. Never let the assistant fetch models automatically in a restricted environment.
Should I add a cloud fallback?
If the assistant is sometimes online, yes. Hybrid routing keeps privacy-sensitive work local and sends hard tasks to an OpenAI-compatible endpoint when available.
How do I test an offline assistant?
Disable networking at the OS level and run a script of real tasks from cold start to answer, including document retrieval and error handling.