Key facts
| Desktop options | LM Studio, Jan and GPT4All run models locally with a chat interface |
| Team options | Open WebUI and LibreChat add accounts, history and shared deployments |
| Backend options | Ollama, llama.cpp and vLLM serve models over OpenAI-compatible endpoints |
| Privacy model | Local inference keeps prompts on your hardware; local storage does not encrypt itself automatically |
| Remote access | Add a reverse proxy with TLS and authentication before exposing a local assistant to a network |
| Hybrid option | Plugsky is OpenAI-compatible with 30+ models for prompts that outgrow local hardware |
| Endpoint status | Chat, streaming, JSON mode, function calling, embeddings and RAG are live; audio and image endpoints are coming soon |
TL;DR
- Match the assistant to the user: desktop apps for one person, server platforms for teams.
- Keep the backend OpenAI-compatible so you can change models and add cloud fallback without rewrites.
- Local storage still needs encryption, backups and access control.
- Small quantized models handle drafting and summaries well; route complex reasoning elsewhere if needed.
- Test retrieval and citations on real documents before rolling an assistant out to colleagues.
How it works, step by step
- Define the primary use: personal drafting, document QA, coding help or a shared team workspace.
- Estimate memory: 7B-8B models at 4-bit need roughly 4-5 GB of weights plus KV cache.
- Install a backend (Ollama or llama.cpp) and confirm the OpenAI-compatible endpoint responds.
- Choose the front end: LM Studio or Jan for desktop, Open WebUI or LibreChat for teams.
- Load your documents and tune chunk size, overlap and embedding model for retrieval quality.
- Harden access: TLS, authentication, per-user separation and encrypted disks.
- Set a routing policy for prompts that exceed local capacity, and document it for users.
Try it yourself
Open the local model recommender →
Desktop versus team assistants
Desktop assistants are the simplest local option: install an app, download a model, start chatting. LM Studio pairs a model runner with a polished chat UI and an OpenAI-compatible server. Jan and GPT4All follow a similar model, with lighter interfaces and their own model catalogues. These tools suit individual work where no one else needs access.
Team assistants add identity and shared resources, which means a server. Open WebUI provides a browser workspace with user accounts, document upload and model switching. LibreChat focuses on multi-user chat with conversation history and authentication options. Both can point at a local runtime or a hosted API without changing the client experience. The operational cost is real: patching, backups, model updates and log management become someone's job.
What separates a good local assistant
Four things decide whether people keep using it. First, model compatibility: the assistant should talk to any OpenAI-compatible endpoint so you are not locked to one runtime. Second, retrieval quality: document chat lives or dies on chunking, embeddings and citation accuracy. Third, state handling: conversations, documents and settings should persist and be recoverable. Fourth, access control: accounts, per-user data separation and audit trails if more than one person uses it.
- Can it show which source chunks produced an answer?
- Can you swap the model without re-ingesting documents?
- Does it work offline, or does it silently require a network?
- Can you back up and restore conversations and indexes?
Privacy, limits and hybrid routing
Local inference keeps prompts on your hardware, but privacy has more parts than inference. Chat history and vector stores sit on disk unencrypted by default, logs may capture sensitive text, and remote access can expose the endpoint. Encrypt disks, limit log retention, and require authentication before anyone reaches the assistant from the network.
Local models also have limits. Small quantized models are strong at summarising, rewriting and routine questions but lose accuracy on long multi-document reasoning. Plan a hybrid path: keep private and routine work local, and send approved prompts to a hosted API when quality or capacity demands it. Plugsky serves chat, streaming, JSON mode, function calling, embeddings and RAG live across 30+ models through one OpenAI-compatible endpoint; audio and image generation endpoints are coming soon. Start on the free plan with plugsky-micro and plugsky-lite, and compare paid tiers on the live pricing page.
Honest comparison
| Assistant | Best for | Deployment | Multi-user | Offline capable |
|---|---|---|---|---|
| LM Studio | Single user | Desktop app | No | Yes |
| Jan | Single user, open source | Desktop app | No | Yes |
| Open WebUI | Teams | Server or Docker | Yes | Yes |
| LibreChat | Teams needing history | Server or Docker | Yes | With local backend |
| Hybrid with Plugsky | Mixed private and heavy work | Local plus hosted API | Yes | Degrades to local |
Frequently asked questions
Are local AI assistants free?
The assistant software is often open source, but you pay in hardware, electricity and your time on updates. Hosted plans trade that effort for a monthly fee.
Can a local assistant work without internet?
Yes, if the model and tools are local. Document retrieval, chat and generation work offline; web search and any external API do not.
How much RAM or VRAM do I need?
A 7B-8B model at 4-bit needs roughly 4-5 GB of weights plus KV cache, so 8 GB of VRAM or unified memory is a realistic entry point, and 16 GB or more is comfortable.
Is a local assistant more private than ChatGPT?
Inference data stays on your machine, which removes a major exposure path. You still need disk encryption, access control and careful logging to keep it that way.
Can I share one local assistant with my team?
Yes, with a server-based front end such as Open WebUI or LibreChat, plus authentication, per-user separation and a machine sized for concurrent requests.
What if my documents are too large for a local model's context?
Use retrieval: store chunks in a vector database and pass only the most relevant passages to the model. This keeps context small and answers grounded.
Can I move from a local assistant to a hosted API later?
Yes, if the assistant speaks OpenAI-compatible endpoints. Changing the base URL and model name is usually enough; Plugsky offers 30+ models behind that same interface.