Local AI roleplay: your chats, your device

Your roleplay history can become a personal library: characters you have developed, scenes you want to revisit, and stories you would rather keep private. Where that library lives matters, even before you choose the AI that writes the next reply.

“Local” can mean two different things: storing your chats on your device, or running the AI model there. Museris gives you on-device storage whether you use a cloud model or your own. Local inference adds another layer of privacy by keeping the generation of replies on your hardware too.

What local AI roleplay actually means

Local storage: where your stories live

When a chat service stores your library on its servers, it controls your access to it. An account can be deleted without notice, taking years of chats and characters with it. Policies can change, banning stories or characters that were allowed when you created them. The site itself can shut down. Without your own copy, your stories can disappear along with it.

Your stories should belong to you for as long as you want to keep them. Museris stores your characters and conversation history on your device. Access to that saved library does not depend on a hosted account staying open or a website continuing to allow your stories. You can open it offline, keep your own copies, and decide what to save or delete.

That ownership comes with looking after your copy: keep exports and backups so a lost device or cleared app data does not take your library with it. With those copies in your hands, you can preserve your stories for the long term, independently of a service’s policies or future.

You do not need a powerful GPU for any of this. Keeping your data on your device is separate from running an AI model, and you get that benefit even when you use a cloud model for replies.

Local inference: where replies are written

Inference is the process of running a model to generate a reply. With a cloud model, the app sends the prompt to an outside provider. That prompt can include your character details, lore, and conversation history, not just the message you typed.

With local inference, a model running on your own hardware handles that request instead. This adds privacy on top of local storage: both your saved library and the prompts used to write replies can stay on your machine.

Local storage does not make a cloud model private. Your chats can be saved on your device while the information needed for each reply still goes to your chosen AI provider. Keeping both storage and inference local is the more private setup.

The honest trade-off

On-device storage is useful with any model. Running the model yourself is a separate commitment: you need suitable hardware, space for model files, and some patience with setup.

Expect entry-level local models to struggle more than strong cloud models with subtle characterization, complex instructions, and long scenes. A model can load successfully and still be too slow or inconsistent for enjoyable roleplay. Better hardware lets you run larger models or longer contexts, but it does not guarantee better writing.

In return, you choose the model, control when you update it, and avoid a cloud inference subscription. Hardware and electricity still cost money, and you take responsibility for maintaining the setup.

A realistic hardware baseline

For usable roleplay, we recommend a model with at least 7 billion parameters (7B), and a GPU with at least 8 GB of video memory (VRAM) as the minimum for an entry-level local inference setup. Treat that as a starting point, not a promise of fast replies or cloud-level quality. Smaller models can be useful for experiments, but we would not recommend building your roleplay setup around them.

A quantized 7B–8B model is a reasonable first target at this level. You will still need to check the model’s memory requirements and leave room for context and other GPU use. An 8 GB card will not fit every model in that size range at every setting.

Parameter count. This describes the model’s size. Larger models generally need more memory and can offer better writing, but training and suitability for roleplay matter too.
Quantization. This reduces the memory needed to store the model’s weights, with a possible loss in quality. A 4-bit GGUF model is a practical starting point; GGUF is a file format supported by servers such as KoboldCPP and llama.cpp.
Context length. Character cards, lore, conversation history, and the reply all need room in the model’s context window. Longer contexts use more memory and can slow generation. A model’s advertised maximum context may be far beyond what your hardware can handle.

CPU-only inference is possible, but waits can interrupt the flow of a scene. Moving part of a model into system RAM can help it fit, often at a substantial speed cost. Neither is our recommended starting point for responsive roleplay.

On systems with unified memory, such as Apple Silicon Macs, the CPU and GPU share a memory pool. The operating system and other apps need part of it, so 8 GB of total unified memory is not equivalent to a dedicated GPU with 8 GB of VRAM. Check your server’s hardware guidance before choosing a model.

Try a short scene before committing to a long story. Check reply speed, whether the character stays consistent, and how the model handles a growing history. If your hardware falls short, you can still keep your library on-device and use a cloud model.

Choosing an inference server

An inference server is the program that loads your model and accepts requests from Museris. Choose one that supports your hardware and offers an OpenAI-compatible API, then follow its installation instructions.

Approachable options

KoboldCPP — runs GGUF models with a built-in web interface and is popular with roleplay users. A good first stop if you want to try local models without living in a terminal.
Jan AI — a desktop app for downloading, chatting with, and running local models. Choose it if you prefer a polished desktop experience.
Unsloth Studio — a guided interface for local models, useful as an on-ramp if you may explore the wider ecosystem later.

Options for technical users

llama.cpp — fast, flexible, and the foundation much of the local ecosystem is built on. The Museris developers use the llama.cpp server in router mode for their own testing and recommend it when you want maximum control and the newest capabilities.
Oobabooga Text Generation WebUI — an extensive interface with many loaders, extensions, and generation settings. Good if you like to experiment.

Whichever you choose, confirm it offers an OpenAI-compatible API. That is the common language between servers and apps.

Connecting it to Museris

Start your inference server and load a model.
Note the address it listens on, usually a local port such as http://127.0.0.1:5001.
In Museris, create a connection preset using the OpenAI-compatible provider and point it at that address.
Choose a model name if your server asks for one, then send a short test message.

If Museris and the server run on the same machine, keep the server bound to the loopback address (127.0.0.1) where possible. Connecting from another device requires a reachable network address and appropriate access controls. Avoid exposing the server to the public internet.

Characters work the same way

Local inference changes where replies come from, not how characters work. You still build or import character cards, attach lorebooks, and shape prompts with templates. Museris stores core characters and conversations on your device, and it imports community-standard PNG character cards, so an existing cast comes with you.

If you are moving cards from another app, the AI character card guide covers what carries over.

Privacy in practice

On-device storage and local inference protect different parts of your story. Keep both in mind when choosing a setup:

Back up your library. Device storage gives you control, but losing the device or clearing browser/app data can also mean losing locally stored chats.
Check where exports and backups go. A folder synced to a cloud service creates another copy outside your device.
Check the active connection preset. A cloud provider receives the context sent for generation and handles it under its own policies, even though Museris saves your history locally.

When cloud is the better call

A cloud model can be the more practical choice while your saved library stays on your device. Consider it when:

Your hardware cannot hold a model big enough for the scenes you want.
You need fast replies more than you need local control.
You are on a laptop that throttles under sustained load.
You would rather not maintain a server at all.

Museris makes it easy to switch between providers and models using connection presets, so you can try the same characters and scenes with different models and find the writing style you enjoy. If a provider hosts a model you are considering running locally, try it there first to see how it handles your roleplay before spending time on setup. You can keep experimenting with cloud and local options while your characters and chat history stay in the same library.

FAQ

Do I need a GPU?

For the entry-level roleplay setup we recommend, plan on a GPU with at least 8 GB of VRAM and a quantized model with at least 7B parameters. CPU-only inference is technically possible, but it can be too slow for comfortable back-and-forth scenes.

Is local AI roleplay really private?

Local storage keeps your saved library on your device. Local inference also keeps generation on your hardware, provided your connection points to your own server. A cloud connection still sends prompt context to that provider. Device security, exports, and backups matter in either setup.

Which model should I start with?

Start with a roleplay-suited model with at least 7B parameters, in a quantized GGUF version that fits your hardware with room for context. Parameter count alone does not guarantee good roleplay. The local inference server guide links to GGUF model lists.

Can I use my SillyTavern characters?

Yes. Museris imports standard PNG character cards. See the AI character card guide.

Does a local setup cost anything?

Local inference avoids cloud inference fees, but you pay for hardware, storage, and electricity. Using a cloud model with on-device chat storage still carries that provider’s charges.

Can I switch between local and cloud later?

Yes. Connection presets are separate configurations; switching does not affect your characters or history.