Your GPU is honest. It is also 24GB.
Ollama is the right answer for a lot of people and this page will say so more than once. Pull a model, run it on your own machine, and nothing you type ever leaves the building. No subscription, no rate limit, no terms of service, no company that can change its policy next quarter and take the model away. You control quantization and sampling, you can run it on a plane, and after the hardware is paid for the marginal cost is electricity. For privacy absolutism, that is not merely a competing option — it is a category OpenRogue cannot enter.
The ceiling is hardware, and it is a hard ceiling. Consumer VRAM decides which models you can run and at what quality, and the models at the top of the uncensored lineup are not reachable from a desktop: Hermes 3 405B needs hundreds of gigabytes of GPU memory, and WizardLM-2 8x22B is a 141B-parameter mixture of experts. Local users mostly run heavily compressed versions of much smaller models, which is a real quality difference and not a snobbish one. OpenRogue is the trade in the other direction — no install, no weights to manage, the whole lineup available from any device including a phone, history that syncs, and the 405B in the same dropdown as the 8B.
| Feature | OpenRogue | Ollama |
|---|---|---|
| Uncensored | Yes | Yes |
| Cost after setup | Free tier — 10 messages a day · Rogue $10/mo · Pro $20/mo | Electricity |
| Privacy | No training on your prompts; history exportable and deletable | Nothing leaves your machine |
| Works offline | No | Yes |
| Largest model realistically available | Hermes 3 405B | Whatever your VRAM allows |
| Setup | None | Install, pull weights, tune, maintain |
| Works on a phone or a borrowed laptop | Yes | No |
| Sampler and quantization control | No | Yes |
| Vision, file uploads, voice input, Projects, sync | Yes | Depends on the front end you add |
| Rate limits | Metered compute balance | None |
Choose OpenRogue when you want frontier-scale uncensored models without owning the hardware, when you move between devices, or when you would simply rather not maintain a local stack — the models, the front end, the file handling and the updates — to have a conversation.
Choose Ollama if privacy is non-negotiable or you already own the GPU. Nothing leaving your machine is a guarantee no hosted service can match, the running cost is effectively zero, and there are no limits of any kind. If a mid-size model on your own hardware meets your needs, running it locally is the better decision and you should.
If you have the GPU and a mid-size model does what you need, genuinely yes. Local wins on privacy, cost and control, and those are not small advantages. The case for hosted is the models you cannot fit, the setup you would rather not maintain, and access from devices that will never run a 70B.
Hermes 3 405B is the clearest example — a full fine-tune of Llama 3.1 405B needs hundreds of gigabytes of GPU memory, which puts it out of reach of any consumer setup. WizardLM-2 8x22B, a 141B-parameter mixture of experts, is similarly impractical at home. Both are in the dropdown here.
No, and it cannot be. Your messages travel to a server to be answered — that is inherent to hosting. What OpenRogue commits to is not training on your prompts and not selling them on, with history you own and can export or delete at any time. If your threat model requires that nothing leaves your machine, local is the only correct answer.
They are the same families — Dolphin, the Hermes line, Euryale, Magnum, Rocinante, UnslopNemo are open-weights models the local community runs too. The practical difference is size and compression: locally you are usually running a smaller model at a lower precision to fit it, where a hosted service is not constrained by your card.
Plenty of people do, and it is a sensible split. Keep a small local model for anything genuinely sensitive or for working offline, and use a hosted lineup when you want a 70B or a 405B, a long-context session, or access from a phone. They solve different problems and neither one obsoletes the other.
The free tier is capped daily. Paid plans replace that with a monthly compute balance shared across every model — efficient models barely move it, frontier models move it more — and you can see the balance before you spend it. Local has no limits at all, which remains one of its best arguments.