Ollama, Gemma, censored and uncensored, all in one go
ChatGPT, Claude and Gemini are all cloud models.
The computing happens on someone else's servers and you connect to it.
But an AI model can also run right on your own computer. That is a local model, sometimes called an on-device model.
This page clears up the basics in one pass: cloud vs local, what Ollama is, what Gemma is, and how censored differs from uncensored,
so the words stop sounding like fog next time you meet them.
Cloud model: like ordering delivery.
The chef is good (the model is strong) and the menu is long (all the features are there),
but you pay, you wait (queues or rate limits), and your food passes through someone else's kitchen (your data goes to their servers).
Local model: like cooking at home.
Free, cook whenever you feel like it, ingredients (your data) never leave the house,
but you are limited by your kitchen equipment (your hardware), so the food usually does not match a five-star chef.
Local models live mainly on memory (RAM). Check how much you have, then find your row:
| Your memory | Tier you can run | What that means |
|---|---|---|
| 8GB | 7B class | Entry level on a small machine, good for trying it out |
| 16GB | 12B class | The sweet spot most people are on, mainstream laptops land here |
| 32GB and up | 35B class | Starts to come close to cloud models |
There are plenty of models, but your first one does not need a comparison spreadsheet:
on a computer with 16GB of memory, download Gemma 4 12B (from Google) and pick the standard build.
On 8GB take Qwen3 7B, on 32GB and up take Qwen3.6-35B.
Once you are comfortable and have a specific need (writing without getting refused, say), come back to the full comparison table below.
Local models suit work that is high volume, repetitive and does not need top quality. That part is free from now on:
Because the division of labour looks a lot like hiring:
it is on call 24 hours a day, runs several things at once, shows up instantly, never takes leave or quits,
and the cost is a subscription or an electricity bill instead of a salary.
The difference: creativity, reading people, and carrying final responsibility still need a human.
So the sensible setup is to hand the repetitive chores to AI (a free local model already carries a big chunk of that),
and keep human time for the work only humans can do.
These are actual download counts from the official Ollama model library (checked Aug 2026), so they show the local models the most people are really running:
| Rank | Model | Family／source | Downloads |
|---|---|---|---|
| 1 | Llama 3.1 | Meta | about 118 million |
| 2 | DeepSeek-R1 | DeepSeek | about 79 million |
| 5 | Gemma3 | about 39.6 million |
Source: download statistics from the official Ollama model library (checked Aug 2026), see public write-ups such as Morph and BenchLM.
This table is compiled from public sources and community reviews. These are not numbers I benchmarked myself, so treat them as direction only.
| Model | Family／source | Parameters | Censorship | Best for |
|---|---|---|---|---|
| Qwen3.6-35B-A3B | Alibaba Qwen | 35B (only 3B active) | standard | The best all-rounder at the 32GB memory tier, fast like a small model, capable like a big one |
| Gemma 4 12B beginners pick this | Google Gemma | 12B | standard | A capable little assistant at the 16GB memory tier, the right first model |
| Qwen3 7B | Alibaba Qwen | 7B | standard | The strongest coder in the 7-8B class |
| Dolphin 3.0 Mistral | Community fine-tune (based on Mistral) | depends on the build | uncensored | Low refusal rate, small footprint, well regarded for writing and creative work |
| Hermes 3 (Llama 3.3) | Community fine-tune (based on Llama) | depends on the build | uncensored | Low refusal rate, strong at following instructions |
| Gemma Abliterated 9B | Community de-censored build (based on Gemma) | 9B | uncensored | Reasoning barely drops once the refusal layer is removed, runs at the 12GB tier |
Compiled from public write-ups on Hugging Face, Morph, InsiderLLM and Apidog (checked Aug 2026). What counts is how it feels when you run it yourself.
No, not at this stage.
A local model is the cost-saving workhorse, handling volume that does not need top quality;
anything important, anything you deliver, anything needing the strongest reasoning is safer on a top cloud model.
No. With a tool like Ollama you open the app, pick a model to download, and start chatting. It is about the same as installing any app.
Uncensored models are public open-source technology, and downloading and using them is not illegal.
But once the refusal layer is gone, more of the responsibility for the output sits with you,
so use your own judgement about what you do with it.
The three are different wrappers around the same core:
underneath it is all the llama.cpp open-source engine, and Ollama and LM Studio are friendlier shells on top of it.
For beginners, Ollama (simplest commands) or LM Studio (full graphical interface) are both the mainstream answer;
people chasing maximum performance drive llama.cpp directly, and advanced Mac users add Apple's MLX framework for another step up.
Start with whatever is easy. Go a layer down when it stops being enough.
Yes, just pick a smaller model (7B class, for example),
and the more memory you have, the bigger and smarter the model you can run.
Any computer with 16GB of memory or more is enough to start.
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