Top 5 models i can run with my hardware? No AI lobotomization
Since whether a model is useful depends heavily on what you want to accomplish, I don’t think there is a simple universal “top 5” I can give you. But I can outline some useful ways to search and evaluate models under that assumption:
Short version
I would not treat this as “find the five best uncensored models.”
I would turn it into a branching process:
use case
→ hardware tier
→ refusal/willingness requirement
→ parent-model capability
→ community reputation for the same task
→ local test under fixed settings
→ keep the one you actually use
For your hardware, my first pass would be:
- Start with strong 8B–14B models on 16GB VRAM.
- Treat 20B–27B/32B models as stretch candidates , usually with quantization/context/speed tradeoffs.
- Use UGI Leaderboard as a refusal / willingness filter , not as a universal intelligence ranking.
- For uncensored finetunes, merges, or abliterated models, check the parent model.
- Use HF Forum / Reddit / X / model-card discussions / GitHub issues as candidate-discovery signals , not authority.
- Test 3–5 candidates yourself under the same quant, runtime, chat template, context length, and sampling settings.
The shortest version:
Leaderboards and reputation build the shortlist. Your own prompts make the decision.
1. Split the problem into axes
“Uncensored” can be a useful search term if your main frustration is over-refusal. But I would not read it as “smarter.”
A model can be less likely to refuse without being better at reasoning, coding, factuality, writing, or long-context work.
| Axis | What to look at |
|---|---|
| Refusal / willingness | whether it answers or avoids a topic |
| General capability | reasoning, coding, writing, summarization, instruction-following |
| Topic competence | whether it handles your actual subject area well |
| Local usability | speed, VRAM/RAM use, quantization, context length, runtime |
| Personal usefulness | whether you would actually use it again |
UGI is relevant mostly to the first axis. It is useful, but it is not the whole decision.
More detail: why these axes are different (click for more details)
2. Be careful with the “lobotomized” framing
I understand what people usually mean by “lobotomized”: a model that refuses too much, moralizes, avoids useful discussion, or feels worse than an earlier version.
But for modern chat models, refusal behavior is not always just a dumb external filter bolted onto an otherwise unchanged model.
The final behavior often comes from several layers:
- pretraining,
- instruction tuning,
- preference tuning / RLHF-like training,
- safety tuning,
- system prompt,
- chat template,
- runtime behavior,
- inference settings.
So an uncensored finetune may reduce refusals or change style, but that does not automatically add reasoning ability, knowledge, factuality, or reliability.
A better framing is:
less refusal changes one behavior axis; capability still has to be tested separately.
More detail: why less refusal is not the same as more intelligence (click for more details)
3. Sensitive-topic competence is another axis
Knowing sensitive facts is not the same as handling a sensitive domain well.
In some fields, understanding harmful failure modes is part of understanding the useful side. Medicine, toxicology, cybersecurity, and safety engineering are obvious examples. But a model that can output more sensitive details is not automatically better at the useful task.
For sensitive domains, I would want a model that can handle the relationship between:
- useful knowledge,
- misuse risk,
- uncertainty,
- mitigation,
- domain norms,
- appropriate abstraction level.
So I would evaluate sensitive-domain usefulness by whether the model can help with the actual safe/useful task, not only by whether it refuses less.
More detail: why dangerous knowledge is not the same as domain competence (click for more details)
4. Use UGI as one branch, not the whole tree
I would use UGI Leaderboard to build a shortlist, not to make the final decision.
| Use UGI for… | Do not use UGI alone for… |
|---|---|
| refusal / willingness | coding quality |
| controversial-topic engagement | math ability |
| finding low-refusal candidates | writing style |
| comparing refusal-heavy behavior | long-context summarization |
| deciding what to test first | local speed on your GPU |
| finding models worth inspecting | usefulness for your actual prompts |
UGI is a useful signal. It is not the whole map.
More detail: how I would combine UGI with other signals (click for more details)
5. Check the parent model first
For uncensored finetunes, merges, abliterated models, and distills, I would always check the parent model.
A finetune can change:
- style,
- refusal behavior,
- roleplay behavior,
- task bias,
- verbosity,
- tone,
- willingness.
But much of the broad capability usually still comes from the parent model:
- tokenizer,
- architecture,
- pretraining quality,
- multilingual ability,
- context behavior,
- coding/math priors,
- general instruction-following quality.
That does not mean a strong parent guarantees a strong finetune. A bad finetune, bad merge, wrong chat template, poor dataset, or too-low quant can still make the result worse.
If an uncensored model is based on Qwen, Llama, Mistral, Gemma, DeepSeek, etc., I would look at the parent too.
More detail: model-card checklist (click for more details)
6. 16GB VRAM branch
With 16GB VRAM, I would branch by size like this:
| Tier | How I would treat it |
|---|---|
| 8B–14B | first serious test range |
| 20B–27B/32B | stretch range; try with quantization and realistic speed/context expectations |
| 70B+ | not my first stop unless experimenting with offload/slowness |
“Fits” is not the same as “usable.”
A model can fit in memory and still be too slow, unstable, repetitive, or unpleasant at the context length you need.
Also: context length matters because KV cache uses memory too. A model that feels fine at 4k context may feel different at 16k or 32k.
More detail: variables that affect local usability (click for more details)
7. Template branch
Before judging a model, verify the chat template.
A wrong template can make a good model look much worse than it is.
This matters especially for:
- finetunes,
- abliterated models,
- RP models,
- GGUF repos,
- Ollama imports,
- text-completion backends,
- models with unusual system/user/assistant formatting.
If a model looks “dumb,” I would check the template before blaming the model.
More detail: template checklist (click for more details)
8. Model-family starting points, not a fixed top 5
I would think in terms of model families and use cases, not one universal list.
| If you care about… | First places I would look | Caveat |
|---|---|---|
| General reasoning / coding / multilingual | Qwen-family 8B–14B | Test thinking mode, non-thinking mode, template, speed |
| Writing / multilingual / long-form assistant use | Gemma-family or similar 12B-ish models | Compare with your own writing prompts |
| General chat / instruction following / function calling | Mistral Small-class quants | 24B is heavier; 16GB VRAM means quant/context/speed tradeoffs |
| Reasoning-style / math-ish work | DeepSeek-R1-Distill-Qwen-style 14B | May be verbose or awkward for normal chat |
| Creative / RP / uncensored variants | Qwen / Llama / Mistral / Gemma derivatives | Read parent model, tuning method, template, warnings |
| Low-VRAM comfort / speed | smaller 7B–14B GGUFs | largest possible model may not be the most usable |
This table is a starting map, not a verdict.
More detail: why these families (click for more details)
9. Community reputation branch
If your use case is already specific, community reputation can be useful.
The branch is:
specific task
→ search people doing that same task
→ find repeated model families
→ inspect model cards/issues/templates/quants
→ test early, but do not trust blindly
Search HF Forum, Reddit/LocalLLaMA, X, model-card discussions, and GitHub issues for people doing the same kind of work:
- coding,
- creative writing,
- RP,
- RAG,
- math/reasoning,
- long-context summarization,
- multilingual use,
- low-VRAM GGUF use.
If the same model family keeps coming up from people doing the same task, I would test it early.
But I would treat that as a shortcut, not authority.
Likes, downloads, Reddit recommendations, X hype, model-card claims, and leaderboard ranks are all public signals. They help choose what to test first. They do not decide what is useful for you.
More detail: how I would search community reputation (click for more details)
10. RP / SillyTavern branch
RP / creative writing is a useful example of why “smartest model” is not enough.
A good RP model is not necessarily the model with the highest general benchmark score. For RP, people may care more about:
- character consistency,
- prose style,
- emotional nuance,
- dialogue rhythm,
- lore adherence,
- not speaking for the user,
- response length,
- low repetition,
- scene pacing,
- compatibility with character cards and presets.
So the branch is:
RP / creative writing
→ check RP-specific reputation
→ check SillyTavern/template compatibility
→ test with your own character cards and presets
→ judge by role fit, not only benchmark rank
More detail: SillyTavern and role-specific behavior (click for more details)
11. Variant-browsing branch
If you want to browse this ecosystem, I would look at people/projects like:
| Source | Why I would look there |
|---|---|
| DavidAU | creative / uncensored / merge / finetune experiments |
| grimjim | technical abliteration / refusal-direction experiments and writeups |
| huihui-ai | many abliterated variants |
| mradermacher | GGUF / imatrix quants |
But I would treat these as places to gather candidates, not final answers.
The useful part is not only the model list. It is reading parent model, method, warnings, template, quant, settings, comments, discussions, and known failure modes.
More detail: how I would read these pages (click for more details)
12. Practical decision tree
If I were in your position, I would turn this into a small decision tree rather than a top-5 list.
Step 1: Pick your real use case
Choose 2–3:
- complex discussion,
- coding,
- math/reasoning,
- writing,
- RP / creative writing,
- long-context summarization,
- RAG,
- multilingual use,
- low-refusal sensitive discussion,
- speed / low-VRAM comfort.
Step 2: Choose candidate direction
| Main use case | First candidate direction |
|---|---|
| General local assistant | strong 8B–14B general model |
| Coding | Qwen/Coder-reputation models, coding-specific community recommendations |
| Math/reasoning | reasoning-style 14B candidates, e.g. DeepSeek-R1-Distill-Qwen-like models |
| Writing | Gemma/Qwen/Mistral/Llama derivatives tested on your writing prompts |
| RP / creative | RP-specific community models, SillyTavern presets, creative-writing collections |
| Sensitive discussion | UGI/W10 shortlist + parent-model capability check |
| Long context | test actual context length, not advertised max context only |
| Speed / comfort | smaller Q4/Q5 GGUFs before heavier stretch models |
Step 3: Build a 3–5 model shortlist
| Slot | Candidate type | Why |
|---|---|---|
| A | one strong 8B–14B general model | baseline |
| B | one model known for your main use case | task fit |
| C | one UGI-high / lower-refusal candidate | refusal / willingness |
| D | optionally the parent model of an uncensored finetune | compare derivative vs source |
| E | optionally one heavier 24B-ish quantized model | see if size helps enough to justify speed/context cost |
Step 4: Keep the runtime variables fixed
- same runtime,
- same quant tier,
- same context length,
- same chat template,
- same system prompt,
- same sampling settings,
- same prompt order.
Otherwise you may be comparing runtime/template/quant differences, not model quality.
Step 5: Run a tiny benchmark made of your own prompts
Use 5–10 prompts. Include:
- one complex discussion prompt,
- one controversial-but-safe prompt,
- one factual question where you can check the answer,
- one multi-step reasoning prompt,
- one writing/rewrite prompt,
- one coding prompt, if you code,
- one long-context summarization prompt, if you need long context,
- one strict-format prompt,
- one prompt that often triggers over-refusal,
- one repeat test to check stability.
Step 6: Decide by actual usefulness
The winner is not necessarily the highest-ranked model.
The winner is the one you actually want to use.
13. Tiny local test matrix
I would not test twenty models first. I would test three or four properly.
| Item | Model A | Model B | Model C | Model D |
|---|---|---|---|---|
| Parent model | ||||
| Method: base / instruct / SFT / merge / abliterated / distilled | ||||
| Quant | ||||
| Runtime | ||||
| Chat template verified? | ||||
| Context length tested | ||||
| Tokens/sec | ||||
| VRAM/RAM use | ||||
| Refusal behavior | ||||
| Reasoning usefulness | ||||
| Factuality / hallucination | ||||
| Writing style | ||||
| Repetition / derailment | ||||
| Would I use it daily? |
That will give you a better answer than a generic top-5 list.
14. Common traps
The main traps are treating labels, popularity, leaderboard ranks, or VRAM fit as final answers.
More detail: things I would not assume (click for more details)
Final practical path
I would do this:
pick 2–3 real use cases
→ start with 8B–14B candidates
→ add one task-specific community favorite
→ add one UGI-high / lower-refusal candidate
→ optionally add one heavier 24B-ish quant
→ compare parent vs uncensored derivative if relevant
→ verify chat template and quant
→ run 5–10 of your own prompts
→ keep the model you actually want to use
Public signals can save time. They can help avoid starting from thousands of models.
But no leaderboard, model card, HF account, Reddit thread, X post, download count, or like count can know what you are trying to accomplish.
That part has to be tested.
Discussion in the ATmosphere