❝ Can my laptop run an uncensored AI? ❞
Yes — and a bigger one than anyone in this thread told you.
A Mixture-of-Experts model holds 30 billion parameters but only switches on ~3 billion per word. Your 32 GB DDR4 is the warehouse; the CPU carries three boxes at a time. That is the whole reason a 30B can feel quicker than an 8B on your Dell. 
The 30-second version of why this works on an i7-1255U
- dense models spend CPU time on every parameter, every token
- MoE models spend it on the active slice only — 3B of 30B
- so your RAM decides whether it fits, and your CPU only decides how fast the slice moves
- at Q4 those 24–30B files land at 13–18 GB — inside your 32 GB with room for the OS
- on-prem-llm-cpu already measured gpt-oss-20B and Qwen3-Coder-30B-A3B on a GPU-less Xeon, 168 runs, llama.cpp vs ik_llama.cpp — read these numbers before you download anything

❝ Do I need a jailbreak prompt? ❞
No. You need a file, not a spell.
Your own post above is the proof — omnirush answered “safety rules cannot be modified or overridden.” A jailbreak argues with a filter; the filter gets patched; the argument expires. An uncensored open-weight model has no filter to argue with, because the refusal was never in your laptop to begin with. 
Start here — the one file that is both uncensored and built for your CPU:
huihui_ai/qwen3-abliterated:30b-a3b — Qwen3’s 30B MoE, abliterated. Only ~3B active, so it moves on DDR4 while the dense 24Bs crawl, and the refusal direction is gone from the weights. One command: ollama run huihui_ai/qwen3-abliterated:30b-a3b · ~18 GB at Q4
Then, if you want a different flavour — these are all filtered-out weights, sizes at Q4:
Or skip the download queue entirely — make your own uncensored model tonight 
Every “uncensored” model on that list was made by one tool, and it runs on a CPU:
- Heretic — fully automatic censorship removal. 27,800 stars, 4,000+ models produced, 13M downloads, one command, and it optimises the ablation until the model still thinks while it stops refusing
- heretic-gguf — the same thing applied straight to a quantized GGUF, on llama.cpp. Stock Heretic loads full bf16 and eats your RAM; this fork works on the 4-bit file you already have
- a real run log on an Intel iGPU, no NVIDIA card — proof it does not need the GPU you do not have
- the cross-architecture comparison paper — which ablation method actually survives on which architecture, instead of vibes
That is the difference between borrowing somebody’s uncensored file and owning the recipe: the next base model that lands next month, you can free yourself in an afternoon.
Then carry it — llamafile wraps a model and llama.cpp into one executable. Put it on a USB stick, double-click it on any laptop, and the assistant answers with no install, no internet, no account.
Real CPU numbers, not vibes — 
- 10 small LLMs measured on a 2-core laptop CPU (Oct 2026): speed, RAM, first-token time and a 20-question check. Their winner on trade-off: Gemma 4 E2B
- CPU-only numbers per model: Phi-4 Mini 3.8B at 2.3 GB runs 12 tokens/sec with no GPU — that is your floor, not your ceiling
- edge-cpu-inference — a toolkit that profiles any GGUF on your own CPU: memory footprint, first-token time, tokens/sec, before you commit to an 18 GB download
- xebench — the “which engine is fastest” shoot-out, and its honest headline is there is no single fastest engine: the ranking flips by model and by CPU
- one more MoE worth knowing: LFM2-24B-A2B — 24B total, 2B active, the lightest of the big-MoE family
A 2026 abliterated-model guide sorted by hardware · Uncensor Index (real RAM need + a blunt “not for this” per model) · RunThisModel (filters the same list by the machine you own)
❝ Then what do I do tonight? ❞
Install ik_llama.cpp — the CPU-tuned llama.cpp fork; its -fmoe flag keeps the experts in RAM where they belong
Pull one model at Q4 — the abliterated 30B-A3B first. Not three at Q8; you have RAM, not VRAM
Ask it the thing Grok refused — no preamble, no “pretend you are”, and notice there is nothing to argue with
No patience for downloads?
Venice.ai gives 10 text prompts a day on an uncensored default, and if you need a GPU for an hour, Kaggle gives ~30 free hours a week — keep it to harmless text, their terms are strict.