Llama 3.3 70B Abliterated and the Hardware It Needs

If you run models on your own machine, the refusals get old fast — a prompt that is pure engineering still comes back with a lecture. There is a version of Meta's Llama 3.3 70B with that behavior stripped out of the weights, and the specs are clear enough to tell whether your hardware can hold it.

The bar is high, because this is a 70B model. You are looking at two 24GB cards or one 64GB Mac Studio, and roughly 42GB of free disk.

What Happened

This is Llama 3.3 70B with abliteration applied — the refusal directional vectors are removed from the model weights themselves. What is left, according to the recommendation, is general intelligence in the GPT-4o class that follows technical instructions without moralizing.

It is part four of a running list of uncensored, open-weight models. The hardware it wants:

• VRAM: 2x 24GB (RTX 3090 or 4090) for a 4-bit quant like EXL2 or GGUF Q4, or 1x Mac Studio with 64GB of unified memory
• System RAM: 64GB to 128GB
• Storage: about 42GB for GGUF Q4_K_M
• Context window: 128k tokens

Why It Matters

Abliteration changes what you can ask. Prompts that a stock model deflects — low-level engineering, red-team work, anything that trips a refusal — go through, because the model is not carrying the refusal directions anymore.

The catch is the shopping list. Two 24GB GPUs or a 64GB Mac Studio is not a laptop-and-go setup, and the 4-bit download alone wants 42GB of drive space. If your machine clears that bar, the 128k context means long documents and long code files fit in one pass, and everything stays on hardware you own.