What GPU do I need to run mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated?
8.0B parameters, published in BF16. View on Hugging Face
Meta-Llama-3.1-8B-Instruct-abliterated is published by mlabonne on Hugging Face, with 11,756 downloads and 214 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Meta-Llama-3.1-8B-Instruct-abliterated at its published (BF16) precision: 1× RTX 3090 on simplepod, at $0.160/hr per GPU ($0.160/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More mlabonne models
- Qwen3-30B-A3B-abliterated (30.5B, F32)
- NeuralMonarch-7B (7.2B, F16)
- NeuralDaredevil-8B-abliterated (8.0B, F16)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
