What GPU do I need to run NousResearch/Meta-Llama-3-8B-Instruct?
8.0B parameters, published in BF16. View on Hugging Face
Meta-Llama-3-8B-Instruct is published by NousResearch on Hugging Face, with 147,669 downloads and 103 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-3-8B-Instruct 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 NousResearch models
- Hermes-3-Llama-3.1-8B (8.0B, BF16)
- Llama-2-7b-hf (6.7B, F16)
- Meta-Llama-3.1-8B-Instruct (8.0B, BF16)
- Meta-Llama-3-8B (8.0B, BF16)
- Qwen3-0.6B (752M, BF16)
- Qwen3-8B (8.2B, BF16)
