What GPU do I need to run HuggingFaceTB/SmolLM3-3B?
3.1B parameters, published in BF16. View on Hugging Face
Set up SmolLM3-3BSmolLM3-3B is published by HuggingFaceTB on Hugging Face, with 585,759 downloads and 1,016 likes to date. It's a SmolLM3ForCausalLM 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 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run SmolLM3-3B at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
SmolLM3-3B: common questions
Does SmolLM3-3B fit on a 8 GB GPU?
Yes. At BF16 it needs 6.9 GB of VRAM, so an 8 GB card holds it with 1.1 GB to spare. A 6 GB card is not enough for it at BF16.
What is the least VRAM SmolLM3-3B can run in?
1.7 GB, at INT4 (quantized), which fits a 6 GB card, against 6.9 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More HuggingFaceTB models
- SmolLM2-135M (135M, BF16)
- SmolVLM2-500M-Video-Instruct (507M, F32)
- SmolLM2-135M-Instruct (135M, BF16)
- SmolLM3-3B-Base (3.1B, BF16)
- SmolVLM-256M-Instruct (256M, BF16)
- SmolLM2-360M (362M, BF16)
