What GPU do I need to run HuggingFaceTB/SmolVLM2-2.2B-Instruct?
2.2B parameters, published in F32. View on Hugging Face
SmolVLM2-2.2B-Instruct is published by HuggingFaceTB on Hugging Face, with 162,729 downloads and 331 likes to date. It's a SmolVLMForConditionalGeneration model built for image-text-to-text, published natively in F32.
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 SmolVLM2-2.2B-Instruct at its published (F32) precision: 1× RTX 4070 on simplepod, at $0.080/hr per GPU ($0.080/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 HuggingFaceTB models
- SmolLM2-135M (135M, BF16)
- SmolVLM2-500M-Video-Instruct (507M, F32)
- SmolLM2-135M-Instruct (135M, BF16)
- SmolLM3-3B-Base (3.1B, BF16)
- SmolLM3-3B (3.1B, BF16)
- SmolVLM-256M-Instruct (256M, BF16)
