What GPU do I need to run OpenGVLab/InternVL2-1B?
938M parameters, published in BF16. View on Hugging Face
InternVL2-1B is published by OpenGVLab on Hugging Face, with 592,942 downloads and 83 likes to date. It's a InternVLChatModel model built for image-text-to-text, 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 InternVL2-1B 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.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More OpenGVLab models
- InternVL2-2B (2.2B, BF16)
- InternVL2_5-4B (3.7B, BF16)
- InternVL3-1B (938M, BF16)
- InternVL2-26B (25.5B, BF16)
- InternVL3-1B-hf (938M, BF16)
- InternVL3-8B (7.9B, BF16)
