What GPU do I need to run HuggingFaceTB/SmolVLM2-2.2B-Instruct?
2.2B parameters, published in F32. View on Hugging Face
Set up SmolVLM2-2.2B-InstructSmolVLM2-2.2B-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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 8.4 GB | 10.0 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 2.1 GB | 2.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.0 GB | 1.3 GB | RTX 5060 Ti | 1 | $0.110/hr |
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 SmolVLM2-2.2B-Instruct at its published (F32) precision: 1× V100, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
SmolVLM2-2.2B-Instruct: common questions
Does SmolVLM2-2.2B-Instruct fit on a 12 GB GPU?
Yes. At FP32 it needs 10.0 GB of VRAM, so a 12 GB card holds it with 2.0 GB to spare. An 8 GB card is not enough for it at FP32.
Can SmolVLM2-2.2B-Instruct run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 8.4 GB, or 10.0 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 4.2 GB, or 5.0 GB with overhead. That moves it onto a 6 GB card instead of a 12 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM SmolVLM2-2.2B-Instruct can run in?
1.3 GB, at INT4 (quantized), which fits a 6 GB card, against 10.0 GB at FP32. 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 SmolVLM models
- SmolVLM2-500M-Video-Instruct (507M, F32)
- SmolVLM-500M-Instruct (507M, BF16)
- SmolVLM-256M-Instruct (256M, BF16)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
