What GPU do I need to run gnani/gnani-evon-v3.3-30B-A3B?
31.7B parameters, published in BF16. View on Hugging FaceGated
Set up gnani-evon-v3.3-30B-A3Bgnani-evon-v3.3-30B-A3B is published by gnani on Hugging Face, with 359 downloads and 33 likes to date. It's a NemotronHForCausalLM model built for text-generation, published natively in BF16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
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) |
|---|---|---|---|---|---|
| BF16 | 59.1 GB | 71.0 GB | A100 | 1 | $1.31/hr |
| cheaper alt. | RTX A5000 | 3 | $0.528/hr | ||
| FP8 (quantized) | 29.6 GB | 35.5 GB | RTX 4090 | 1 | $0.441/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr | ||
| INT4 (quantized) | 14.8 GB | 17.7 GB | RTX A5000 | 1 | $0.176/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 gnani-evon-v3.3-30B-A3B at its published (BF16) precision: 1× A100, at $1.31/hr per GPU ($1.31/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gnani-evon-v3.3-30B-A3B: common questions
Can gnani-evon-v3.3-30B-A3B run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 71.0 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.31/hr.
Do I need approval to download gnani-evon-v3.3-30B-A3B?
Yes. gnani gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 71.0 GB the model needs once you have them.
What is the least VRAM gnani-evon-v3.3-30B-A3B can run in?
17.7 GB, at INT4 (quantized), which fits a 24 GB card, against 71.0 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.
Does quantizing gnani-evon-v3.3-30B-A3B lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.31/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
