What GPU do I need to run maya-research/Veena?
3.8B parameters, published in BF16. View on Hugging Face
Set up VeenaVeena is published by maya-research on Hugging Face, with 14,195 downloads and 239 likes to date. It's a LlamaForCausalLM model built for text-to-speech, 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 7.0 GB | 8.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 3.5 GB | 4.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.8 GB | 2.1 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 Veena at its published (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Veena: common questions
Does Veena fit on a 12 GB GPU?
Yes. At BF16 it needs 8.5 GB of VRAM, so a 12 GB card holds it with 3.5 GB to spare. An 8 GB card is not enough for it at BF16.
What is the least VRAM Veena can run in?
2.1 GB, at INT4 (quantized), which fits a 6 GB card, against 8.5 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.
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
