What GPU do I need to run akhilaaa3/Jev-Omni?
12.0B parameters, published in BF16. View on Hugging Face
Set up Jev-OmniJev-Omni is published by akhilaaa3 on Hugging Face, with 3,907 downloads and 375 likes to date. It's a Gemma4UnifiedForConditionalGeneration model built for text-classification, 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 | 22.3 GB | 26.7 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 3060 | 3 | $0.330/hr | ||
| FP8 (quantized) | 11.1 GB | 13.4 GB | RTX 5060 Ti | 1 | $0.188/hr |
| INT4 (quantized) | 5.6 GB | 6.7 GB | RTX 3070 | 1 | $0.088/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 Jev-Omni at its published (BF16) precision: 1× RTX 4080 Super, at $0.338/hr per GPU ($0.338/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Jev-Omni: common questions
Does Jev-Omni fit on a 32 GB GPU?
Yes. At BF16 it needs 26.7 GB of VRAM, so a 32 GB card holds it with 5.3 GB to spare. A 24 GB card is not enough for it at BF16.
What is the least VRAM Jev-Omni can run in?
6.7 GB, at INT4 (quantized), which fits an 8 GB card, against 26.7 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 Jev-Omni lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 4080 Super at $0.338/hr. At INT4 (quantized) it drops to one RTX 3070 at $0.088/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More akhilaaa3 models
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
