What GPU do I need to run AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4?
6.2B parameters, published in BF16. View on Hugging Face
Set up Gemma-4-E4B…RD-HERETIC-NVFP4Gemma-4-E4B-DECKARD-HERETIC-NVFP4 is published by AEON-7 on Hugging Face, with 248,141 downloads and 1 like to date. It's a Gemma4ForConditionalGeneration model built for text-generation, 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 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Gemma-4-E4B-DECKARD-HERETIC-NVFP4 at its published (BF16) precision: 1× RTX A4000 on hyperstack, at $0.151/hr per GPU ($0.151/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Gemma-4-E4B-DECKARD-HERETIC-NVFP4: common questions
Does Gemma-4-E4B-DECKARD-HERETIC-NVFP4 fit on a 16 GB GPU?
Yes. At BF16 it needs 13.9 GB of VRAM, so a 16 GB card holds it with 2.1 GB to spare. A 12 GB card is not enough for it at BF16.
What is the least VRAM Gemma-4-E4B-DECKARD-HERETIC-NVFP4 can run in?
3.5 GB, at INT4 (quantized), which fits a 6 GB card, against 13.9 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 Gemma-4-E4B-DECKARD-HERETIC-NVFP4 lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A4000 on hyperstack at $0.151/hr. At INT4 (quantized) it drops to one RTX 3060 on simplepod at $0.080/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More AEON-7 models
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- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
