What GPU do I need to run tencent/EVIE-Preview-4.5B?

4.5B parameters, published in BF16. View on Hugging Face

Set up EVIE-Preview-4.5B
4.5B
Parameters
BF16
Native precision
ColQwen3_5
Architecture
visual-document-retrieval
Pipeline

EVIE-Preview-4.5B is published by tencent on Hugging Face, with 2,065 downloads and 93 likes to date. It's a ColQwen3_5 model built for visual-document-retrieval, 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
8.5 GB
10.1 GB
RTX 3060 (simplepod)
1
$0.080/hr
FP8 (quantized)
4.2 GB
5.1 GB
RTX 4080 (akash)
1
$0.158/hr
INT4 (quantized)
2.1 GB
2.5 GB
RTX 3070 (simplepod)
1
$0.050/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 EVIE-Preview-4.5B at its published (BF16) precision: 1× RTX 3060 on simplepod, at $0.080/hr per GPU ($0.080/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.

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