What GPU do I need to run sensenova/SenseNova-U1.5-8B-MoT?

17.5B parameters, published in BF16. View on Hugging Face

Set up SenseNova-U1.5-8B-MoT
17.5B
Parameters
BF16
Native precision
NEOChatModel
Architecture
any-to-any
Pipeline

SenseNova-U1.5-8B-MoT is published by sensenova on Hugging Face, with 6,733 downloads and 200 likes to date. It's a NEOChatModel model built for any-to-any, 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
32.7 GB
39.2 GB
A40 (runpod)
1
$0.440/hr
cheaper alt.
RTX 3070 (simplepod)
5
$0.250/hr
FP8 (quantized)
16.3 GB
19.6 GB
RTX 4000 Ada (runpod)
1
$0.200/hr
INT4 (quantized)
8.2 GB
9.8 GB
RTX 3080 (simplepod)
1
$0.070/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 SenseNova-U1.5-8B-MoT at its published (BF16) precision: 1× A40 on runpod, at $0.440/hr per GPU ($0.440/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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