What GPU do I need to run moonshotai/Moonlight-16B-A3B?

16.0B parameters, published in BF16. View on Hugging Face

16.0B
Parameters
BF16
Native precision
DeepseekV3ForCausalLM
Architecture
text-generation
Pipeline

Moonlight-16B-A3B is published by moonshotai on Hugging Face, with 19,280 downloads and 116 likes to date. It's a DeepseekV3ForCausalLM 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.

Precision
Weight size
Required VRAM
Cheapest live fit
GPUs needed
Est. $/hr (full fit)
BF16
29.7 GB
35.7 GB
A40 (runpod)
1
$0.440/hr
cheaper alt.
RTX 4070 (simplepod)
3
$0.240/hr
FP8 (quantized)
14.9 GB
17.8 GB
RTX 4090 (runpod)
1
$0.340/hr
cheaper alt.
RTX 4070 (simplepod)
2
$0.160/hr
INT4 (quantized)
7.4 GB
8.9 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 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.

Cheapest way to run Moonlight-16B-A3B 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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