What GPU do I need to run ornith-ai/Ornith-1.5-397B?

403.4B parameters, published in BF16. View on Hugging Face

403.4B
Parameters
BF16
Native precision
Qwen3_5MoeForConditionalGeneration
Architecture
text-generation
Pipeline

Ornith-1.5-397B is published by ornith-ai on Hugging Face, with 283,560 downloads and 84 likes to date. It's a Qwen3_5MoeForConditionalGeneration 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
751.4 GB
901.7 GB
No capable live offer found
FP8 (quantized)
375.7 GB
450.8 GB
RTX PRO 6000 (runpod)
5
$8.20/hr
INT4 (quantized)
187.8 GB
225.4 GB
A40 (runpod)
5
$2.20/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.

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

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