What GPU do I need to run tencent/Hy3?

298.8B parameters, published in BF16. View on Hugging Face

298.8B
Parameters
BF16
Native precision
HYV3ForCausalLM
Architecture
text-generation
Pipeline

Hy3 is published by tencent on Hugging Face, with 12,216 downloads and 961 likes to date. It's a HYV3ForCausalLM 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
556.5 GB
667.8 GB
RTX PRO 6000 (runpod)
7
$11.48/hr
FP8 (quantized)
278.3 GB
333.9 GB
L40 (massecompute)
7
$5.41/hr
INT4 (quantized)
139.1 GB
167.0 GB
RTX 3090 (simplepod)
7
$1.12/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 Hy3 at its published (BF16) precision: 7× RTX PRO 6000 on runpod, at $1.64/hr per GPU ($11.48/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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