What GPU do I need to run tencent/Hy3-FP8?

298.8B parameters, published in F8_E4M3. View on Hugging Face

298.8B
Parameters
F8_E4M3
Native precision
HYV3ForCausalLM
Architecture
text-generation
Pipeline

Hy3-FP8 is published by tencent on Hugging Face, with 40,013 downloads and 70 likes to date. It's a HYV3ForCausalLM model built for text-generation, published natively in F8_E4M3.

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)
FP8 (native)
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-FP8 at its published (F8_E4M3) precision: 7× L40 on massecompute, at $0.772/hr per GPU ($5.41/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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