What GPU do I need to run zai-org/GLM-5.3-Flash-BF16?

321.3B parameters, published in BF16. View on Hugging Face

Set up GLM-5.3-Flash-BF16
321.3B
Parameters
BF16
Native precision
Glm5NextForConditionalGeneration
Architecture
image-text-to-text
Pipeline

GLM-5.3-Flash-BF16 is published by zai-org on Hugging Face, with 9,183 downloads and 55 likes to date. It's a Glm5NextForConditionalGeneration model built for image-text-to-text, 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
598.5 GB
718.2 GB
RTX PRO 6000 (simplepod)
8
$12.72/hr
FP8 (quantized)
299.3 GB
359.1 GB
L40 (runpod)
8
$5.52/hr
INT4 (quantized)
149.6 GB
179.6 GB
RTX 3090 (simplepod)
8
$1.28/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 GLM-5.3-Flash-BF16 at its published (BF16) precision: 8× RTX PRO 6000 on simplepod, at $1.59/hr per GPU ($12.72/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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