What GPU do I need to run unsloth/GLM-5.3-Flash?

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

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

GLM-5.3-Flash is published by unsloth on Hugging Face, with 1,190 downloads and 16 likes to date. It's a Glm5NextForConditionalGeneration 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
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 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.

More unsloth models

Ready when you are

Your next GPU already
has your environment on it.

Sign up in 60 seconds. Pay for the GPU minutes you actually use.

© 2026 Aquanode. All rights reserved.

All trademarks, logos and brand names are the property of their respective owners.