What GPU do I need to run zai-org/GLM-5.3-Flash?
321.3B parameters, published in F8_E4M3. View on Hugging Face
GLM-5.3-Flash is published by zai-org on Hugging Face, with 441,348 downloads and 1,848 likes to date. It's a Glm5NextForConditionalGeneration model built for image-text-to-text, 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.
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 GLM-5.3-Flash at its published (F8_E4M3) precision: 8× L40 on massecompute, at $0.772/hr per GPU ($6.18/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 zai-org models
- GLM-OCR (1.3B, BF16)
- GLM-4.7-Flash (31.2B, BF16)
- GLM-5.2-FP8 (753.3B, F8_E4M3)
- GLM-5.2 (753.3B, BF16)
- GLM-5-FP8 (753.9B, F8_E4M3)
- GLM-5.1-FP8 (753.9B, F8_E4M3)
