What GPU do I need to run zai-org/GLM-4.5V?
107.7B parameters, published in BF16. View on Hugging Face Full specs & deploy guide
Set up GLM-4.5VGLM-4.5V is published by zai-org on Hugging Face, with 41,093 downloads and 722 likes to date. It's a Glm4vMoeForConditionalGeneration 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 | 200.6 GB | 240.8 GB | RTX A6000 | 6 | $1.98/hr |
| FP8 (quantized) | 100.3 GB | 120.4 GB | RTX 4000 SFF Ada | 7 | $1.26/hr |
| INT4 (quantized) | 50.2 GB | 60.2 GB | A100 | 1 | $0.851/hr |
| cheaper alt. | RTX 3090 | 3 | $0.441/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-4.5V at its published (BF16) precision: 6× RTX A6000, at $0.330/hr per GPU ($1.98/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
GLM-4.5V: common questions
Can GLM-4.5V run on a single GPU?
No. At BF16 it needs 240.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 48.0 GB RTX A6000, and it takes 6 of them.
How many GPUs do I need to run GLM-4.5V?
6 at BF16. It needs 240.8 GB of VRAM and the cheapest capable live offer is a 48.0 GB RTX A6000, so 6 of them come to $1.98/hr in total.
Does quantizing GLM-4.5V lower the GPU bill?
Yes. At BF16 the cheapest live fit is 6 RTX A6000 cards at $1.98/hr. At INT4 (quantized) it drops to one A100 at $0.851/hr, provided a quantized checkpoint exists for it.
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-5.3-Flash (321.3B, F8_E4M3)
