Home / Will it run? / GLM-4.7-Flash
Z.ai (Zhipu) · GLM-4.7
Will GLM-4.7-Flash run on my GPU?
30B parameters (mixture of experts, 3B active per token, but every expert must sit in memory), 202,752-token context, MIT. At Q4_K_M the weights come to about 16.8 GiB; add 1.5 GiB for the runtime and you need roughly 18.3 GiB of VRAM before context.
Weights by quant
GiBQ4_K_M · the everyday quant16.8 GiB
Q8_0 · near-lossless29.6 GiB
FP16 · full weights55.9 GiB
Runtime reserve1.5 GiB
Card by card
4 of 18 fit at Q4Headroom is what remains for the KV cache after weights and the 1.5 GiB reserve. How far it stretches depends on the architecture: grouped-query models are frugal, older dense models are not. If the table says tight, plan on a shorter context or a lower quant. Parameter count from the model's own card. The Buy column opens live Amazon listings; those links may earn us a commission.