GLM-4.7-Flash (30B-A3B)
GLM-4.7-Flash (30B-A3B) by Zhipu AI (Z.ai) (30B parameters, MoE with 3B active per token). At Q4_K_M with an 8K context it needs about 18.7 GiB of memory. Z.ai's 30B MoE with 3B active and a compressed (MLA) KV cache, under MIT; it fits a 20–24 GB card at 4-bit.
Last updated: 2026-10-13
Model facts
Run it
ollama run glm-4.7-flash:q4_K_M
Memory needed
Weights + KV cache + runtime overhead, in GiB. Lower quantization = less memory, slightly lower quality.
| Quantization | 4K context | 8K context | 32K context |
|---|---|---|---|
| Q4_K_M | 18.5 GiB | 18.7 GiB | 19.9 GiB |
| Q5_K_M | 21.6 GiB | 21.8 GiB | 23.0 GiB |
| Q6_K | 24.9 GiB | 25.1 GiB | 26.3 GiB |
| Q8_0 | 31.9 GiB | 32.1 GiB | 33.3 GiB |
| FP16 | 59.4 GiB | 59.6 GiB | 60.8 GiB |
Which hardware runs it
Best quantization that fits each device with a 8K context. Estimates, not benchmarks. See the methodology.
Frequently asked questions
How much VRAM does GLM-4.7-Flash (30B-A3B) need?
With an 8K context, about 18.7 GiB at Q4_K_M and 32.1 GiB at Q8_0 (weights + KV cache + runtime overhead). Longer contexts need more memory for the KV cache.
Can GLM-4.7-Flash (30B-A3B) run on an GeForce RTX 4090?
Yes. GLM-4.7-Flash (30B-A3B) fits on the GeForce RTX 4090 (24 GB) at Q5_K_M with an 8K context, using about 21.8 GiB, at an estimated 331 tokens/s.
Can I run GLM-4.7-Flash (30B-A3B) on a Mac?
Yes. GLM-4.7-Flash (30B-A3B) fits on the M3 Ultra (96 GB) at FP16 with an 8K context, using about 59.6 GiB, at an estimated 89 tokens/s.
Can GLM-4.7-Flash (30B-A3B) be used commercially?
GLM-4.7-Flash (30B-A3B) is released under MIT. The licence allows commercial use (read its conditions).