Zai Org Glm 4 7 speed on RTX 3090 and quantization-level VRAM fit.
RTX 3090 does not meet the minimum VRAM requirement for Q4 inference of Zai Org Glm 4 7. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 3090 lacks sufficient VRAM for comfortable Zai Org Glm 4 7 operation with Q4 quantization.
Your 24GB GPU is 156GB short of the 180GB minimum.
Options: (1) Try Q2 or Q3 quantization for lower VRAM requirements, (2) Consider cloud GPU rental, (3) Upgrade to a GPU with at least 16GB VRAM.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 180GB | 24GB | 18.50 tok/s | ❌ Not recommended |
| Q8 | 359GB | 24GB | 12.95 tok/s | ❌ Not recommended |
| FP16 | 717GB | 24GB | 7.03 tok/s | ❌ Not recommended |
Check current pricing links for RTX 3090 and similar cards.
Open RTX 3090 buy links →Use workload-focused recommendations before committing to a purchase.
Browse best GPU guides →Compare complete systems if you want ready-to-run hardware.
Compare prebuilt systems →Your GPU doesn't meet the VRAM requirements. Run Zai Org Glm 4 7 on cloud GPU instantly.
RTX 3090 is not a comfortable Q4 fit for Zai Org Glm 4 7 (about 180GB needed).
Q4 inference is estimated to need about 180GB VRAM on this page, while RTX 3090 has 24GB available.
Try lower-bit quantization, choose a smaller model, or move to a higher-VRAM GPU from the alternatives list.