Can the RTX 4090 run GLM-4.7-Flash?
Yes. GLM-4.7-Flash fits the RTX 4090's 24 GB at Q4_K_M (about 19.8 GB needed), running at an estimated 241 tok/s. There's room for up to Q5_K_M if you want higher quality.
Every quantization
GLM-4.7-Flash (31.2B, 3B active) on 24 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 18 GB | 19.8 GB | Fits | ~241 tok/s |
| Q5_K_M | 22 GB | 23.8 GB | Tight | ~197 tok/s |
| Q8_0 | 33 GB | 34.8 GB | Too big | - |
| FP16 | 62 GB | 63.8 GB | Too big | - |
Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 4090's 1008 GB/s bandwidth (XiongjieDai/GPU-Benchmarks-on-LLM-Inference). Long contexts need more memory and run slower.
Run it
Copy, paste, go
llama-server -hf unsloth/GLM-4.7-Flash-GGUF -ngl 99Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full GLM-4.7-Flash details →
Compare
Same model, other hardware - and other models on this card
GLM-4.7-Flash on other hardware
- RTX 5090~396 tok/s
- RTX 5090 D~395 tok/s
- RTX 6000D~347 tok/s
- Radeon RX 7900 XTX~212 tok/s
- RTX 3090~211 tok/s
- MacBook Pro 16" M4 Max~121 tok/s
- Arc Pro B60~101 tok/s
- DGX Spark (GB10)~60 tok/s
Also runs on the RTX 4090
- Qwen3-30B-A3B-Instruct-2507~236 tok/s
- Qwen3.6 27B~39 tok/s
- Qwen3.8 27B~39 tok/s
- Gemma 4 26B A4B~184 tok/s
- gpt-oss-20b~208 tok/s
- Qwen2.5-Coder-14B-Instruct~70 tok/s
- Qwen3 14B~78 tok/s
- Phi-4~74 tok/s
Frequently asked
Quick answers
Can the NVIDIA RTX 4090 run GLM-4.7-Flash?
Yes. GLM-4.7-Flash fits the RTX 4090's 24 GB at Q4_K_M (about 19.8 GB needed), running at an estimated 241 tok/s. There's room for up to Q5_K_M if you want higher quality.
How much VRAM does GLM-4.7-Flash need?
About 18 GB of weights at Q4_K_M, 33 GB at Q8_0 and 62 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.
How fast is GLM-4.7-Flash on the RTX 4090?
Roughly 241 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 4090's 1008 GB/s memory bandwidth; as a mixture-of-experts model only about 3B parameters are read per token, which is why it is faster than its size suggests.