text model · GLM · Windows
Can I run GLM-4.7-Flash on Nvidia GeForce RTX 5090 (32GB)?
Yes. GLM-4.7-Flash runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~19.2 GB of ~31 GB usable).
Runs at Q4_K_M using ~19.2 GB of ~31 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 5090 (32GB) leaves ~11.8 GB of headroom.
- Q4_K_M needed
- ~19.2 GB
- Usable on device
- ~31 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run glm-4.7-flash:latest llama-cli -hf unsloth/GLM-4.7-Flash-GGUF:Q4_K_M lms get unsloth/GLM-4.7-Flash-GGUF - Parameters
- 30B (MoE, 3B active)
- Q4_K_M size
- 17.05 GB
- Q8_0 size
- 29.66 GB
- Context
- 200k
- Ollama tag
- glm-4.7-flash:latest
- Memory
- 32 GB vram
- Usable for weights
- ~31 GB
- Power draw
- ~575 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run GLM-4.7-Flash on other hardware
FAQ
Can Nvidia GeForce RTX 5090 (32GB) run GLM-4.7-Flash?
Yes. GLM-4.7-Flash runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~19.2 GB of ~31 GB usable).
How much memory does GLM-4.7-Flash need?
Nvidia GeForce RTX 5090 (32GB) has room to spare. At Q4_K_M the weights are ~17.05 GB; with KV cache and runtime overhead, budget ~19.2 GB at a 4k context. It is a Mixture-of-Experts model (30B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run GLM-4.7-Flash on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/glm-4.7-flash/nvidia-rtx-5090-32gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.