text model · GLM · Windows
Can I run GLM-4 9B on Nvidia GeForce RTX 3060 Ti (8GB)?
No. GLM-4 9B needs ~7.3 GB even at Q4_K_M, but Nvidia GeForce RTX 3060 Ti (8GB) only has ~7 GB usable.
Needs ~7.3 GB even at Q4_K_M, but only ~7 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 0.3 GB: GLM-4 9B needs roughly 7.3 GB at Q4_K_M and Nvidia GeForce RTX 3060 Ti (8GB) leaves only about 7 GB usable for a model. The lightest tracked hardware that runs GLM-4 9B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See GLM-4 9B on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~7.3 GB
- Usable on device
- ~7 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 9B
- Q4_K_M size
- 5.82 GB
- Q8_0 size
- 9.31 GB
- Context
- 128k
- Ollama tag
- glm4:9b
- Memory
- 8 GB vram
- Usable for weights
- ~7 GB
- Power draw
- ~200 W
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
What you can run instead
Run GLM-4 9B on other hardware
FAQ
Can Nvidia GeForce RTX 3060 Ti (8GB) run GLM-4 9B?
No. GLM-4 9B needs ~7.3 GB even at Q4_K_M, but Nvidia GeForce RTX 3060 Ti (8GB) only has ~7 GB usable.
How much memory does GLM-4 9B need?
Nvidia GeForce RTX 3060 Ti (8GB) does not have enough memory. At Q4_K_M the weights are ~5.82 GB; with KV cache and runtime overhead, budget ~7.3 GB at a 4k context.
What is the best tool to run GLM-4 9B 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/glm-4-9b/nvidia-rtx-3060-ti-8gb) 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.