text model · GLM · Windows
Can I run GLM-4-9B-0414 on Nvidia GeForce RTX 5090 (32GB)?
Yes. GLM-4-9B-0414 runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~7.2 GB of ~31 GB usable).
Runs at Q4_K_M using ~7.2 GB of ~31 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 5090 (32GB) leaves ~23.8 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~7.2 GB
- Usable on device
- ~31 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~575 W
- Electricity / 1M tokens
- ~$0.12
- Pays for itself after
- ~5,261M tok
At ~$0.15/kWh and the estimated ~203 tok/s, a million generated tokens costs about $0.12 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,999 Nvidia GeForce RTX 5090 (32GB) pays for itself after roughly 5,261 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 2 load the same Q4_K_M weights.
llama-cli -hf bartowski/THUDM_GLM-4-9B-0414-GGUF:Q4_K_M lms get bartowski/THUDM_GLM-4-9B-0414-GGUF How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).
- Parameters
- 9B
- Q4_K_M size
- 5.74 GB
- Q8_0 size
- 9.31 GB
- Context
- 32k
- 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-4-9B-0414 on other hardware
FAQ
Can Nvidia GeForce RTX 5090 (32GB) run GLM-4-9B-0414?
Yes. GLM-4-9B-0414 runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~7.2 GB of ~31 GB usable).
How much memory does GLM-4-9B-0414 need?
Nvidia GeForce RTX 5090 (32GB) has room to spare. At Q4_K_M the weights are ~5.74 GB; with KV cache and runtime overhead, budget ~7.2 GB at a 4k context.
What is the best tool to run GLM-4-9B-0414 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-4-9b-0414/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.