text model · GLM · Windows
Can I run GLM-5.2 on Nvidia GeForce RTX 4080 (16GB)?
No. GLM-5.2 needs ~473.1 GB even at Q4_K_M, but Nvidia GeForce RTX 4080 (16GB) only has ~15 GB usable.
Needs ~473.1 GB even at Q4_K_M, but only ~15 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 458.1 GB: GLM-5.2 needs roughly 473.1 GB at Q4_K_M and Nvidia GeForce RTX 4080 (16GB) leaves only about 15 GB usable for a model. No single tracked device has enough memory; GLM-5.2 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~473.1 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
Which quant fits
How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).
- Parameters
- 744B (MoE, 40B active)
- Q4_K_M size
- 465.83 GB
- Q8_0 size
- 801.36 GB
- Context
- 1000k
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Power draw
- ~320 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
What you can run instead
FAQ
Can Nvidia GeForce RTX 4080 (16GB) run GLM-5.2?
No. GLM-5.2 needs ~473.1 GB even at Q4_K_M, but Nvidia GeForce RTX 4080 (16GB) only has ~15 GB usable.
How much memory does GLM-5.2 need?
Nvidia GeForce RTX 4080 (16GB) does not have enough memory. At Q4_K_M the weights are ~465.83 GB; with KV cache and runtime overhead, budget ~473.1 GB at a 4k context. It is a Mixture-of-Experts model (744B total / 40B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run GLM-5.2 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-5.2/nvidia-rtx-4080-16gb) 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.