text model · Qwen2.5 · Windows
Can I run Qwen2.5 72B on Nvidia GeForce RTX 5090 (32GB)?
No. Qwen2.5 72B needs ~50.2 GB even at Q4_K_M, but Nvidia GeForce RTX 5090 (32GB) only has ~31 GB usable.
Needs ~50.2 GB even at Q4_K_M, but only ~31 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 19.2 GB: Qwen2.5 72B needs roughly 50.2 GB at Q4_K_M and Nvidia GeForce RTX 5090 (32GB) leaves only about 31 GB usable for a model. The lightest tracked hardware that runs Qwen2.5 72B is the Apple M4 Max (128GB) at 128 GB. See Qwen2.5 72B on Apple M4 Max (128GB).
Too big for Nvidia GeForce RTX 5090 (32GB)'s VRAM, but you could offload some layers to system RAM and still run it at roughly ~2 tok/s, far slower than a model that fits. How this is estimated.
- Q4_K_M needed
- ~50.2 GB
- Usable on device
- ~31 GB
- Device memory
- 32 GB
Which quant fits
- Parameters
- 72B
- Q4_K_M size
- 47.42 GB
- Q8_0 size
- 77.26 GB
- Context
- 128k
- Ollama tag
- qwen2.5:72b
- Memory
- 32 GB vram
- Usable for weights
- ~31 GB
- Power draw
- ~575 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
What you can run instead
Run Qwen2.5 72B on other hardware
FAQ
Can Nvidia GeForce RTX 5090 (32GB) run Qwen2.5 72B?
No. Qwen2.5 72B needs ~50.2 GB even at Q4_K_M, but Nvidia GeForce RTX 5090 (32GB) only has ~31 GB usable.
How much memory does Qwen2.5 72B need?
Nvidia GeForce RTX 5090 (32GB) does not have enough memory. At Q4_K_M the weights are ~47.42 GB; with KV cache and runtime overhead, budget ~50.2 GB at a 4k context.
What is the best tool to run Qwen2.5 72B 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
[](https://localmodel.run/can-i-run/qwen2.5-72b/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.