text model · Qwen2.5-Coder · Windows
Can I run Qwen2.5 Coder 32B on Nvidia GeForce RTX 4090 (24GB)?
Yes. Qwen2.5 Coder 32B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~20.7 GB of ~23 GB usable).
Fits at Q4_K_M (~20.7 GB of ~23 GB usable) but with little headroom, close other apps.
- Q4_K_M needed
- ~20.7 GB
- Usable on device
- ~23 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run qwen2.5-coder:32b llama-cli -hf bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M lms get bartowski/Qwen2.5-Coder-32B-Instruct-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 32B
- Q4_K_M size
- 18.49 GB
- Q8_0 size
- 32.43 GB
- Context
- 32k
- Ollama tag
- qwen2.5-coder:32b
- Memory
- 24 GB vram
- Usable for weights
- ~23 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Qwen2.5 Coder 32B on other hardware
FAQ
Can Nvidia GeForce RTX 4090 (24GB) run Qwen2.5 Coder 32B?
Yes. Qwen2.5 Coder 32B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~20.7 GB of ~23 GB usable).
How much memory does Qwen2.5 Coder 32B need?
It is a tight fit on Nvidia GeForce RTX 4090 (24GB). At Q4_K_M the weights are ~18.49 GB; with KV cache and runtime overhead, budget ~20.7 GB at a 4k context.
What is the best tool to run Qwen2.5 Coder 32B 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.
Sources
Memory figures are estimates. See methodology.