text model · phi · Windows
Can I run Phi-4 14B on Nvidia GeForce RTX 5090 (32GB)?
Yes. Phi-4 14B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~10.8 GB of ~31 GB usable).
Runs at Q4_K_M using ~10.8 GB of ~31 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~10.8 GB
- Usable on device
- ~31 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run phi4:14b llama-cli -hf bartowski/phi-4-GGUF:Q4_K_M lms get bartowski/phi-4-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 14B
- Q4_K_M size
- 9.05 GB
- Q8_0 size
- 15.58 GB
- Context
- 16k
- Ollama tag
- phi4:14b
- Memory
- 32 GB vram
- Usable for weights
- ~31 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Phi-4 14B on other hardware
FAQ
Can Nvidia GeForce RTX 5090 (32GB) run Phi-4 14B?
Yes. Phi-4 14B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~10.8 GB of ~31 GB usable).
How much memory does Phi-4 14B need?
Nvidia GeForce RTX 5090 (32GB) has room to spare. At Q4_K_M the weights are ~9.05 GB; with KV cache and runtime overhead, budget ~10.8 GB at a 4k context.
What is the best tool to run Phi-4 14B 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.