text model · Phi-4 · Windows
Can I run Phi-4-reasoning on Nvidia GeForce RTX 4080 (16GB)?
Yes. Phi-4-reasoning runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M (~10.1 GB of ~15 GB usable).
Runs at Q4_K_M using ~10.1 GB of ~15 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 4080 (16GB) leaves ~4.9 GB of headroom.
- Q4_K_M needed
- ~10.1 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~320 W
- Electricity / 1M tokens
- ~$0.24
- Pays for itself after
- ~4,612M tok
At ~$0.15/kWh and the estimated ~55 tok/s, a million generated tokens costs about $0.24 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,199 Nvidia GeForce RTX 4080 (16GB) pays for itself after roughly 4,612 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 3 load the same Q4_K_M weights.
ollama run phi4-reasoning:14b llama-cli -hf bartowski/microsoft_Phi-4-reasoning-GGUF:Q4_K_M lms get bartowski/microsoft_Phi-4-reasoning-GGUF - Parameters
- 14B
- Q4_K_M size
- 8.43 GB
- Q8_0 size
- 14.51 GB
- Context
- 32k
- Ollama tag
- phi4-reasoning:14b
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Power draw
- ~320 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Phi-4-reasoning on other hardware
FAQ
Can Nvidia GeForce RTX 4080 (16GB) run Phi-4-reasoning?
Yes. Phi-4-reasoning runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M (~10.1 GB of ~15 GB usable).
How much memory does Phi-4-reasoning need?
Nvidia GeForce RTX 4080 (16GB) has room to spare. At Q4_K_M the weights are ~8.43 GB; with KV cache and runtime overhead, budget ~10.1 GB at a 4k context.
What is the best tool to run Phi-4-reasoning 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/phi-4-reasoning/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.