- Check whether Ollama is using the GPU or CPU.
- Review drivers, compatibility, and VRAM on Windows.
- Decide when to use native Windows, WSL2, LM Studio, or a smaller model.
Quick diagnosis
Open PowerShell and try this while generating a long response in Ollama:
ollama ps ollama run qwen3:4b "Write a long explanation about local AI" # In another terminal, if you have NVIDIA: nvidia-smi -l 1
Read the logs before changing anything
The logs usually say whether Ollama found a GPU, fell back to CPU, or if a driver failed during detection.
# PowerShell Get-ChildItem "$env:LOCALAPPDATA\Ollama" -Recurse -Filter "*.log" # Open the most recent log: notepad "$env:LOCALAPPDATA\Ollama\server.log"
Look for words like cuda , rocm , vulkan , gpu , fallback , memory or no compatible GPUs . If nothing about the GPU appears, Windows isn't even presenting it properly to Ollama.
NVIDIA checklist
- Update the NVIDIA driver. Ollama documents support for NVIDIA GPUs with compatible compute capability and recent drivers.
- Restart Windows after installing the driver.
- Verify that nvidia-smi works in PowerShell.
- If you have a hybrid laptop, force the dedicated GPU for Ollama from Windows Graphics settings or the NVIDIA Control Panel.
- Try a small model to rule out insufficient VRAM.
nvidia-smi ollama pull qwen3:4b ollama run qwen3:4b "Reply with 20 sentences to test performance"
NVIDIA + Intel hybrid laptops
This is the trickiest case: Windows may launch Ollama with the Intel iGPU even if you have a dedicated NVIDIA GPU.
- Open Settings → System → Display → Graphics.
- Add the Ollama app if it doesn't appear.
- Set High performance to use the dedicated GPU.
- In the NVIDIA Control Panel, use High-performance NVIDIA processor for Ollama if your system allows it.
- Quit Ollama from the system tray and reopen it.
# Check before and after: nvidia-smi -l 1 ollama run qwen3:4b "Run a long performance test"
AMD Radeon checklist
Ollama for Windows includes AMD Radeon support, but practical compatibility depends heavily on the GPU, driver, and available backend.
- Update AMD Adrenalin and restart.
- Try native Ollama on Windows first, not Docker.
- If your iGPU or APU doesn't accelerate well, try LM Studio with Vulkan on that machine.
- On Linux, check the ROCm version and drivers; if they're outdated, Ollama may fall back to CPU.
Vulkan as plan B for AMD, iGPU, and unusual setups
If your GPU doesn't work through CUDA or ROCm, Vulkan can be a useful path on some machines. Don't treat it as a universal guarantee: test it and measure.
# PowerShell: persistent variables for your user setx OLLAMA_VULKAN 1 setx OLLAMA_IGPU_ENABLE 1 # Quit Ollama completely, open a new terminal, and test: ollama run qwen3:4b "Vulkan test in Ollama"
Windows Defender can slow down models
Models are huge files. On some machines, Defender may scan every download or read and make it feel like Ollama is broken.
# Default models path: %USERPROFILE%\.ollama # PowerShell: explorer "$env:USERPROFILE\.ollama"
Add that folder to Windows Security exclusions only if you understand the risk and download models from trusted sources. Don't exclude generic folders like Downloads or your entire user profile.
WSL2 or native Windows
For most people, native Windows is simpler. WSL2 makes sense if you already work in Linux, Docker, or backend development.
wsl --status wsl --shutdown # Inside Ubuntu/WSL, if you have NVIDIA: nvidia-smi
Docker on Windows
If you run Ollama in Docker, you need to pass the GPU to the container. Before blaming Ollama, verify that Docker can see the GPU.
docker run --rm --gpus all nvidia/cuda:12.6.0-base-ubuntu22.04 nvidia-smi
If it still uses the CPU
- Restart Ollama from the tray icon or restart Windows.
- Update Ollama to the latest version.
- Try a smaller model or a lighter quantization.
- Check free VRAM before launching the model.
- On a hybrid laptop, plug in the charger and enable high-performance mode.
- Compare with LM Studio if you have an iGPU or AMD and need easy Vulkan offload.