Can RTX 5080 run Phi-4?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5080 (16 GB VRAM) handles Phi-4 comfortably using the Q5_K_M quantization, which fits in 10.4 GB. Expected throughput is around 48 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Microsoft's 14B parameter model. Punches well above its weight on reasoning.
Setup tutorial: Phi-4 on RTX 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-4 runs at Grade S on the NVIDIA GeForce RTX 5080 with the Q5_K_M quantization, achieving ~55 tok/sec and using 10.4GB VRAM.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the Q5_K_M quantization, you can expect Phi-4 to run at approximately 55 tokens per second, using 10.4GB of VRAM. The remaining 5.6GB of VRAM provides ample headroom to handle contexts up to 16384 tokens, ensuring smooth and snappy performance.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Phi-4 Q5_K_M quantized model (9.9GB file) from the Hugging Face repository.
ollama pull bartowski/phi-4-GGUF:phi-4-Q5_K_M.gguf3. Run it
ollama run phi-4-Q5_K_M --interactive
ollama chat phi-4-Q5_K_M4. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 70 to maximize GPU utilization. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 10.4GB VRAM in use, you have 5.6GB of headroom for larger context windows, allowing you to process longer sequences efficiently.
Troubleshooting
Out of memory error during inference
Reduce the number of layers offloaded to the GPU using --n-gpu-layers <N>, where <N> is a lower value. For example, try --n-gpu-layers 50.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn. If still slow, consider reducing the batch size or using a different quantization level.
Model fails to load
Verify that the model file has been downloaded correctly and is not corrupted. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
While Ollama is recommended for its ease of use and performance, you can also run Phi-4 using alternative runtimes like LM Studio or llama.cpp. LM Studio offers a more graphical interface and is suitable for users who prefer a GUI. llama.cpp is a lightweight option for those who need minimal dependencies. Jan is another runtime that supports advanced features but may require more configuration. Choose based on your specific needs and preferences.
Other models that run great on RTX 5080
FAQ (20)
What GPU do I need to run Phi-4?
To run Phi-4, you need a GPU with at least 8.9 GB of VRAM, but 15.0 GB is recommended for optimal performance.
Is Phi-4 good for coding?
Yes, Phi-4 is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 16,384 tokens.
Phi-4 vs Llama 3.1 8B?
Phi-4 has 14 billion parameters compared to Llama 3.1's 8 billion, making it more powerful for complex tasks but requiring more VRAM.
Can I run Phi-4 on a Mac?
Yes, you can run Phi-4 on a Mac with a compatible GPU, such as an AMD or NVIDIA card with sufficient VRAM.
How much VRAM does Phi-4 need?
Phi-4 requires between 8.9 GB and 15.0 GB of VRAM, depending on the quantization level used.
Is Phi-4 censored?
Phi-4 is not inherently censored, but its outputs can be filtered based on the implementation and configuration settings.
Is Phi-4 commercial-use allowed?
Yes, Phi-4 is licensed under the MIT License, which allows for commercial use without restriction.
Phi-4 context length?
Phi-4 has a context length of 16,384 tokens, allowing it to handle longer sequences of text effectively.
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