Can RTX 4080 SUPER run Phi-4?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4080 SUPER (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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Phi-4 on an NVIDIA GeForce RTX 4080 SUPER with Q5_K_M quantization for Grade S performance at ~55 tokens/second.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.8 or later installed.
Expected performance
With the Q5_K_M quantization, you can expect the model to run at approximately 55 tokens/second, using around 10.4GB of VRAM. This leaves 5.6GB of VRAM for context, allowing you to achieve a practical context window of up to 16384 tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Phi-4 model with Q5_K_M quantization (9.9GB file size) from Hugging Face.
ollama pull bartowski/phi-4-GGUF:phi-4-Q5_K_M.gguf3. Run it
ollama run phi-4 --n-gpu-layers 32 --flash-attn --context-length 163844. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn for faster attention computation. The model will use approximately 10.4GB of VRAM, leaving 5.6GB of headroom for larger context windows or additional layers.
Troubleshooting
Out of memory error during inference
Reduce the number of --n-gpu-layers or decrease the --context-length to fit within the available VRAM.
Slow token generation speed
Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.
Model fails to load
Verify that the model file has been downloaded correctly and that there are no disk space issues.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a more user-friendly interface but may require more system resources. llama.cpp provides more control over low-level optimizations and is suitable for advanced users. Jan is lightweight and efficient but may lack some features compared to Ollama. Choose based on your specific needs and system configuration.
Other models that run great on RTX 4080 SUPER
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.
Want personalized recommendations for your exact setup? Detect my hardware →