Can RTX 5060 Ti run Phi-4?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5060 Ti (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 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Phi-4 with Q5_K_M quantization on a NVIDIA GeForce RTX 5060 Ti for Grade S performance at ~55 tok/sec.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 512.15 or later, and CUDA 11.7 or later installed.
Expected performance
With the recommended settings, you can expect the Phi-4 model to run at approximately 55 tokens per second, using around 10.4GB of VRAM. The remaining 5.6GB of VRAM can be used to support a practical context window of up to 8192 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 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.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 2
ollama chat phi-4-Q5_K_M.gguf4. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, use the --n-gpu-layers 32 flag to load most layers onto the GPU. Enable --flash-attn for faster attention computation and set --tensor-parallelism 2 to distribute the workload across GPU cores. This configuration will utilize approximately 10.4GB of VRAM, leaving 5.6GB for context and other operations.
Troubleshooting
Out of memory error during model loading
Reduce the number of GPU layers using the --n-gpu-layers flag, e.g., --n-gpu-layers 16.
Slow token generation speed
Ensure that --flash-attn is enabled and try increasing the --tensor-parallelism value to 4.
Model fails to load with CUDA errors
Update your NVIDIA driver to the latest version and ensure CUDA 11.7 is installed.
Alternative runtimes
For users who prefer a different runtime, consider LM Studio for a more user-friendly GUI, llama.cpp for fine-grained control over model execution, or Jan for a lightweight, efficient runtime. Each alternative has its strengths, but Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 5060 Ti.
Other models that run great on RTX 5060 Ti
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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