Can RTX 4060 Ti 16GB run Phi-4?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 4060 Ti 16GB (16 GB VRAM) handles Phi-4 comfortably using the Q5_K_M quantization, which fits in 10.4 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU in interactive use. Microsoft's 14B parameter model. Punches well above its weight on reasoning.
Setup tutorial: Phi-4 on RTX 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Phi-4 (14B parameters) on your NVIDIA GeForce RTX 4060 Ti 16GB with Ollama using the Q5_K_M quantization. Expect 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, and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8 or higher.
Expected performance
With the Q5_K_M quantization, expect ~55 tok/sec performance and approximately 10.4GB of VRAM usage, leaving 5.6GB of headroom for context. This allows for a practical context window of around 16384 tokens, making it suitable for long-form reasoning tasks.
1. Install runtimeOllama
curl -Lo ollama.tar.gz https://ollama.com/install/linux-amd64.tar.gz
tar -xvf ollama.tar.gz
sudo mv ollama /usr/local/bin/2. 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 --model-path phi-4-Q5_K_M.gguf --n-gpu-layers 14 --flash-attn --tensor-parallelism 24. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 14 to utilize most of the 16GB VRAM. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload efficiently across the GPU cores.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 12 or 10 and decrease --tensor-parallelism to 1.
Slow token generation speed
Ensure --flash-attn is enabled and check that CUDA is properly installed and up-to-date.
Model fails to load
Verify the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
Consider using LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for lightweight deployment. Use these alternatives if you need specific features not supported by Ollama, such as custom training or deployment on edge devices.
Other models that run great on RTX 4060 Ti 16GB
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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