Can RTX 5060 Ti run Phi-4 Mini 3.8B?

Yes — runs locally

~114 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

3.8B

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The RTX 5060 Ti (16 GB VRAM) handles Phi-4 Mini 3.8B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 114 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Latest Phi mini with strong reasoning. Drop-in upgrade from Phi-3.5 Mini.

Setup tutorial: Phi-4 Mini 3.8B on RTX 5060 Ti

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Phi-4 Mini 3.8B runs at Grade S on the NVIDIA GeForce RTX 5060 Ti with Q8_0 quantization, achieving ~177 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB 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 Q8_0 quantization, you can expect the model to run at ~177 tok/sec, using approximately 4.3GB of VRAM. The remaining 11.7GB of VRAM provides ample headroom to handle large context windows, making it suitable for tasks requiring extensive context.

1. Install runtimeOllama

pip install ollama
ollama config set device cuda

2. Download the model

Download the Q8_0 quantized model (3.8GB file) from Hugging Face.

ollama pull bartowski/microsoft_Phi-4-mini-instruct-GGUF:microsoft_Phi-4-mini-instruct-Q8_0.gguf

3. Run it

ollama run bartowski/microsoft_Phi-4-mini-instruct-GGUF --model microsoft_Phi-4-mini-instruct-Q8_0.gguf
ollama chat --model bartowski/microsoft_Phi-4-mini-instruct-GGUF

4. Optimize for RTX 5060 Ti

For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Enable flash attention with --flash-attn to reduce memory usage and improve speed. Given the 4.3GB VRAM requirement, you will have approximately 11.7GB of VRAM left for context, allowing for a large context window of up to 131072 tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers with --n-gpu-layers <num_layers> or enable flash attention with --flash-attn.

Slow inference speed

Ensure CUDA is properly installed and configured. Use --flash-attn to optimize memory usage and speed.

Model not found error

Verify the model path and ensure the model is correctly downloaded and accessible.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more graphical interface, llama.cpp for lightweight deployment, or Jan for advanced customization. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 5060 Ti.

Full Phi-4 Mini 3.8B details →

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FAQ (20)

What GPU do I need to run Phi-4 Mini 3.8B?

To run Phi-4 Mini 3.8B, you need a GPU with at least 2.8 GB of VRAM, but 4.3 GB is recommended for optimal performance, especially with higher quantization levels.

Is Phi-4 Mini 3.8B good for coding?

Yes, Phi-4 Mini 3.8B is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 131,072 tokens, which allows it to handle complex code snippets and documentation.

Phi-4 Mini 3.8B vs Llama 3.1 8B?

Phi-4 Mini 3.8B has fewer parameters (3.8B vs 8B) but is more efficient in terms of VRAM usage and performance, making it a better choice for systems with limited resources. It also offers a larger context length of 131,072 tokens compared to Llama 3.1 8B.

Can I run Phi-4 Mini 3.8B on a Mac?

Yes, you can run Phi-4 Mini 3.8B on a Mac, provided your Mac has a compatible GPU with at least 2.8 GB of VRAM. Ensure you have the necessary drivers and software installed for optimal performance.

How much VRAM does Phi-4 Mini 3.8B need?

Phi-4 Mini 3.8B requires between 2.8 GB and 4.3 GB of VRAM, depending on the quantization level used. Higher quantization levels generally require more VRAM but offer better performance.

Is Phi-4 Mini 3.8B censored?

Phi-4 Mini 3.8B is not inherently censored, but it may include content filters or safeguards to prevent the generation of harmful or inappropriate content, as is common in many AI models.

Is Phi-4 Mini 3.8B commercial-use allowed?

Yes, Phi-4 Mini 3.8B is licensed under the MIT License, which allows for both personal and commercial use without additional restrictions.

Phi-4 Mini 3.8B context length?

Phi-4 Mini 3.8B has a context length of 131,072 tokens, which is significantly larger than many other models, allowing it to process and generate longer sequences of text.

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