Can RTX 4060 Ti 16GB run Phi-4 Mini 3.8B?

Yes — runs locally

~78 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 4060 Ti 16GB (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 78 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 4060 Ti 16GB

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 4060 Ti 16GB with Q8_0 quantization, achieving ~177 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows 10/11 or Linux, and the latest NVIDIA drivers (version 525.60 or later) installed along with CUDA 11.8 or later.

Expected performance

With the Q8_0 quantization, you can expect ~177 tok/sec performance while using approximately 4.3GB of VRAM. This leaves you with 11.7GB of VRAM for context, allowing for a practical context window of up to 131072 tokens.

1. Install runtimeOllama

pip install ollama
ollama config set device cuda

2. Download the model

Download the Q8_0 quantized version of Phi-4 Mini 3.8B (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 --model-file microsoft_Phi-4-mini-instruct-Q8_0.gguf

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to balance between speed and memory usage. Enable flash attention (--flash-attn) to further optimize performance. With 16GB VRAM, you can comfortably fit the model and maintain a large context window.

Troubleshooting

Out of memory errors during inference

Reduce the --n-gpu-layers value to 24 or 16 to lower VRAM usage.

Slow token generation

Ensure that the --flash-attn flag is enabled to utilize optimized attention mechanisms.

Model not loading

Check that the model file path is correct and that the file exists in the specified location.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment. LM Studio is ideal for GUI-based interaction, llama.cpp offers a lightweight and highly customizable solution, and Jan is suitable for cloud deployments. However, Ollama provides a simple and efficient way to run Phi-4 Mini 3.8B on your NVIDIA GeForce RTX 4060 Ti 16GB.

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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