Can RTX 4080 SUPER 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 4080 SUPER (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 4080 SUPER

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

TL;DR

Run Phi-4 Mini 3.8B on an NVIDIA GeForce RTX 4080 SUPER with Q8_0 quantization for Grade S performance at ~177 tok/sec.

Prerequisites

Before starting, ensure you have at least 4GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 525.60 or later, and CUDA 11.8 or later installed.

Expected performance

You can expect the model to run at approximately 177 tokens per second, using 4.3GB of VRAM. Given the remaining 11.7GB of VRAM, you can achieve a practical context window of up to 131072 tokens, which is the maximum supported by the model.

1. Install runtimeOllama

pip install ollama
ollama init

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 --model=microsoft_Phi-4-mini-instruct-Q8_0.gguf --n-gpu-layers=32 --flash-attn
ollama chat --model=microsoft_Phi-4-mini-instruct-Q8_0.gguf

4. 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 attention (--flash-attn) to speed up inference. With 4.3GB VRAM used by the model, you have 11.7GB of VRAM headroom for larger context windows, allowing you to process longer sequences without running out of memory.

Troubleshooting

Out of memory errors during inference

Reduce the number of --n-gpu-layers or decrease the context length to fit within the available VRAM.

Slow inference speeds

Ensure that flash attention is enabled (--flash-attn) and that the NVIDIA driver and CUDA versions are up to date.

Model fails to load

Verify that the model file was downloaded correctly and that there are no issues with the Ollama installation. Try reinstalling Ollama or downloading the model again.

Alternative runtimes

For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for integration with other machine learning frameworks. Each runtime has its strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 4080 SUPER.

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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