Can RTX 5080 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 5080 (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 5080

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 5080 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 10/11 or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

You can expect ~177 tok/sec with 4.3GB VRAM in use, leaving 11.7GB for context. This allows for a practical context window of up to 131,072 tokens, depending on the complexity of the input.

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 bartowski/microsoft_Phi-4-mini-instruct-GGUF --model microsoft_Phi-4-mini-instruct-Q8_0.gguf --context-length 131072
ollama chat

4. Optimize for RTX 5080

For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 4.3GB VRAM used by the model, you have 11.7GB of VRAM left for context, allowing for a large practical context window.

Troubleshooting

Out of memory errors during inference

Reduce the number of GPU layers (--n-gpu-layers) or decrease the context length (--context-length).

Slow inference speed

Ensure flash attention (--flash-attn) is enabled and check if your CUDA installation is up to date.

Model not loading

Verify the model file integrity and try re-downloading it using the 'ollama pull' command.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. Use LM Studio for a more user-friendly interface, llama.cpp for low-level customization, and Jan for distributed inference across multiple GPUs. Ollama is recommended for its ease of use and performance on single-GPU setups like the RTX 5080.

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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