Can RTX 3080 Ti run Phi-4 Mini 3.8B?

Yes — runs locally

~74 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

3.8B

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The RTX 3080 Ti (12 GB VRAM) handles Phi-4 Mini 3.8B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 74 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 3080 Ti

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

TL;DR

Phi-4 Mini 3.8B runs at Grade S on an NVIDIA GeForce RTX 3080 Ti with Q8_0 quantization, achieving ~133 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible OS (Windows or Linux), the latest NVIDIA driver (version 510.47.03 or later), and CUDA 11.2 or later installed.

Expected performance

With the Q8_0 quantization, you can expect Phi-4 Mini 3.8B to run at approximately 133 tokens per second, using around 4.3GB of VRAM. This leaves about 7.7GB of VRAM available for context, allowing for a practical context window of up to 65,536 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, which is a 3.8GB file.

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 --interactive
ollama chat --model=microsoft_Phi-4-mini-instruct-Q8_0.gguf

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and consider using tensor parallelism with --tensor-parallel-size 2. This configuration will help maximize the token generation speed while keeping the VRAM usage within the 12GB limit.

Troubleshooting

Out of memory errors during inference

Reduce the number of GPU layers with --n-gpu-layers or decrease the batch size.

Slow inference times

Enable flash attention with --flash-attn and increase the tensor parallelism with --tensor-parallel-size 2.

Model fails to load

Ensure the model file is correctly downloaded and not corrupted. Verify the integrity of the file with md5sum or sha256sum.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a graphical interface, while llama.cpp offers more control over low-level optimizations. Jan is a lightweight runtime that can be used for quick prototyping. For the NVIDIA GeForce RTX 3080 Ti, Ollama is recommended for its ease of use and performance optimization.

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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