Can RTX 3090 Ti run Phi-4 Mini 3.8B?

Yes — runs locally

~96 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

3.8B

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The RTX 3090 Ti (24 GB VRAM) handles Phi-4 Mini 3.8B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 96 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 3090 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 3090 Ti with Q8_0 quantization, achieving ~265 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows 10/11 or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.

Expected performance

With the Q8_0 quantization, you can expect Phi-4 Mini 3.8B to run at approximately 265 tokens per second, using around 4.3GB of VRAM. The remaining 19.7GB of VRAM provides ample headroom for handling large context windows, making it suitable for tasks requiring extensive context.

1. Install runtimeOllama

pip install ollama
ollama config set runtime 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 --model bartowski/microsoft_Phi-4-mini-instruct-GGUF --quantization Q8_0
ollama chat --model bartowski/microsoft_Phi-4-mini-instruct-GGUF

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 4.3GB VRAM used by the model, you have 19.7GB of VRAM left for context, allowing for a practical context window of up to 131072 tokens.

Troubleshooting

Out of memory error during inference

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

Slow token generation

Ensure flash attention is enabled with --flash-attn and check that the CUDA runtime is correctly configured.

Model not found

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

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different performance characteristics. LM Studio offers a user-friendly interface, while llama.cpp provides more control over quantization and optimization. Jan is suitable for distributed training and inference scenarios. For the NVIDIA GeForce RTX 3090 Ti, Ollama is recommended for its ease of use and excellent performance.

Full Phi-4 Mini 3.8B details →

Other models that run great on RTX 3090 Ti

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.

Want personalized recommendations for your exact setup? Detect my hardware →