Can RTX 4070 SUPER run Phi-4 Mini 3.8B?

Yes — runs locally

~94 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 4070 SUPER (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 94 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 4070 SUPER

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

TL;DR

Run Phi-4 Mini 3.8B on an NVIDIA GeForce RTX 4070 SUPER with Grade S performance at ~133 tok/sec using the Q8_0 quantization.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 526.95 or later) installed along with CUDA 11.8 or later.

Expected performance

With the Q8_0 quantization, you can expect the model to run at approximately 133 tokens per second, using around 4.3GB of VRAM. This leaves about 7.7GB of VRAM for context, enabling a practical context window of up to 131,072 tokens, depending on the complexity of the input.

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, 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 bartowski/microsoft_Phi-4-mini-instruct-GGUF:microsoft_Phi-4-mini-instruct-Q8_0.gguf --interactive
ollama stream bartowski/microsoft_Phi-4-mini-instruct-GGUF:microsoft_Phi-4-mini-instruct-Q8_0.gguf

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, 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 4.3GB VRAM used by the model, you will have approximately 7.7GB of VRAM left for context, allowing for a large practical context window.

Troubleshooting

Out of memory error during inference

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

Slow token generation rate

Ensure that flash attention is enabled with --flash-attn and that the CUDA backend is properly configured.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model file.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a graphical interface and need more control over model parameters. llama.cpp is ideal for low-memory systems and those who want to run models on CPUs. Jan is a lightweight runtime that is easy to set up but may lack some advanced features. For the NVIDIA GeForce RTX 4070 SUPER, Ollama is recommended due to its ease of use and optimized performance.

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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