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

Yes — runs locally

~102 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 4070 Ti 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 102 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 Ti 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 Ti SUPER with Q8_0 quantization for Grade S performance at ~177 tok/sec.

Prerequisites

Before starting, ensure you have at least 8GB 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 ~177 tok/sec performance with 4.3GB VRAM in use, leaving 11.7GB of VRAM for context. This should allow for a practical context window of up to 131072 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 model (3.8GB file) from the Hugging Face repository.

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 --n-gpu-layers=32 --flash-attn --context-length=131072

4. Optimize for RTX 4070 Ti SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn for faster attention computations. 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 error during inference

Reduce --n-gpu-layers to 24 or 16 and try again.

Slow token generation speed

Ensure --flash-attn is enabled and check your CUDA installation for any issues.

Model fails to load

Verify the integrity of the downloaded model file and try downloading it again.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for specific needs. LM Studio offers a GUI for easier management, llama.cpp provides more fine-grained control over optimizations, and Jan is suitable for distributed setups. However, Ollama is recommended for its simplicity and performance on the NVIDIA GeForce RTX 4070 Ti 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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