Can RTX 3070 Ti run Phi-4 Mini 3.8B?

Yes — runs locally

~60 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

8 GB

Model size

3.8B

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The RTX 3070 Ti (8 GB VRAM) handles Phi-4 Mini 3.8B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 60 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 3070 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 3070 Ti with Q8_0 quantization, achieving ~88 tok/sec. It uses 4.3GB VRAM, leaving 3.7GB for context.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 510.73 or later) installed. You also need CUDA 11.0 or later.

Expected performance

With the Q8_0 quantization, you can expect Phi-4 Mini 3.8B to run at approximately 88 tokens per second, using 4.3GB of the 8GB VRAM available on the NVIDIA GeForce RTX 3070 Ti. This leaves 3.7GB of VRAM for context, allowing for a practical context window of around 16,000 tokens.

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 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 bartowski/microsoft_Phi-4-mini-instruct-GGUF --model microsoft_Phi-4-mini-instruct-Q8_0.gguf --interactive
ollama chat --model bartowski/microsoft_Phi-4-mini-instruct-GGUF --quantization Q8_0

4. Optimize for RTX 3070 Ti

For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, set --n-gpu-layers to 30 to maximize GPU utilization while ensuring enough VRAM for context. Enable flash attention (--flash-attn) to speed up inference. Given the 4.3GB VRAM usage, you can achieve a practical context window of around 16,000 tokens with the remaining 3.7GB VRAM.

Troubleshooting

Out of memory error during inference

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

Slow inference speed

Ensure flash attention (--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 re-downloading it.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the runtime environment or specific features. LM Studio is ideal for a user-friendly interface, llama.cpp offers low-level customization, and Jan is suitable for distributed training scenarios. However, Ollama provides a balanced approach with ease of use and good performance for most users.

Full Phi-4 Mini 3.8B details →

Other models that run great on RTX 3070 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 →