Can RTX 5070 Ti run Phi-4?

Yes — runs locally

~48 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

16 GB

Model size

14B

Best quant

Q5_K_M

VRAM needed

10.4 GB

The verdict

The RTX 5070 Ti (16 GB VRAM) handles Phi-4 comfortably using the Q5_K_M quantization, which fits in 10.4 GB. Expected throughput is around 48 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Microsoft's 14B parameter model. Punches well above its weight on reasoning.

Setup tutorial: Phi-4 on RTX 5070 Ti

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

TL;DR

Phi-4 runs exceptionally well on the NVIDIA GeForce RTX 5070 Ti with a Grade S performance, using the Q5_K_M quantization. Expect around 55 tokens per second.

Prerequisites

Before starting, ensure you have at least 20GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA driver (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the Q5_K_M quantization, expect Phi-4 to run at approximately 55 tokens per second, consuming 10.4GB of VRAM. The remaining 5.6GB of VRAM provides ample headroom for a practical context window of up to 10,000 tokens, ensuring smooth and efficient operation.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Phi-4 model with Q5_K_M quantization (9.9GB file size) from the Hugging Face repository.

ollama pull bartowski/phi-4-GGUF:phi-4-Q5_K_M.gguf

3. Run it

ollama run --model phi-4-Q5_K_M.gguf --interactive
ollama chat --model phi-4-Q5_K_M.gguf

4. Optimize for RTX 5070 Ti

For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 14000 to fully utilize the GPU. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 10.4GB VRAM in use, you have 5.6GB of headroom for context, allowing for a practical context window of up to 10,000 tokens.

Troubleshooting

Out of memory errors during inference

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

Slow token generation

Ensure that flash attention (--flash-attn) is enabled and that your CUDA installation is up to date.

Model fails to load

Verify that the model file has been downloaded correctly and that there are no disk space issues.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more graphical interface, llama.cpp for fine-grained control over quantization, or Jan for lightweight deployment. Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 5070 Ti.

Full Phi-4 details →

Other models that run great on RTX 5070 Ti

FAQ (20)

What GPU do I need to run Phi-4?

To run Phi-4, you need a GPU with at least 8.9 GB of VRAM, but 15.0 GB is recommended for optimal performance.

Is Phi-4 good for coding?

Yes, Phi-4 is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 16,384 tokens.

Phi-4 vs Llama 3.1 8B?

Phi-4 has 14 billion parameters compared to Llama 3.1's 8 billion, making it more powerful for complex tasks but requiring more VRAM.

Can I run Phi-4 on a Mac?

Yes, you can run Phi-4 on a Mac with a compatible GPU, such as an AMD or NVIDIA card with sufficient VRAM.

How much VRAM does Phi-4 need?

Phi-4 requires between 8.9 GB and 15.0 GB of VRAM, depending on the quantization level used.

Is Phi-4 censored?

Phi-4 is not inherently censored, but its outputs can be filtered based on the implementation and configuration settings.

Is Phi-4 commercial-use allowed?

Yes, Phi-4 is licensed under the MIT License, which allows for commercial use without restriction.

Phi-4 context length?

Phi-4 has a context length of 16,384 tokens, allowing it to handle longer sequences of text effectively.

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