Can RTX 4070 SUPER run Phi-3.5 Vision?

Yes — runs locally

~94 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

4.2B

Best quant

Q4_K_M

VRAM needed

3.2 GB

The verdict

The RTX 4070 SUPER (12 GB VRAM) handles Phi-3.5 Vision comfortably using the Q4_K_M quantization, which fits in 3.2 GB. Expected throughput is around 94 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Vision-language model from Microsoft. Can understand images and documents.

Setup tutorial: Phi-3.5 Vision on RTX 4070 SUPER

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

TL;DR

Run Phi-3.5 Vision on an NVIDIA GeForce RTX 4070 SUPER with Q4_K_M quantization for Grade S performance at ~175 tok/sec.

Prerequisites

Before starting, ensure you have at least 5GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.

Expected performance

With the recommended settings, you can expect the model to run at approximately 175 tokens per second, using around 3.2GB of VRAM. This leaves you with 8.8GB of VRAM for context, allowing for a practical context window of up to 131,072 tokens, depending on the complexity of the inputs.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Phi-3.5 Vision Q4_K_M quantized model (2.5GB) from Hugging Face.

ollama pull abetlen/Phi-3.5-vision-instruct-gguf:Phi-3.5-vision-instruct-Q4_K_M.gguf

3. Run it

ollama run --model Phi-3.5-vision-instruct-Q4_K_M.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 1

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 1 to avoid overloading the GPU. This configuration ensures that the model runs efficiently within the 12GB VRAM limit.

Troubleshooting

Out of memory error during inference

Reduce the number of --n-gpu-layers or decrease the batch size to fit within the 12GB VRAM limit.

Slow inference speed

Ensure that --flash-attn is enabled and that the CUDA toolkit is correctly installed and up-to-date.

Model fails to load

Verify that the model file is downloaded correctly and that the Ollama runtime is properly installed.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more customization or specific features. LM Studio is ideal for a graphical interface, llama.cpp offers more fine-grained control over execution, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 4070 SUPER.

Full Phi-3.5 Vision details →

Other models that run great on RTX 4070 SUPER

FAQ (20)

What GPU do I need to run Phi-3.5 Vision?

To run Phi-3.5 Vision, you need a GPU with at least 3.2 GB of VRAM. Higher VRAM will improve performance, especially for larger tasks.

Is Phi-3.5 Vision good for coding?

Phi-3.5 Vision is primarily designed for vision and language tasks, such as understanding images and documents. It may not be as optimized for coding-specific tasks compared to models like Codex or CodeLlama.

Phi-3.5 Vision vs Llama 3.1 8B?

Phi-3.5 Vision has 4.2 billion parameters and is specialized for vision-language tasks, while Llama 3.1 8B is a text-only model with 8 billion parameters, making it more versatile for text generation but less suited for image understanding.

Can I run Phi-3.5 Vision on a Mac?

Yes, you can run Phi-3.5 Vision on a Mac, but ensure your Mac has a compatible GPU with at least 3.2 GB of VRAM. Apple Silicon GPUs may require additional drivers or software.

How much VRAM does Phi-3.5 Vision need?

Phi-3.5 Vision requires 3.2 GB of VRAM, which is consistent across different quantization levels. More VRAM can help with larger batch sizes and more complex tasks.

Is Phi-3.5 Vision censored?

Phi-3.5 Vision is not inherently censored, but it adheres to ethical guidelines and may have filters to prevent harmful content. Users can configure additional safety measures as needed.

Is Phi-3.5 Vision commercial-use allowed?

Yes, Phi-3.5 Vision is licensed under the MIT License, which allows for commercial use. However, always review the specific terms of the license to ensure compliance.

Phi-3.5 Vision context length?

Phi-3.5 Vision has a context length of 131,072 tokens, allowing it to process very long sequences of text and images effectively.

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