Can RTX 4070 Ti SUPER run Phi-3.5 Vision?

Yes — runs locally

~102 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

4.2B

Best quant

Q4_K_M

VRAM needed

3.2 GB

The verdict

The RTX 4070 Ti SUPER (16 GB VRAM) handles Phi-3.5 Vision comfortably using the Q4_K_M quantization, which fits in 3.2 GB. Expected throughput is around 102 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 Ti SUPER

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

TL;DR

Phi-3.5 Vision runs at Grade S on the NVIDIA GeForce RTX 4070 Ti SUPER with Q4_K_M quantization, achieving ~233 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB 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 Phi-3.5 Vision to run at approximately 233 tokens per second with 3.2GB of VRAM in use. The remaining 12.8GB of VRAM provides ample headroom for a practical context window of up to 131,072 tokens, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama setup

2. Download the model

Download the Q4_K_M quantized Phi-3.5 Vision 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 Phi-3.5-vision-instruct-Q4_K_M.gguf --n-gpu-layers 32 --flash-attn
ollama interactive Phi-3.5-vision-instruct-Q4_K_M.gguf

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 most of the GPU's memory. Enable flash attention (--flash-attn) to speed up inference. With 3.2GB VRAM used by the model, you have 12.8GB of VRAM available for context, allowing for a large context window.

Troubleshooting

Out of memory errors during inference

Reduce the number of GPU layers with --n-gpu-layers 16 or lower.

Slow inference times

Ensure flash attention is enabled with --flash-attn and check your CUDA installation.

Model not loading

Verify the model file integrity and try re-downloading it using the ollama pull command.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment or specific features. LM Studio is ideal for a graphical interface, llama.cpp offers more quantization options, and Jan is suitable for distributed training and inference scenarios.

Full Phi-3.5 Vision details →

Other models that run great on RTX 4070 Ti 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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