Can RTX 3070 Ti run Phi-3.5 Vision?

Yes — runs locally

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

Your VRAM

8 GB

Model size

4.2B

Best quant

Q4_K_M

VRAM needed

3.2 GB

The verdict

The RTX 3070 Ti (8 GB VRAM) handles Phi-3.5 Vision comfortably using the Q4_K_M quantization, which fits in 3.2 GB. Expected throughput is around 60 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 3070 Ti

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

TL;DR

Phi-3.5 Vision runs at Grade S on an NVIDIA GeForce RTX 3070 Ti with Q4_K_M quantization, achieving ~117 tok/sec.

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 512.15 or later) installed. Additionally, CUDA 11.4 or later is required for optimal performance.

Expected performance

With the Q4_K_M quantization, you can expect ~117 tok/sec and 3.2GB VRAM usage, leaving 4.8GB of VRAM for context. This allows 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 Q4_K_M quantized version of Phi-3.5 Vision (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 abetlen/Phi-3.5-vision-instruct-gguf --quantization Q4_K_M --n-gpu-layers 28 --flash-attn
ollama interactive

4. Optimize for RTX 3070 Ti

For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, set --n-gpu-layers to 28 to maximize GPU utilization while keeping within VRAM limits. Enable --flash-attn for faster attention computations. Tensor parallelism is not necessary for this model size and GPU configuration.

Troubleshooting

Out of memory errors during inference

Reduce --n-gpu-layers to 24 or 20 to lower VRAM usage.

Slow token generation speed

Ensure CUDA and NVIDIA drivers are up to date. Reinstall Ollama if necessary.

Model fails to load

Verify the model file integrity using 'ollama verify'. If corrupted, redownload the model.

Alternative runtimes

Alternative runtimes like LM Studio and llama.cpp can be used for more fine-grained control over optimizations, but Ollama provides a simpler and more user-friendly experience. For research purposes or custom integrations, consider using llama.cpp. For production environments, LM Studio offers robust deployment options.

Full Phi-3.5 Vision details →

Other models that run great on RTX 3070 Ti

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.

Want personalized recommendations for your exact setup? Detect my hardware →