Can RTX 3090 Ti run Phi-3.5 Vision?

Yes — runs locally

~96 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

4.2B

Best quant

Q4_K_M

VRAM needed

3.2 GB

The verdict

The RTX 3090 Ti (24 GB VRAM) handles Phi-3.5 Vision comfortably using the Q4_K_M quantization, which fits in 3.2 GB. Expected throughput is around 96 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 3090 Ti

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

TL;DR

Run Phi-3.5 Vision on an NVIDIA GeForce RTX 3090 Ti with Q4_K_M quantization for Grade S performance at ~350 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) with CUDA 11.7 installed.

Expected performance

With the Q4_K_M quantization, you can expect ~350 tok/sec performance, utilizing 3.2GB of VRAM. The remaining 20.8GB of VRAM allows for a practical context window of up to 131072 tokens, making it suitable for complex vision-language tasks.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the Phi-3.5-vision-instruct-Q4_K_M.gguf 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 abetlen/Phi-3.5-vision-instruct-gguf --quant Q4_K_M --n-gpu-layers 12 --flash-attn
ollama interactive

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers 12 flag to offload some layers to the CPU, enabling flash attention (--flash-attn) for faster inference. With 3.2GB VRAM usage, you will have 20.8GB of VRAM headroom for larger context windows.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers using --n-gpu-layers 8 or lower.

Slow inference speed

Ensure flash attention is enabled with --flash-attn and check that CUDA is properly configured.

Model fails to load

Verify the model file integrity and try re-downloading with the same command.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different hardware setups. LM Studio is ideal for GUI-based interaction, llama.cpp offers fine-grained control over quantization, and Jan is suitable for distributed inference across multiple GPUs.

Full Phi-3.5 Vision details →

Other models that run great on RTX 3090 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.

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