Can RTX 4080 SUPER run Phi-3.5 Vision?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 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 114 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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-3.5 Vision runs at Grade S on the NVIDIA GeForce RTX 4080 SUPER with Q4_K_M quantization, achieving ~233 tok/sec.
Prerequisites
Before starting, ensure you have at least 5GB of free disk space, a compatible operating system (Windows 10/11 or Linux), NVIDIA driver version 525.60.13 or later, and CUDA 11.8 or later installed.
Expected performance
With the recommended settings, you can expect Phi-3.5 Vision to run at ~233 tok/sec, using approximately 3.2GB of VRAM. Given the remaining 12.8GB of VRAM, you can achieve a practical context window of up to 131072 tokens, allowing for extensive image and document understanding.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q4_K_M quantized version of Phi-3.5 Vision (2.5GB file) from Hugging Face.
ollama pull abetlen/Phi-3.5-vision-instruct-gguf:Phi-3.5-vision-instruct-Q4_K_M.gguf3. Run it
ollama run Phi-3.5-vision-instruct-Q4_K_M.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 2
ollama chat Phi-3.5-vision-instruct-Q4_K_M.gguf4. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU's memory. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload across the GPU cores. This configuration will use approximately 3.2GB of VRAM, leaving 12.8GB available for context and other tasks.
Troubleshooting
Out of memory error during inference
Reduce the number of --n-gpu-layers or disable --tensor-parallelism to lower VRAM usage.
Slow inference speed
Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.
Model fails to load
Verify the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different performance profiles. LM Studio offers a graphical interface and is suitable for users who prefer a visual setup. llama.cpp provides a lightweight and highly customizable runtime, ideal for fine-tuning performance. Jan is another efficient runtime that supports a wide range of models and is suitable for users who need flexibility in their setup.
Other models that run great on RTX 4080 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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