Can RTX 4090 run Phi-3.5 Vision?
Yes — runs locally
~144 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (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 144 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 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Phi-3.5 Vision on an NVIDIA GeForce RTX 4090 with Grade S performance at ~350 tok/sec using the Q4_K_M quantization. The model requires 3.2GB VRAM and runs efficiently on this GPU.
Prerequisites
Before starting, ensure you have at least 2.5GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the recommended settings, you can expect the Phi-3.5 Vision model to run at approximately 350 tokens per second, using around 3.2GB of VRAM. This leaves you with 20.8GB of VRAM headroom, allowing for a practical context window of up to 131,072 tokens, which is ideal for handling large images and documents.
1. Install runtimeOllama
pip install ollama
ollama setup2. Download the model
Download the Q4_K_M quantized Phi-3.5 Vision model (2.5GB) from the Hugging Face repository.
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 12 --flash-attn --tensor-parallelism 2
ollama interactive Phi-3.5-vision-instruct-Q4_K_M.gguf4. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, set --n-gpu-layers to 12 to utilize most of the GPU's memory. Enable flash-attn for faster attention computations and set --tensor-parallelism to 2 to leverage the GPU's parallel processing capabilities. This configuration ensures that the model runs efficiently without exceeding the available VRAM.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 8 or 6 and decrease --tensor-parallelism to 1.
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 and configured.
Alternative runtimes
For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for low-level control, or Jan for specialized use cases. Each runtime has its own strengths, but Ollama provides a balanced approach with good performance and ease of use on the NVIDIA GeForce RTX 4090.
Other models that run great on RTX 4090
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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