Can RTX 3090 Ti run Phi-4?
Yes — runs locally
~42 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 3090 Ti (24 GB VRAM) handles Phi-4 comfortably using the Q8_0 quantization, which fits in 15.0 GB. Expected throughput is around 42 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Microsoft's 14B parameter model. Punches well above its weight on reasoning.
Setup tutorial: Phi-4 on RTX 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Phi-4 (14B parameters) on an NVIDIA GeForce RTX 3090 Ti with Q8_0 quantization for Grade S performance at ~57 tokens per second.
Prerequisites
Before starting, ensure you have at least 15GB of free disk space, a compatible operating system (Windows or Linux), NVIDIA driver version 470.82.01 or later, and CUDA 11.4 or later installed.
Expected performance
With the Q8_0 quantization, you can expect the model to run at approximately 57 tokens per second, using around 15.0GB of VRAM. The remaining 9.0GB of VRAM provides ample headroom for handling large context windows, making it suitable for tasks requiring extensive reasoning and context retention.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Phi-4 model with Q8_0 quantization (14.5GB file) from the Hugging Face repository.
ollama pull bartowski/phi-4-GGUF:phi-4-Q8_0.gguf3. Run it
ollama run phi-4-Q8_0.gguf --interactive
ollama chat --model phi-4-Q8_0.gguf4. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 14000 to maximize GPU utilization while leaving enough VRAM for context. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 15.0GB VRAM in use, you will have approximately 9.0GB of VRAM left for context, allowing for a practical context window of up to 16384 tokens.
Troubleshooting
Out of memory errors during inference
Reduce the number of layers offloaded to the GPU using --n-gpu-layers. For example, try --n-gpu-layers 12000.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn. Also, check that your CUDA drivers and Ollama runtime are up to date.
Model fails to load
Verify that the model file has been downloaded correctly and is not corrupted. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a more user-friendly interface and is suitable for users who prefer a graphical environment. llama.cpp is highly customizable and can be fine-tuned for specific hardware configurations. Jan is lightweight and efficient but may lack some features found in Ollama. Choose based on your specific needs and comfort level with command-line tools.
Other models that run great on RTX 3090 Ti
FAQ (20)
What GPU do I need to run Phi-4?
To run Phi-4, you need a GPU with at least 8.9 GB of VRAM, but 15.0 GB is recommended for optimal performance.
Is Phi-4 good for coding?
Yes, Phi-4 is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 16,384 tokens.
Phi-4 vs Llama 3.1 8B?
Phi-4 has 14 billion parameters compared to Llama 3.1's 8 billion, making it more powerful for complex tasks but requiring more VRAM.
Can I run Phi-4 on a Mac?
Yes, you can run Phi-4 on a Mac with a compatible GPU, such as an AMD or NVIDIA card with sufficient VRAM.
How much VRAM does Phi-4 need?
Phi-4 requires between 8.9 GB and 15.0 GB of VRAM, depending on the quantization level used.
Is Phi-4 censored?
Phi-4 is not inherently censored, but its outputs can be filtered based on the implementation and configuration settings.
Is Phi-4 commercial-use allowed?
Yes, Phi-4 is licensed under the MIT License, which allows for commercial use without restriction.
Phi-4 context length?
Phi-4 has a context length of 16,384 tokens, allowing it to handle longer sequences of text effectively.
Want personalized recommendations for your exact setup? Detect my hardware →