Can RTX 4070 Ti SUPER run Phi-4?
Yes — runs locally
~42 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4070 Ti SUPER (16 GB VRAM) handles Phi-4 comfortably using the Q5_K_M quantization, which fits in 10.4 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 4070 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-4 runs at Grade S on the NVIDIA GeForce RTX 4070 Ti SUPER with the Q5_K_M quantization, achieving ~55 tokens/second.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.12 or later) with CUDA 11.8 installed.
Expected performance
With the Q5_K_M quantization, you can expect Phi-4 to run at approximately 55 tokens/second, utilizing about 10.4GB of VRAM. Given the remaining 5.6GB of VRAM, you can comfortably handle a practical context window of up to 8192 tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama config set cuda_path /usr/local/cuda2. Download the model
Download the Phi-4 model with Q5_K_M quantization (9.9GB file) from the Hugging Face repository.
ollama pull bartowski/phi-4-GGUF:phi-4-Q5_K_M.gguf3. Run it
ollama run phi-4-Q5_K_M --interactive
ollama chat phi-4-Q5_K_M4. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and consider using tensor parallelism with --tensor-parallel-size 2. This configuration will help achieve the target ~55 tokens/second while keeping VRAM usage around 10.4GB, leaving 5.6GB for context.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers with --n-gpu-layers or decrease the context length.
Slow token generation speed
Ensure CUDA is properly configured and try enabling flash attention with --flash-attn.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
While Ollama is recommended for its ease of use and performance, you can also run Phi-4 using alternative runtimes like LM Studio for a more graphical interface, llama.cpp for low-level control, or Jan for specialized use cases. Choose an alternative runtime if you need specific features not supported by Ollama, such as custom model modifications or advanced profiling tools.
Other models that run great on RTX 4070 Ti SUPER
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.
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