Can RTX 4070 SUPER run Phi-4?
Yes — runs locally
~36 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles Phi-4 comfortably using the Q4_K_M quantization, which fits in 8.9 GB. Expected throughput is around 36 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 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-4 runs exceptionally well on the NVIDIA GeForce RTX 4070 SUPER with a Grade A performance, using the Q4_K_M quantization. Expect ~48 tok/sec with snappy responsiveness.
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 525.60 or later) installed along with CUDA 11.8 or later.
Expected performance
With the Q4_K_M quantization, you can expect a throughput of approximately 48 tokens per second, with 8.9GB of VRAM in use. This leaves a headroom of 3.1GB for context, allowing for a practical context window of around 10,000 tokens given the remaining VRAM.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Phi-4 model with Q4_K_M quantization (8.4GB file) from the Hugging Face repository.
ollama pull bartowski/phi-4-GGUF:phi-4-Q4_K_M.gguf3. Run it
ollama run phi-4-Q4_K_M.gguf --interactive
ollama chat phi-4-Q4_K_M.gguf4. Optimize for RTX 4070 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the --n-gpu-layers flag to offload some layers to CPU memory. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. Given the 12GB VRAM, you can set --n-gpu-layers to 32 to balance between speed and memory usage, leaving enough headroom for larger contexts.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers <N>, where <N> is a lower value such as 24 or 16.
Slow inference speed
Enable flash attention with --flash-attn and ensure your CUDA drivers are up to date.
Model fails to load
Verify the integrity of the downloaded model file and try downloading it again using the 'ollama pull' command.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more graphical interface, llama.cpp for fine-grained control over optimizations, or Jan for a lightweight, easy-to-use alternative. Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 4070 SUPER.
Other models that run great on RTX 4070 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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