Can RTX 3070 Ti run Phi-4?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 3070 Ti (8 GB VRAM) handles Phi-4 comfortably using the Q4_K_M quantization, which fits in 8.9 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU in interactive use. Microsoft's 14B parameter model. Punches well above its weight on reasoning.
Setup tutorial: Phi-4 on RTX 3070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-4 runs on an NVIDIA GeForce RTX 3070 Ti with a Grade C performance, using the Q4_K_M quantization. Expect ~32 tokens per second with 8.9GB VRAM usage.
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 510.47.03 or later) installed along with CUDA 11.2 or higher.
Expected performance
With the Q4_K_M quantization, you can expect a token generation rate of approximately 32 tokens per second, utilizing 8.9GB of VRAM. This leaves about -0.9GB of VRAM 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 size) from the Hugging Face repository.
ollama pull bartowski/phi-4-GGUF:phi-4-Q4_K_M.gguf3. Run it
ollama run --model phi-4-Q4_K_M.gguf --interactive
ollama chat --model phi-4-Q4_K_M.gguf4. Optimize for RTX 3070 Ti
For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, set --n-gpu-layers to 28 to fit the model within the available VRAM. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. Tensor parallelism is not recommended due to the limited VRAM.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers (--n-gpu-layers 20) or enable CPU offloading (--cpu-offload)
Slow token generation
Ensure flash attention is enabled (--flash-attn) and check 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
If you prefer a different runtime, consider LM Studio for a more user-friendly interface, or llama.cpp for more advanced customization options. Jan is another lightweight option but may not support all features of Phi-4. Choose based on your specific needs and the level of control you require.
Other models that run great on RTX 3070 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.
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