Can RTX 3070 Ti run Phi-3.5 Mini 3.8B?
Yes — runs locally
~60 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3070 Ti (8 GB VRAM) handles Phi-3.5 Mini 3.8B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 60 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Tiny but capable 3.8B model. Runs on almost any hardware including phones.
Setup tutorial: Phi-3.5 Mini 3.8B on RTX 3070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Runs Phi-3.5 Mini 3.8B on NVIDIA GeForce RTX 3070 Ti with Grade S performance at ~89 tok/sec using Q8_0 quantization. Requires 4.3GB VRAM.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 470.82 or later, and CUDA 11.4 or later installed.
Expected performance
With the Q8_0 quantization, you can expect ~89 tok/sec performance and 4.3GB VRAM usage, leaving 3.7GB of VRAM for context. This allows for a practical context window of up to 131072 tokens, though the actual usable context will depend on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Phi-3.5 Mini 3.8B model with Q8_0 quantization (3.8GB file).
ollama pull bartowski/Phi-3.5-mini-instruct-GGUF:Phi-3.5-mini-instruct-Q8_0.gguf3. Run it
ollama run Phi-3.5-mini-instruct-Q8_0 --n-gpu-layers 16 --flash-attn
ollama chat Phi-3.5-mini-instruct-Q8_04. Optimize for RTX 3070 Ti
For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, set --n-gpu-layers to 16 to utilize the GPU effectively while keeping within the VRAM limit. Enable --flash-attn for faster inference and better memory efficiency. Tensor parallelism is not necessary for this model size and GPU configuration.
Troubleshooting
Out of memory errors during inference.
Reduce --n-gpu-layers to 8 or lower and ensure --flash-attn is enabled.
Slow inference speed.
Ensure CUDA is properly installed and update your NVIDIA drivers to the latest version.
Model fails to load.
Check that the model file is correctly downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio provides a more user-friendly interface and is suitable for users who prefer a GUI. llama.cpp offers more fine-grained control over model parameters and is ideal for advanced users. Jan is a lightweight runtime that is easy to set up but may lack some features compared to Ollama. Choose based on your specific needs and comfort level with command-line tools.
Other models that run great on RTX 3070 Ti
FAQ (20)
What GPU do I need to run Phi-3.5 Mini 3.8B?
Phi-3.5 Mini 3.8B requires a GPU with at least 2.7 GB of VRAM, but 4.3 GB is recommended for optimal performance.
Is Phi-3.5 Mini 3.8B good for coding?
Phi-3.5 Mini 3.8B is capable of generating code and providing coding assistance, but its performance is best suited for simpler tasks due to its 3.8B parameters.
Phi-3.5 Mini 3.8B vs Llama 3.1 8B?
Phi-3.5 Mini 3.8B has 3.8B parameters, making it smaller and more resource-efficient than Llama 3.1 8B, which has 8B parameters and requires more VRAM and computational power.
Can I run Phi-3.5 Mini 3.8B on a Mac?
Yes, Phi-3.5 Mini 3.8B can run on a Mac, provided your Mac has a compatible GPU with at least 2.7 GB of VRAM.
How much VRAM does Phi-3.5 Mini 3.8B need?
Phi-3.5 Mini 3.8B requires a minimum of 2.7 GB of VRAM, but 4.3 GB is recommended for better performance, depending on the quantization level.
Is Phi-3.5 Mini 3.8B censored?
Phi-3.5 Mini 3.8B is not inherently censored, but it may include content filters to prevent harmful or inappropriate content.
Is Phi-3.5 Mini 3.8B commercial-use allowed?
Yes, Phi-3.5 Mini 3.8B is licensed under the MIT License, which allows for commercial use.
Phi-3.5 Mini 3.8B context length?
Phi-3.5 Mini 3.8B supports a context length of 131,072 tokens, which is quite large and allows for extensive context in conversations and tasks.
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