Can RTX 3080 Ti run Phi-3.5 Mini 3.8B?

Yes — runs locally

~74 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

3.8B

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The RTX 3080 Ti (12 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 74 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 3080 Ti

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

The Phi-3.5 Mini 3.8B model runs at Grade S on an NVIDIA GeForce RTX 3080 Ti with Q8_0 quantization, achieving ~133 tok/sec.

Prerequisites

Before starting, ensure you have at least 4GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 512.15 or later) installed along with CUDA 11.4 or later.

Expected performance

With the Q8_0 quantization, you can expect the model to run at approximately 133 tokens per second, using around 4.3GB of VRAM. This leaves about 7.7GB of VRAM for context, allowing for a practical context window of up to 131,072 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized model (3.8GB file) from the Hugging Face repository.

ollama pull bartowski/Phi-3.5-mini-instruct-GGUF:Phi-3.5-mini-instruct-Q8_0.gguf

3. Run it

ollama run Phi-3.5-mini-instruct-Q8_0.gguf --n-gpu-layers 38 --flash-attn --tensor-parallelism 2

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, set --n-gpu-layers to 38 to utilize most of the available VRAM while leaving some headroom. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to leverage the multi-core architecture of the GPU.

Troubleshooting

Out of memory errors during inference

Reduce the number of --n-gpu-layers or decrease the batch size.

Slow inference speed

Ensure that --flash-attn is enabled and that the CUDA toolkit is correctly installed and up to date.

Model fails to load

Verify that the model file has been downloaded correctly and that there are no file corruption issues.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used. LM Studio is ideal for a graphical interface, llama.cpp offers more control over low-level optimizations, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 3080 Ti.

Full Phi-3.5 Mini 3.8B details →

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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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