Can RTX 4070 Ti SUPER run Phi-3.5 Mini 3.8B?

Yes — runs locally

~102 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

3.8B

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The RTX 4070 Ti SUPER (16 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 102 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 4070 Ti SUPER

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 the NVIDIA GeForce RTX 4070 Ti SUPER with Q8_0 quantization, achieving ~178 tok/sec.

Prerequisites

Before starting, ensure you have at least 4GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the recommended settings, you can expect the Phi-3.5 Mini 3.8B model to run at ~178 tok/sec, using around 4.3GB of VRAM. Given the remaining 11.7GB of VRAM, you can achieve a practical context window of up to 131,072 tokens, which is the maximum supported by the model.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Phi-3.5 Mini 3.8B model with Q8_0 quantization (3.8GB file size) from Hugging Face.

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 --n-gpu-layers 38 --flash-attn --tensor-parallelism 1

4. Optimize for RTX 4070 Ti SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 38 to utilize most of the available VRAM. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 1 for single-GPU operation. This configuration will use approximately 4.3GB of VRAM, leaving 11.7GB for context and other operations.

Troubleshooting

Out of memory error during inference

Reduce the number of --n-gpu-layers to 30 or lower to decrease VRAM usage.

Slow token generation speed

Ensure that --flash-attn is enabled and that your CUDA drivers are 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 be used for more advanced customization or different use cases. LM Studio is ideal for a graphical interface, llama.cpp offers more control over quantization and optimization, and Jan is suitable for lightweight deployment scenarios. However, Ollama provides a balanced and easy-to-use solution for most users on the NVIDIA GeForce RTX 4070 Ti SUPER.

Full Phi-3.5 Mini 3.8B details →

Other models that run great on RTX 4070 Ti SUPER

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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