Can RTX 4060 Ti 16GB run Phi-3.5 Mini 3.8B?

Yes — runs locally

~78 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 4060 Ti 16GB (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 78 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 4060 Ti 16GB

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 4060 Ti 16GB 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 Q8_0 quantization, you can expect the model to run at approximately 178 tokens per second, using around 4.3GB of VRAM. This leaves 11.7GB of VRAM available for context, allowing for a large practical context window of up to 131072 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized Phi-3.5 Mini 3.8B model (3.8GB file) 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 32 --flash-attn --context-length 131072

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 32 to fully utilize the 16GB VRAM. Enable --flash-attn for faster attention computations. Given the 16GB VRAM, you can achieve a practical context window close to the maximum 131072 tokens, with 11.7GB VRAM remaining for context after loading the model.

Troubleshooting

Model fails to load due to insufficient VRAM.

Reduce --n-gpu-layers to 24 or 16.

Performance is below 178 tok/sec.

Ensure --flash-attn is enabled and update your NVIDIA drivers to the latest version.

Context window is smaller than expected.

Check if other processes are consuming VRAM and close them to free up more memory.

Alternative runtimes

Alternatively, you can use LM Studio for a more user-friendly interface, llama.cpp for more fine-grained control over quantization and performance settings, or Jan for a lightweight, easy-to-use runtime. Choose Ollama for its simplicity and ease of use, especially if you are new to running LLMs on your GPU.

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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