Can RTX 4080 SUPER run Phi-3.5 Mini 3.8B?

Yes — runs locally

~114 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 4080 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 114 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 4080 SUPER

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

TL;DR

Run Phi-3.5 Mini 3.8B on an NVIDIA GeForce RTX 4080 SUPER with Q8_0 quantization for Grade S performance at ~178 tok/sec.

Prerequisites

Before starting, ensure you have at least 4GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.8 or later installed.

Expected performance

With the recommended settings, you can expect a token generation speed of ~178 tok/sec and 4.3GB of VRAM in use. The remaining 11.7GB of VRAM allows for a practical context window of up to 131072 tokens, making it suitable for long-form text generation and large context tasks.

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).

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 32 --flash-attn --tensor-parallelism 2
ollama chat

4. Optimize for RTX 4080 SUPER

For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload efficiently. This configuration will use approximately 4.3GB of VRAM, leaving 11.7GB for context and other tasks.

Troubleshooting

Out of memory error during inference.

Reduce --n-gpu-layers to 16 or enable --cpu-offload to offload some layers to the CPU.

Low token generation speed.

Ensure that CUDA is properly installed and that the GPU is being utilized. Check the --flash-attn flag is enabled.

Model not found error.

Verify the model file path and ensure the model has been successfully downloaded using the 'ollama pull' command.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. Use LM Studio for a more user-friendly interface, llama.cpp for low-level control and customization, and Jan for distributed training scenarios. However, Ollama is recommended for its ease of use and efficient performance on the NVIDIA GeForce RTX 4080 SUPER.

Full Phi-3.5 Mini 3.8B details →

Other models that run great on RTX 4080 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.

Want personalized recommendations for your exact setup? Detect my hardware →