Can RTX 3060 12GB run Phi-3.5 Mini 3.8B?

Yes — runs locally

~58 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

12 GB

Model size

3.8B

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The RTX 3060 12GB (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 58 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. 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 3060 12GB

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

TL;DR

Run Phi-3.5 Mini 3.8B on an NVIDIA GeForce RTX 3060 12GB with Grade S performance at ~133 tok/sec using the Q8_0 quantization.

Prerequisites

Before starting, ensure you have at least 5GB 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 11.2 or later.

Expected performance

With the Q8_0 quantization, you can expect ~133 tok/sec performance with 4.3GB VRAM in use, leaving 7.7GB of VRAM for context. This allows for a practical context window of around 100,000 tokens, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the Phi-3.5 Mini 3.8B model in 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
ollama chat Phi-3.5-mini-instruct-Q8_0

4. Optimize for RTX 3060 12GB

For optimal performance on the NVIDIA GeForce RTX 3060 12GB, use the --n-gpu-layers 38 flag to allocate layers efficiently within the 12GB VRAM. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. Tensor parallelism is not necessary for this model size and GPU configuration.

Troubleshooting

Out of memory errors during inference

Reduce the number of GPU layers using --n-gpu-layers <number> or decrease the batch size.

Slow inference speeds

Ensure flash attention is enabled with --flash-attn and check that your CUDA installation is up to date.

Model fails to load

Verify the model file integrity and try re-downloading it using the ollama pull command.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment or specific features not supported by Ollama. For example, LM Studio provides a GUI interface, while llama.cpp offers more fine-grained control over quantization and optimization settings. Jan is suitable for lightweight deployments on resource-constrained devices.

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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