Can RTX 5060 Ti run Gemma 3 4B?

Yes — runs locally

~114 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The RTX 5060 Ti (16 GB VRAM) handles Gemma 3 4B 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. Balanced 4B model with strong reasoning. Great for iPhones.

Setup tutorial: Gemma 3 4B on RTX 5060 Ti

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

TL;DR

Run Gemma 3 4B Q8_0 on an NVIDIA GeForce RTX 5060 Ti for Grade S performance at ~173 tok/sec. Requires 4.3GB VRAM.

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 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

You can expect ~173 tok/sec performance with 4.3GB VRAM in use, leaving 11.7GB for context. This setup allows for a practical context window of up to 32K tokens, given the remaining VRAM.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized version of Gemma 3 4B (3.8GB file) from Hugging Face.

ollama pull bartowski/google_gemma-3-4b-it-GGUF:google_gemma-3-4b-it-Q8_0.gguf

3. Run it

ollama run google_gemma-3-4b-it-Q8_0 --n-gpu-layers 4096 --flash-attn --context-length 32768

4. Optimize for RTX 5060 Ti

For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, set --n-gpu-layers to 4096 to fully utilize the GPU. Enable --flash-attn for faster inference and better memory efficiency. With 4.3GB VRAM used by the model, you have 11.7GB of VRAM left for context, allowing for a large practical context window.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 2048 and decrease --context-length to 16384.

Slow inference speed

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

Model fails to load

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

Alternative runtimes

Consider using LM Studio for a more user-friendly interface, llama.cpp for advanced customization, or Jan for lightweight deployment. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 5060 Ti.

Full Gemma 3 4B details →

Other models that run great on RTX 5060 Ti

FAQ (20)

What GPU do I need to run Gemma 3 4B?

To run Gemma 3 4B, you need a GPU with at least 2.8 GB of VRAM for the lowest quantization level, up to 4.3 GB for higher quantizations.

Is Gemma 3 4B good for coding?

Gemma 3 4B is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 32,768 tokens.

Gemma 3 4B vs Llama 3.1 8B?

Gemma 3 4B has fewer parameters (4B vs 8B) but offers a larger context length (32,768 tokens) and better performance on mobile devices like iPhones.

Can I run Gemma 3 4B on a Mac?

Yes, you can run Gemma 3 4B on a Mac, especially if your Mac has a compatible GPU with at least 2.8 GB of VRAM.

How much VRAM does Gemma 3 4B need?

Gemma 3 4B requires between 2.8 GB and 4.3 GB of VRAM, depending on the quantization level used.

Is Gemma 3 4B censored?

Gemma 3 4B is not inherently censored, but its responses may be filtered based on the implementation and settings used.

Is Gemma 3 4B commercial-use allowed?

Gemma 3 4B is licensed under the 'gemma' license, which allows for commercial use, provided you comply with the terms of the license.

Gemma 3 4B context length?

Gemma 3 4B has a context length of 32,768 tokens, allowing it to handle very long sequences of text.

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