Can RTX 4060 Ti 16GB run Gemma 3 4B?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4060 Ti 16GB (16 GB VRAM) handles Gemma 3 4B 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. Balanced 4B model with strong reasoning. Great for iPhones.
Setup tutorial: Gemma 3 4B on RTX 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 4B on an NVIDIA GeForce RTX 4060 Ti 16GB with Grade S performance, using the Q8_0 quantization for ~173 tok/sec.
Prerequisites
Before starting, ensure you have at least 8GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect ~173 tok/sec with 4.3GB VRAM in use, leaving 11.7GB of VRAM for context. This setup allows for a practical context window of up to 32768 tokens, making it ideal for tasks requiring extensive context.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. 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.gguf3. Run it
ollama run google_gemma-3-4b-it-Q8_0 --n-gpu-layers 32 --flash-attn --context-length 327684. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use --n-gpu-layers 32 to offload layers to the GPU, enable --flash-attn for efficient attention computation, and set --context-length to 32768. The 16GB VRAM allows for a large context window while maintaining high throughput.
Troubleshooting
Low token generation speed
Increase --n-gpu-layers to 32 and ensure --flash-attn is enabled.
Out of memory errors
Reduce --context-length to 16384 or lower.
Model not loading
Verify that the model file is correctly downloaded and not corrupted.
Alternative runtimes
Alternative runtimes include LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, and Jan for multi-GPU setups. Use these alternatives if you need features not provided by Ollama, such as custom model training or advanced inference settings.
Other models that run great on RTX 4060 Ti 16GB
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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