Can RTX 4070 SUPER run Gemma 3 4B?
Yes — runs locally
~94 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles Gemma 3 4B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 94 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 4070 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 4B on an NVIDIA GeForce RTX 4070 SUPER with a Grade S performance, using the Q8_0 quantization, achieving ~130 tok/sec.
Prerequisites
Before starting, ensure you have at least 12GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.11 or later), and CUDA 11.8 or later installed.
Expected performance
You can expect a token generation rate of approximately 130 tok/sec, with 4.3GB of VRAM in use. The remaining 7.7GB of VRAM provides ample headroom for handling large context windows, enabling efficient processing of long sequences.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of Gemma 3 4B, which is a 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 --context-length 32768 --n-gpu-layers 32 --flash-attn
ollama chat google_gemma-3-4b-it-Q8_04. Optimize for RTX 4070 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory. Enable --flash-attn to speed up attention computations. With 4.3GB VRAM used by the model, you will have 7.7GB of VRAM left for context, allowing for a practical context window of up to 32K tokens.
Troubleshooting
Out of Memory (OOM) errors during inference.
Reduce the number of layers offloaded to the GPU by decreasing --n-gpu-layers. For example, try --n-gpu-layers 24.
Slow token generation rate.
Ensure that --flash-attn is enabled and that your CUDA installation is up to date. You can also try increasing the batch size if your application allows it.
Model fails to load.
Verify that the model file has been downloaded correctly and that there are no issues with the file path. Re-run the download command if necessary.
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. LM Studio is ideal for a graphical interface, llama.cpp offers a lightweight and highly customizable solution, and Jan is suitable for distributed training and inference scenarios. However, Ollama provides a straightforward and efficient way to run Gemma 3 4B on the NVIDIA GeForce RTX 4070 SUPER.
Other models that run great on RTX 4070 SUPER
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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