Can RTX 3090 Ti run Gemma 3 4B?
Yes — runs locally
~96 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 Ti (24 GB VRAM) handles Gemma 3 4B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 96 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 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 4B Q8_0 on an NVIDIA GeForce RTX 3090 Ti for Grade S performance at ~260 tok/sec, using 4.3GB VRAM.
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 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the recommended settings, you can expect Gemma 3 4B Q8_0 to run at approximately 260 tokens per second, using around 4.3GB of VRAM. This leaves 19.6GB of VRAM available for context, allowing for a practical context window of up to 32768 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. 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 4096 --flash-attn --tensor-parallelism 14. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, set --n-gpu-layers to 4096 to fully utilize the GPU memory. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 1 to avoid splitting the model across multiple GPUs. This configuration ensures that the model runs efficiently within the 24GB VRAM limit.
Troubleshooting
Out of memory error during inference
Reduce the number of layers offloaded to the GPU by decreasing the --n-gpu-layers parameter, e.g., --n-gpu-layers 2048.
Slow inference speed
Ensure that --flash-attn is enabled and that the CUDA toolkit is correctly installed and up-to-date.
Model fails to load
Verify that the model file has been downloaded correctly and that the Ollama runtime is properly installed. Try re-downloading the model file.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different use cases. LM Studio offers a graphical interface and is suitable for users who prefer a visual setup. llama.cpp provides more control over quantization and is ideal for fine-tuning performance. Jan is a lightweight option for quick prototyping and testing. For the NVIDIA GeForce RTX 3090 Ti, Ollama is generally the most straightforward and efficient choice.
Other models that run great on RTX 3090 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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