Can RTX 4080 SUPER run Gemma 3 4B?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 4B Q8_0 on an NVIDIA GeForce RTX 4080 SUPER for Grade S performance at ~173 tok/sec. Requires 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 526.95 or later) with CUDA 11.8 installed.
Expected performance
You can expect the model to run at approximately 173 tokens per second with 4.3GB VRAM in use. Given the remaining 11.7GB of VRAM, you can achieve a practical context window of up to 32768 tokens, making it suitable for long-form content generation.
1. Install runtimeOllama
pip install ollama
ollama config set cuda_path /usr/local/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 --model google_gemma-3-4b-it-Q8_0 --n-gpu-layers 32 --flash-attn
ollama chat --model google_gemma-3-4b-it-Q8_04. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn to speed up attention computations. 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 16 or enable --cpu-offload to offload some layers to CPU.
Slow inference speed
Ensure that --flash-attn is enabled and that your CUDA installation is up to date.
Model not loading
Check the integrity of the downloaded model file and try re-downloading it using the 'ollama pull' command.
Alternative runtimes
For users who prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for a lightweight, easy-to-deploy solution. Each has its own strengths, but Ollama provides a balanced approach for most use cases on the NVIDIA GeForce RTX 4080 SUPER.
Other models that run great on RTX 4080 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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