Can RTX 5080 run Gemma 3 4B?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5080 (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 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Gemma 3 4B model runs at Grade S on the NVIDIA GeForce RTX 5080 with Q8_0 quantization, achieving ~173 tok/sec.
Prerequisites
Before starting, ensure you have at least 4GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60 or later) installed along with CUDA 11.8 or later.
Expected performance
With the Q8_0 quantization, you can expect the Gemma 3 4B model to run at approximately 173 tokens per second, using around 4.3GB of VRAM. This leaves about 11.7GB of VRAM for context, allowing for a practical context window of up to 32768 tokens without running out of memory.
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 --model google_gemma-3-4b-it-Q8_0 --context-length 32768
ollama chat --model google_gemma-3-4b-it-Q8_04. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 40 to utilize the full VRAM capacity. Enable flash attention with --flash-attn to speed up inference. Given the 16GB VRAM, you can comfortably run the model with a large context window while maintaining high token throughput.
Troubleshooting
Out of memory errors during inference
Reduce the --n-gpu-layers parameter to 30 or lower to decrease VRAM usage.
Slow inference times
Ensure that flash attention is enabled with --flash-attn 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
While Ollama is recommended for its ease of use and performance, you can also run Gemma 3 4B using LM Studio for a more graphical interface, or llama.cpp for more control over the command-line options. Jan is another alternative for those who prefer a different runtime environment, but Ollama provides the best balance of performance and simplicity for the NVIDIA GeForce RTX 5080.
Other models that run great on RTX 5080
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.
Want personalized recommendations for your exact setup? Detect my hardware →