Can RTX 3080 Ti run Gemma 3 4B?
Yes — runs locally
~74 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3080 Ti (12 GB VRAM) handles Gemma 3 4B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 74 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 3080 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Gemma 3 4B model runs at Grade S on an NVIDIA GeForce RTX 3080 Ti with Q8_0 quantization, achieving ~130 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 470.82.01 or later, and CUDA 11.4 or later installed.
Expected performance
With the Q8_0 quantization, you can expect ~130 tok/sec performance, using approximately 4.3GB of VRAM. This leaves 7.7GB of VRAM available for context, allowing for a practical context window of up to 32768 tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama config set device 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
ollama chat google_gemma-3-4b-it-Q8_04. Optimize for RTX 3080 Ti
For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use --n-gpu-layers 32 to offload some layers to the CPU, enabling flash attention (--flash-attn) for faster inference. Tensor parallelism is not necessary due to the single GPU setup, but it can be used if you plan to scale out to multiple GPUs.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers with --n-gpu-layers 16 or lower, or increase the batch size if using batched inference.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn and that the CUDA backend is properly configured.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a GUI interface, while llama.cpp offers more control over low-level optimizations. Jan is a good choice for those looking for a lightweight, easy-to-use runtime. However, Ollama provides a balanced combination of ease of use and performance, making it the recommended choice for this GPU.
Other models that run great on RTX 3080 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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