Can RTX 3090 Ti run Gemma 3 1B?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 Ti (24 GB VRAM) handles Gemma 3 1B comfortably using the Q8_0 quantization, which fits in 1.5 GB. Expected throughput is around 132 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Google's latest tiny 1B model. Excellent quality for its size.
Setup tutorial: Gemma 3 1B on RTX 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 1B on an NVIDIA GeForce RTX 3090 Ti with Ollama using the Q8_0 quantization. Expect Grade S performance at ~955 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) with CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you should expect a token generation rate of approximately 955 tok/sec, consuming around 1.5GB of VRAM. This leaves you with 22.5GB of VRAM 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 runtime cuda2. Download the model
Download the Q8_0 quantized version of Gemma 3 1B from Hugging Face, which is a 1.0GB file.
ollama pull bartowski/google_gemma-3-1b-it-GGUF:google_gemma-3-1b-it-Q8_0.gguf3. Run it
ollama run google_gemma-3-1b-it-Q8_0 --context-length 32768
ollama chat google_gemma-3-1b-it-Q8_04. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to utilize the full 24GB VRAM effectively. Enable flash attention with --flash-attn to speed up inference. Given the 24GB VRAM, you can maintain a large context window while achieving high token throughput.
Troubleshooting
Out of memory errors during inference
Reduce the --n-gpu-layers value to 24 or lower to free up more VRAM.
Slow token generation
Ensure that flash attention is enabled with --flash-attn. If not, add the flag to your run command.
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 like LM Studio, llama.cpp, and Jan can also run Gemma 3 1B. LM Studio is ideal for a user-friendly GUI, llama.cpp offers more control over quantization and optimization, and Jan is suitable for distributed training and inference. Choose based on your specific needs and preferences.
Other models that run great on RTX 3090 Ti
FAQ (20)
What GPU do I need to run Gemma 3 1B?
To run Gemma 3 1B, you need a GPU with at least 1.3 GB to 1.5 GB of VRAM, depending on the quantization level.
Is Gemma 3 1B good for coding?
Gemma 3 1B is suitable for coding tasks due to its efficient size and high-quality outputs, making it a good choice for developers.
Gemma 3 1B vs Llama 3.1 8B?
Gemma 3 1B is smaller and requires less VRAM (1.3 GB to 1.5 GB) compared to Llama 3.1 8B (which needs more VRAM), but Llama 3.1 8B generally offers better performance for larger tasks.
Can I run Gemma 3 1B on a Mac?
Yes, you can run Gemma 3 1B on a Mac, provided your Mac has a compatible GPU with at least 1.3 GB to 1.5 GB of VRAM.
How much VRAM does Gemma 3 1B need?
Gemma 3 1B requires 1.3 GB to 1.5 GB of VRAM, depending on the quantization level used.
Is Gemma 3 1B censored?
Gemma 3 1B is not inherently censored, but its responses are guided by the training data and can be filtered or moderated as needed.
Is Gemma 3 1B commercial-use allowed?
Gemma 3 1B is licensed under the 'gemma' license, which allows for commercial use, provided you comply with the terms of the license.
Gemma 3 1B context length?
Gemma 3 1B supports a context length of 32,768 tokens, allowing for longer and more complex inputs.
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